[fast_inference] 兼容torch.jit.script (#1489)

* 放弃了在t2s模型中使用@torch.jit.script，确保pytorch环境之间的兼容性

* 优化@torch.jit.script策略

.dockerignore

@@ -1,198 +1,8 @@
-GPT_SoVITS/pretrained_models/*
-tools/asr/models/*
-tools/uvr5/uvr5_weights/*
-
-.git
-.DS_Store
-.vscode
-*.pyc
-env
-runtime
-.idea
-output
+docs
 logs
-SoVITS_weights*/
-GPT_weights*/
+output
+reference
+SoVITS_weights
+GPT_weights
 TEMP
-weight.json
-ffmpeg*
-ffprobe*
-cfg.json
-speakers.json
-ref_audios
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-**/__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-.python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-Pipfile.lock
-
-# UV
-#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-uv.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
-.pdm.toml
-.pdm-python
-.pdm-build/
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
-
-# Ruff stuff:
-.ruff_cache/
-
-# PyPI configuration file
-.pypirc
+.git

.github/build_windows_packages.ps1

@@ -1,194 +0,0 @@
-$ErrorActionPreference = "Stop"
-
-Write-Host "Current location: $(Get-Location)"
-
-$cuda = $env:TORCH_CUDA
-if (-not $cuda) {
-    Write-Error "Missing TORCH_CUDA env (cu124 or cu128)"
-    exit 1
-}
-
-$date = $env:DATE_SUFFIX
-if ([string]::IsNullOrWhiteSpace($date)) {
-    $date = Get-Date -Format "MMdd"
-}
-
-$pkgName = "GPT-SoVITS-$date"
-$tmpDir = "tmp"
-$srcDir = $PWD
-
-$suffix = $env:PKG_SUFFIX
-if (-not [string]::IsNullOrWhiteSpace($suffix)) {
-    $pkgName = "$pkgName$suffix"
-}
-
-$pkgName = "$pkgName-$cuda"
-
-$baseHF = "https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main"
-$PRETRAINED_URL = "$baseHF/pretrained_models.zip"
-$G2PW_URL = "$baseHF/G2PWModel.zip"
-$UVR5_URL = "$baseHF/uvr5_weights.zip"
-$NLTK_URL = "$baseHF/nltk_data.zip"
-$JTALK_URL = "$baseHF/open_jtalk_dic_utf_8-1.11.tar.gz"
-
-$PYTHON_VERSION = "3.11.12"
-$PY_RELEASE_VERSION = "20250409"
-
-Write-Host "[INFO] Cleaning .git..."
-Remove-Item "$srcDir\.git" -Recurse -Force -ErrorAction SilentlyContinue
-
-Write-Host "[INFO] Creating tmp dir..."
-New-Item -ItemType Directory -Force -Path $tmpDir
-
-Write-Host "[INFO] System Python version:"
-python --version
-python -m site
-
-Write-Host "[INFO] Downloading Python $PYTHON_VERSION..."
-$zst = "$tmpDir\python.tar.zst"
-Invoke-WebRequest "https://github.com/astral-sh/python-build-standalone/releases/download/$PY_RELEASE_VERSION/cpython-$PYTHON_VERSION+$PY_RELEASE_VERSION-x86_64-pc-windows-msvc-pgo-full.tar.zst" -OutFile $zst
-& "C:\Program Files\7-Zip\7z.exe" e $zst -o"$tmpDir" -aoa
-$tar = Get-ChildItem "$tmpDir" -Filter "*.tar" | Select-Object -First 1
-& "C:\Program Files\7-Zip\7z.exe" x $tar.FullName -o"$tmpDir\extracted" -aoa
-Move-Item "$tmpDir\extracted\python\install" "$srcDir\runtime"
-
-Write-Host "[INFO] Copying Redistributing Visual C++ Runtime..."
-$vswhere = "${env:ProgramFiles(x86)}\Microsoft Visual Studio\Installer\vswhere.exe"
-$vsPath = & $vswhere -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath
-$redistRoot = Join-Path $vsPath "VC\Redist\MSVC"
-$targetVer = Get-ChildItem -Path $redistRoot -Directory |
-    Where-Object { $_.Name -match "^14\." } |
-    Sort-Object Name -Descending |
-    Select-Object -First 1
-$x64Path = Join-Path $targetVer.FullName "x64"
-Get-ChildItem -Path $x64Path -Directory | Where-Object {
-    $_.Name -match '^Microsoft\..*\.(CRT|OpenMP)$'
-} | ForEach-Object {
-    Get-ChildItem -Path $_.FullName -Filter "*.dll" | ForEach-Object {
-        Copy-Item -Path $_.FullName -Destination "$srcDir\runtime" -Force
-    }
-}
-
-function DownloadAndUnzip($url, $targetRelPath) {
-    $filename = Split-Path $url -Leaf
-    $tmpZip = "$tmpDir\$filename"
-    Invoke-WebRequest $url -OutFile $tmpZip
-    Expand-Archive -Path $tmpZip -DestinationPath $tmpDir -Force
-    $subdirName = $filename -replace '\.zip$', ''
-    $sourcePath = Join-Path $tmpDir $subdirName
-    $destRoot = Join-Path $srcDir $targetRelPath
-    $destPath = Join-Path $destRoot $subdirName
-    if (Test-Path $destPath) {
-        Remove-Item $destPath -Recurse -Force
-    }
-    Move-Item $sourcePath $destRoot
-    Remove-Item $tmpZip
-}
-
-Write-Host "[INFO] Download pretrained_models..."
-DownloadAndUnzip $PRETRAINED_URL "GPT_SoVITS"
-
-Write-Host "[INFO] Download G2PWModel..."
-DownloadAndUnzip $G2PW_URL "GPT_SoVITS\text"
-
-Write-Host "[INFO] Download UVR5 model..."
-DownloadAndUnzip $UVR5_URL "tools\uvr5"
-
-Write-Host "[INFO] Downloading funasr..."
-$funasrUrl = "https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/funasr.zip"
-$funasrZip = "$tmpDir\funasr.zip"
-Invoke-WebRequest -Uri $funasrUrl -OutFile $funasrZip
-Expand-Archive -Path $funasrZip -DestinationPath "$srcDir\tools\asr\models" -Force
-Remove-Item $funasrZip
-
-Write-Host "[INFO] Download ffmpeg..."
-$ffUrl = "https://www.gyan.dev/ffmpeg/builds/ffmpeg-release-essentials.zip"
-$ffZip = "$tmpDir\ffmpeg.zip"
-Invoke-WebRequest -Uri $ffUrl -OutFile $ffZip
-Expand-Archive $ffZip -DestinationPath $tmpDir -Force
-$ffDir = Get-ChildItem -Directory "$tmpDir" | Where-Object { $_.Name -like "ffmpeg*" } | Select-Object -First 1
-Move-Item "$($ffDir.FullName)\bin\ffmpeg.exe" "$srcDir\runtime"
-Move-Item "$($ffDir.FullName)\bin\ffprobe.exe" "$srcDir\runtime"
-Remove-Item $ffZip
-Remove-Item $ffDir.FullName -Recurse -Force
-
-Write-Host "[INFO] Installing PyTorch..."
-& ".\runtime\python.exe" -m ensurepip
-& ".\runtime\python.exe" -m pip install --upgrade pip --no-warn-script-location
-switch ($cuda) {
-    "cu124" {
-        & ".\runtime\python.exe" -m pip install torch==2.6 torchaudio --index-url https://download.pytorch.org/whl/cu124 --no-warn-script-location
-    }
-    "cu128" {
-        & ".\runtime\python.exe" -m pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu128 --no-warn-script-location
-    }
-    default {
-        Write-Error "Unsupported CUDA version: $cuda"
-        exit 1
-    }
-}
-
-Write-Host "[INFO] Installing dependencies..."
-& ".\runtime\python.exe" -m pip install -r extra-req.txt --no-deps --no-warn-script-location
-& ".\runtime\python.exe" -m pip install -r requirements.txt --no-warn-script-location
-
-Write-Host "[INFO] Downloading NLTK and pyopenjtalk dictionary..."
-$PYTHON = ".\runtime\python.exe"
-$prefix = & $PYTHON -c "import sys; print(sys.prefix)"
-$jtalkPath = & $PYTHON -c "import os, pyopenjtalk; print(os.path.dirname(pyopenjtalk.__file__))"
-$nltkZip = "$tmpDir\nltk_data.zip"
-$jtalkTar = "$tmpDir\open_jtalk_dic_utf_8-1.11.tar.gz"
-
-Invoke-WebRequest -Uri $NLTK_URL -OutFile $nltkZip
-Expand-Archive -Path $nltkZip -DestinationPath $prefix -Force
-Remove-Item $nltkZip
-
-Invoke-WebRequest -Uri $JTALK_URL -OutFile $jtalkTar
-& "C:\Program Files\7-Zip\7z.exe" e $jtalkTar -o"$tmpDir" -aoa
-$innerTar = Get-ChildItem "$tmpDir" -Filter "*.tar" | Select-Object -First 1
-& "C:\Program Files\7-Zip\7z.exe" x $innerTar.FullName -o"$jtalkPath" -aoa
-Remove-Item $jtalkTar
-Remove-Item $innerTar.FullName
-
-Write-Host "[INFO] Preparing final directory $pkgName ..."
-$items = @(Get-ChildItem -Filter "*.sh") +
-         @(Get-ChildItem -Filter "*.ipynb") +
-         @("$tmpDir", ".github", "Docker", "docs", ".gitignore", ".dockerignore", "README.md")
-Remove-Item $items -Force -Recurse -ErrorAction SilentlyContinue
-$curr = Get-Location
-Set-Location ../
-Get-ChildItem .
-Copy-Item -Path $curr -Destination $pkgName -Recurse
-$7zPath = "$pkgName.7z"
-$start = Get-Date
-Write-Host "Compress Starting at $start"
-& "C:\Program Files\7-Zip\7z.exe" a -t7z "$7zPath" "$pkgName" -m0=lzma2 -mx=9 -md=1g -ms=1g -mmc=500 -mfb=273 -mlc=0 -mlp=4 -mpb=4 -mc=8g -mmt=on -bsp1
-$end = Get-Date
-Write-Host "Elapsed time: $($end - $start)"
-Get-ChildItem .
-
-python -m pip install --upgrade pip
-python -m pip install "modelscope" "huggingface_hub[hf_transfer]" --no-warn-script-location
-
-Write-Host "[INFO] Uploading to ModelScope..."
-$msUser = $env:MODELSCOPE_USERNAME
-$msToken = $env:MODELSCOPE_TOKEN
-if (-not $msUser -or -not $msToken) {
-    Write-Error "Missing MODELSCOPE_USERNAME or MODELSCOPE_TOKEN"
-    exit 1
-}
-modelscope upload "$msUser/GPT-SoVITS-Packages" "$7zPath" "$7zPath" --repo-type model --token $msToken
-
-Write-Host "[SUCCESS] Uploaded: $7zPath to ModelScope"
-
-Write-Host "[INFO] Uploading to HuggingFace..."
-$hfUser = $env:HUGGINGFACE_USERNAME
-$hfToken = $env:HUGGINGFACE_TOKEN
-if (-not $hfUser -or -not $hfToken) {
-    Write-Error "Missing HUGGINGFACE_USERNAME or HUGGINGFACE_TOKEN"
-    exit 1
-}
-$env:HF_HUB_ENABLE_HF_TRANSFER = "1"
-huggingface-cli upload "$hfUser/GPT-SoVITS-Packages" "$7zPath" "$7zPath" --repo-type model --token $hfToken
-
-Write-Host "[SUCCESS] Uploaded: $7zPath to HuggingFace"

.github/workflows/build_windows_packages.yaml

@@ -1,38 +0,0 @@
-name: Build and Upload Windows Package
-
-on:
-  workflow_dispatch:
-    inputs:
-      date:
-        description: "Date suffix (optional)"
-        required: false
-        default: ""
-      suffix:
-        description: "Package name suffix (optional)"
-        required: false
-        default: ""
-
-jobs:
-  build:
-    runs-on: windows-latest
-    strategy:
-      matrix:
-        torch_cuda: [cu124, cu128]
-    env:
-      TORCH_CUDA: ${{ matrix.torch_cuda }}
-      MODELSCOPE_USERNAME: ${{ secrets.MODELSCOPE_USERNAME }}
-      MODELSCOPE_TOKEN: ${{ secrets.MODELSCOPE_TOKEN }}
-      HUGGINGFACE_USERNAME: ${{ secrets.HUGGINGFACE_USERNAME }}
-      HUGGINGFACE_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
-      DATE_SUFFIX: ${{ github.event.inputs.date }}
-      PKG_SUFFIX: ${{ github.event.inputs.suffix }}
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Run Build and Upload Script
-        shell: pwsh
-        run: |
-          Move-Item .github/build_windows_packages.ps1 ../build_windows_packages.ps1
-          ../build_windows_packages.ps1

.github/workflows/docker-publish.yaml

@@ -1,276 +0,0 @@
-name: Build and Publish Docker Image
-
-on:
-  workflow_dispatch:
-
-jobs:
-  generate-meta:
-    runs-on: ubuntu-22.04
-    outputs:
-      tag: ${{ steps.meta.outputs.tag }}
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-
-      - name: Generate Tag
-        id: meta
-        run: |
-          DATE=$(date +'%Y%m%d')
-          COMMIT=$(git rev-parse --short=6 HEAD)
-          echo "tag=${DATE}-${COMMIT}" >> $GITHUB_OUTPUT
-  build-amd64:
-    needs: generate-meta
-    runs-on: ubuntu-22.04
-    strategy:
-      matrix:
-        include:
-          - cuda_version: 12.6
-            lite: true
-            torch_base: lite
-            tag_prefix: cu126-lite
-          - cuda_version: 12.6
-            lite: false
-            torch_base: full
-            tag_prefix: cu126
-          - cuda_version: 12.8
-            lite: true
-            torch_base: lite
-            tag_prefix: cu128-lite
-          - cuda_version: 12.8
-            lite: false
-            torch_base: full
-            tag_prefix: cu128
-
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-
-      - name: Free up disk space
-        run: |
-          echo "Before cleanup:"
-          df -h  
-
-          sudo rm -rf /opt/ghc
-          sudo rm -rf /opt/hostedtoolcache/CodeQL
-          sudo rm -rf /opt/hostedtoolcache/PyPy
-          sudo rm -rf /opt/hostedtoolcache/go
-          sudo rm -rf /opt/hostedtoolcache/node
-          sudo rm -rf /opt/hostedtoolcache/Ruby
-          sudo rm -rf /opt/microsoft
-          sudo rm -rf /opt/pipx
-          sudo rm -rf /opt/az
-          sudo rm -rf /opt/google
-
-          
-          sudo rm -rf /usr/lib/jvm
-          sudo rm -rf /usr/lib/google-cloud-sdk
-          sudo rm -rf /usr/lib/dotnet
-
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /usr/local/.ghcup
-          sudo rm -rf /usr/local/julia1.11.5
-          sudo rm -rf /usr/local/share/powershell
-          sudo rm -rf /usr/local/share/chromium
-
-          sudo rm -rf /usr/share/swift
-          sudo rm -rf /usr/share/miniconda
-          sudo rm -rf /usr/share/az_12.1.0
-          sudo rm -rf /usr/share/dotnet
-
-          echo "After cleanup:"
-          df -h
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_HUB_USERNAME }}
-          password: ${{ secrets.DOCKER_HUB_PASSWORD }}
-
-      - name: Build and Push Docker Image (amd64)
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: ./Dockerfile
-          push: true
-          platforms: linux/amd64
-          build-args: |
-            LITE=${{ matrix.lite }}
-            TORCH_BASE=${{ matrix.torch_base }}
-            CUDA_VERSION=${{ matrix.cuda_version }}
-            WORKFLOW=true
-          tags: |
-            xxxxrt666/gpt-sovits:${{ matrix.tag_prefix }}-${{ needs.generate-meta.outputs.tag }}-amd64
-            xxxxrt666/gpt-sovits:latest-${{ matrix.tag_prefix }}-amd64
-
-  build-arm64:
-    needs: generate-meta
-    runs-on: ubuntu-22.04-arm
-    strategy:
-      matrix:
-        include:
-          - cuda_version: 12.6
-            lite: true
-            torch_base: lite
-            tag_prefix: cu126-lite
-          - cuda_version: 12.6
-            lite: false
-            torch_base: full
-            tag_prefix: cu126
-          - cuda_version: 12.8
-            lite: true
-            torch_base: lite
-            tag_prefix: cu128-lite
-          - cuda_version: 12.8
-            lite: false
-            torch_base: full
-            tag_prefix: cu128
-
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-
-      - name: Free up disk space
-        run: |
-          echo "Before cleanup:"
-          df -h  
-
-          sudo rm -rf /opt/ghc
-          sudo rm -rf /opt/hostedtoolcache/CodeQL
-          sudo rm -rf /opt/hostedtoolcache/PyPy
-          sudo rm -rf /opt/hostedtoolcache/go
-          sudo rm -rf /opt/hostedtoolcache/node
-          sudo rm -rf /opt/hostedtoolcache/Ruby
-          sudo rm -rf /opt/microsoft
-          sudo rm -rf /opt/pipx
-          sudo rm -rf /opt/az
-          sudo rm -rf /opt/google
-
-          
-          sudo rm -rf /usr/lib/jvm
-          sudo rm -rf /usr/lib/google-cloud-sdk
-          sudo rm -rf /usr/lib/dotnet
-
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /usr/local/.ghcup
-          sudo rm -rf /usr/local/julia1.11.5
-          sudo rm -rf /usr/local/share/powershell
-          sudo rm -rf /usr/local/share/chromium
-
-          sudo rm -rf /usr/share/swift
-          sudo rm -rf /usr/share/miniconda
-          sudo rm -rf /usr/share/az_12.1.0
-          sudo rm -rf /usr/share/dotnet
-
-          echo "After cleanup:"
-          df -h
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_HUB_USERNAME }}
-          password: ${{ secrets.DOCKER_HUB_PASSWORD }}
-
-      - name: Build and Push Docker Image (arm64)
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: ./Dockerfile
-          push: true
-          platforms: linux/arm64
-          build-args: |
-            LITE=${{ matrix.lite }}
-            TORCH_BASE=${{ matrix.torch_base }}
-            CUDA_VERSION=${{ matrix.cuda_version }}
-            WORKFLOW=true
-          tags: |
-            xxxxrt666/gpt-sovits:${{ matrix.tag_prefix }}-${{ needs.generate-meta.outputs.tag }}-arm64
-            xxxxrt666/gpt-sovits:latest-${{ matrix.tag_prefix }}-arm64
-
-
-  merge-and-clean:
-    needs:
-      - build-amd64
-      - build-arm64
-      - generate-meta
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        include:
-          - tag_prefix: cu126-lite
-          - tag_prefix: cu126
-          - tag_prefix: cu128-lite
-          - tag_prefix: cu128
-
-    steps:
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_HUB_USERNAME }}
-          password: ${{ secrets.DOCKER_HUB_PASSWORD }}
-
-      - name: Merge amd64 and arm64 into multi-arch image
-        run: |
-          DATE_TAG=${{ needs.generate-meta.outputs.tag }}
-          TAG_PREFIX=${{ matrix.tag_prefix }}
-
-          docker buildx imagetools create \
-            --tag ${{ secrets.DOCKER_HUB_USERNAME }}/gpt-sovits:${TAG_PREFIX}-${DATE_TAG} \
-            ${{ secrets.DOCKER_HUB_USERNAME }}/gpt-sovits:${TAG_PREFIX}-${DATE_TAG}-amd64 \
-            ${{ secrets.DOCKER_HUB_USERNAME }}/gpt-sovits:${TAG_PREFIX}-${DATE_TAG}-arm64
-
-          docker buildx imagetools create \
-            --tag ${{ secrets.DOCKER_HUB_USERNAME }}/gpt-sovits:latest-${TAG_PREFIX} \
-            ${{ secrets.DOCKER_HUB_USERNAME }}/gpt-sovits:latest-${TAG_PREFIX}-amd64 \
-            ${{ secrets.DOCKER_HUB_USERNAME }}/gpt-sovits:latest-${TAG_PREFIX}-arm64
-      - name: Delete old platform-specific tags via Docker Hub API
-        env:
-          DOCKER_HUB_USERNAME: ${{ secrets.DOCKER_HUB_USERNAME }}
-          DOCKER_HUB_TOKEN: ${{ secrets.DOCKER_HUB_PASSWORD }}
-          TAG_PREFIX: ${{ matrix.tag_prefix }}
-          DATE_TAG: ${{ needs.generate-meta.outputs.tag }}
-        run: |
-          sudo apt-get update && sudo apt-get install -y jq
-
-          TOKEN=$(curl -s -u $DOCKER_HUB_USERNAME:$DOCKER_HUB_TOKEN \
-          "https://auth.docker.io/token?service=registry.docker.io&scope=repository:$DOCKER_HUB_USERNAME/gpt-sovits:pull,push,delete" \
-          | jq -r .token)
-
-          for PLATFORM in amd64 arm64; do
-            SAFE_PLATFORM=$(echo $PLATFORM | sed 's/\//-/g')
-            TAG="${TAG_PREFIX}-${DATE_TAG}-${SAFE_PLATFORM}"
-            LATEST_TAG="latest-${TAG_PREFIX}-${SAFE_PLATFORM}"
-
-            for DEL_TAG in "$TAG" "$LATEST_TAG"; do
-              echo "Deleting tag: $DEL_TAG"
-              curl -X DELETE -H "Authorization: Bearer $TOKEN" https://registry-1.docker.io/v2/$DOCKER_HUB_USERNAME/gpt-sovits/manifests/$DEL_TAG
-            done
-          done
-  create-default:
-    runs-on: ubuntu-latest
-    needs:
-      - merge-and-clean
-    steps:
-    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
-
-    - name: Log in to Docker Hub
-      uses: docker/login-action@v3
-      with:
-        username: ${{ secrets.DOCKER_HUB_USERNAME }}
-        password: ${{ secrets.DOCKER_HUB_PASSWORD }}
-
-    - name: Create Default Tag
-      run: |
-        docker buildx imagetools create \
-          --tag ${{ secrets.DOCKER_HUB_USERNAME }}/gpt-sovits:latest \
-          ${{ secrets.DOCKER_HUB_USERNAME }}/gpt-sovits:latest-cu126-lite
-        

.gitignore

@@ -1,5 +1,4 @@
 .DS_Store
-.vscode
 __pycache__
 *.pyc
 env
@@ -7,189 +6,9 @@ runtime
 .idea
 output
 logs
-SoVITS_weights*/
-GPT_weights*/
+reference
+GPT_weights
+SoVITS_weights
 TEMP
-weight.json
 ffmpeg*
-ffprobe*
-cfg.json
-speakers.json
-ref_audios
-tools/AP_BWE_main/24kto48k/*
-!tools/AP_BWE_main/24kto48k/readme.txt
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# UV
-#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#uv.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
-.pdm.toml
-.pdm-python
-.pdm-build/
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
-
-# Ruff stuff:
-.ruff_cache/
-
-# PyPI configuration file
-.pypirc
+ffprobe*

.pre-commit-config.yaml

@@ -1,15 +0,0 @@
-ci:
-  autoupdate_schedule: monthly
-
-repos:
-- repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.11.7
-  hooks:
-    # Run the linter.
-    - id: ruff
-      types_or: [ python, pyi ]
-      args: [ --fix , "--exit-zero" ]
-    # Run the formatter.
-    - id: ruff-format
-      types_or: [ python, pyi ]
-      args: [ --line-length, "120", --target-version, "py310" ]

Colab-Inference.ipynb

@@ -1,191 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-Inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# GPT-SoVITS Infer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Env Setup (Run Once Only)\n",
-        "## 环境配置, 只需运行一次"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 1."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "e9b7iFV3dm1f"
-      },
-      "outputs": [],
-      "source": [
-        "%%writefile /content/setup.sh\n",
-        "set -e\n",
-        "\n",
-        "cd /content\n",
-        "\n",
-        "git clone https://github.com/RVC-Boss/GPT-SoVITS.git\n",
-        "\n",
-        "cd GPT-SoVITS\n",
-        "\n",
-        "mkdir -p GPT_weights\n",
-        "\n",
-        "mkdir -p SoVITS_weights\n",
-        "\n",
-        "if conda env list | awk '{print $1}' | grep -Fxq \"GPTSoVITS\"; then\n",
-        "    :\n",
-        "else\n",
-        "    conda create -n GPTSoVITS python=3.10 -y\n",
-        "fi\n",
-        "\n",
-        "source activate GPTSoVITS\n",
-        "\n",
-        "pip install ipykernel\n",
-        "\n",
-        "bash install.sh --device CU126 --source HF"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "0NgxXg5sjv7z"
-      },
-      "outputs": [],
-      "source": [
-        "%pip install -q condacolab\n",
-        "import condacolab\n",
-        "condacolab.install_from_url(\"https://repo.anaconda.com/archive/Anaconda3-2024.10-1-Linux-x86_64.sh\")\n",
-        "!cd /content && bash setup.sh"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Download Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Download From HuggingFace"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "vbZY-LnM0tzq"
-      },
-      "outputs": [],
-      "source": [
-        "# Modify These\n",
-        "USER_ID = \"AkitoP\"\n",
-        "REPO_NAME = \"GPT-SoVITS-v2-aegi\"\n",
-        "BRANCH = \"main\"\n",
-        "GPT_PATH = \"new_aegigoe-e100.ckpt\"\n",
-        "SOVITS_PATH = \"new_aegigoe_e60_s32220.pth\"\n",
-        "\n",
-        "# Do Not Modify\n",
-        "HF_BASE = \"https://huggingface.co\"\n",
-        "REPO_ID = f\"{USER_ID}/{REPO_NAME}\"\n",
-        "GPT_URL = f\"{HF_BASE}/{REPO_ID}/blob/{BRANCH}/{GPT_PATH}\"\n",
-        "SOVITS_URL = f\"{HF_BASE}/{REPO_ID}/blob/{BRANCH}/{SOVITS_PATH}\"\n",
-        "\n",
-        "!cd \"/content/GPT-SoVITS/GPT_weights\" && wget \"{GPT_URL}\"\n",
-        "!cd \"/content/GPT-SoVITS/SoVITS_weights\" && wget \"{SOVITS_URL}\"\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Download From ModelScope"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Modify These\n",
-        "USER_ID = \"aihobbyist\"\n",
-        "REPO_NAME = \"GPT-SoVits-V2-models\"\n",
-        "BRANCH = \"master\"\n",
-        "GPT_PATH = \"Genshin_Impact/EN/GPT_GenshinImpact_EN_5.1.ckpt\"\n",
-        "SOVITS_PATH = \"Wuthering_Waves/CN/SV_WutheringWaves_CN_1.3.pth\"\n",
-        "\n",
-        "# Do Not Modify\n",
-        "HF_BASE = \"https://www.modelscope.cn/models\"\n",
-        "REPO_ID = f\"{USER_ID}/{REPO_NAME}\"\n",
-        "GPT_URL = f\"{HF_BASE}/{REPO_ID}/resolve/{BRANCH}/{GPT_PATH}\"\n",
-        "SOVITS_URL = f\"{HF_BASE}/{REPO_ID}/resolve/{BRANCH}/{SOVITS_PATH}\"\n",
-        "\n",
-        "!cd \"/content/GPT-SoVITS/GPT_weights\" && wget \"{GPT_URL}\"\n",
-        "!cd \"/content/GPT-SoVITS/SoVITS_weights\" && wget \"{SOVITS_URL}\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Launch WebUI\n",
-        "# 启动 WebUI"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "4oRGUzkrk8C7"
-      },
-      "outputs": [],
-      "source": [
-        "!cd /content/GPT-SoVITS && source activate GPTSoVITS && export is_share=True && python webui.py"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

Colab-WebUI.ipynb

@@ -1,117 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# GPT-SoVITS WebUI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_o6a8GS2lWQM"
-      },
-      "source": [
-        "## Env Setup (Run Once Only)\n",
-        "## 环境配置, 只需运行一次"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 1."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile /content/setup.sh\n",
-        "set -e\n",
-        "\n",
-        "cd /content\n",
-        "\n",
-        "git clone https://github.com/RVC-Boss/GPT-SoVITS.git\n",
-        "\n",
-        "cd GPT-SoVITS\n",
-        "\n",
-        "if conda env list | awk '{print $1}' | grep -Fxq \"GPTSoVITS\"; then\n",
-        "    :\n",
-        "else\n",
-        "    conda create -n GPTSoVITS python=3.10 -y\n",
-        "fi\n",
-        "\n",
-        "source activate GPTSoVITS\n",
-        "\n",
-        "pip install ipykernel\n",
-        "\n",
-        "bash install.sh --device CU126 --source HF --download-uvr5"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -q condacolab\n",
-        "import condacolab\n",
-        "condacolab.install_from_url(\"https://repo.anaconda.com/archive/Anaconda3-2024.10-1-Linux-x86_64.sh\")\n",
-        "!cd /content && bash setup.sh"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Launch WebUI\n",
-        "## 启动 WebUI"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "4oRGUzkrk8C7"
-      },
-      "outputs": [],
-      "source": [
-        "!cd /content/GPT-SoVITS && source activate GPTSoVITS && export is_share=True && python webui.py"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "include_colab_link": true,
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

Docker/damo.sha256

@@ -0,0 +1,3 @@
+5bba782a5e9196166233b9ab12ba04cadff9ef9212b4ff6153ed9290ff679025 /workspace/tools/damo_asr/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/model.pb
+b3be75be477f0780277f3bae0fe489f48718f585f3a6e45d7dd1fbb1a4255fc5 /workspace/tools/damo_asr/models/speech_fsmn_vad_zh-cn-16k-common-pytorch/model.pb
+a5818bb9d933805a916eebe41eb41648f7f9caad30b4bd59d56f3ca135421916 /workspace/tools/damo_asr/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/model.pb

Docker/download.py

@@ -0,0 +1,5 @@
+# Download moda ASR related models
+from modelscope import snapshot_download
+model_dir = snapshot_download('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',revision="v2.0.4")
+model_dir = snapshot_download('damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',revision="v2.0.4")
+model_dir = snapshot_download('damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',revision="v2.0.4")

Docker/download.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+
+set -Eeuo pipefail
+
+echo "Downloading models..."
+
+aria2c --disable-ipv6 --input-file /workspace/Docker/links.txt --dir /workspace --continue
+
+echo "Checking SHA256..."
+
+parallel --will-cite -a /workspace/Docker/links.sha256 "echo -n {} | sha256sum -c"

Docker/install_wrapper.sh

@@ -1,33 +0,0 @@
-#!/bin/bash
-
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" &>/dev/null && pwd)"
-
-cd "$SCRIPT_DIR" || exit 1
-
-cd .. || exit 1
-
-set -e
-
-source "$HOME/miniconda3/etc/profile.d/conda.sh"
-
-mkdir -p GPT_SoVITS
-
-mkdir -p GPT_SoVITS/text
-
-ln -s /workspace/models/pretrained_models /workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models
-
-ln -s /workspace/models/G2PWModel /workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel
-
-bash install.sh --device "CU${CUDA_VERSION//./}" --source HF
-
-pip cache purge
-
-pip show torch
-
-rm -rf /tmp/* /var/tmp/*
-
-rm -rf "$HOME/miniconda3/pkgs"
-
-mkdir -p "$HOME/miniconda3/pkgs"
-
-rm -rf /root/.conda /root/.cache

Docker/links.sha256

@@ -0,0 +1,12 @@
+b1c1e17e9c99547a89388f72048cd6e1b41b5a18b170e86a46dfde0324d63eb1 /workspace/GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
+fc579c1db3c1e21b721001cf99d7a584214280df19b002e200b630a34fa06eb8 /workspace/GPT_SoVITS/pretrained_models/s2D488k.pth
+020a014e1e01e550e510f2f61fae5e5f5b6aab40f15c22f1f12f724df507e835 /workspace/GPT_SoVITS/pretrained_models/s2G488k.pth
+24164f129c66499d1346e2aa55f183250c223161ec2770c0da3d3b08cf432d3c /workspace/GPT_SoVITS/pretrained_models/chinese-hubert-base/pytorch_model.bin
+e53a693acc59ace251d143d068096ae0d7b79e4b1b503fa84c9dcf576448c1d8 /workspace/GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large/pytorch_model.bin
+39796caa5db18d7f9382d8ac997ac967bfd85f7761014bb807d2543cc844ef05 /workspace/tools/uvr5/uvr5_weights/HP2_all_vocals.pth
+45e6b65199e781b4a6542002699be9f19cd3d1cb7d1558bc2bfbcd84674dfe28 /workspace/tools/uvr5/uvr5_weights/HP3_all_vocals.pth
+5908891829634926119720241e8573d97cbeb8277110a7512bdb0bd7563258ee /workspace/tools/uvr5/uvr5_weights/HP5_only_main_vocal.pth
+8c8fd1582f9aabc363e47af62ddb88df6cae7e064cae75bbf041a067a5e0aee2 /workspace/tools/uvr5/uvr5_weights/VR-DeEchoAggressive.pth
+01376dd2a571bf3cb9cced680732726d2d732609d09216a610b0d110f133febe /workspace/tools/uvr5/uvr5_weights/VR-DeEchoDeReverb.pth
+56aba59db3bcdd14a14464e62f3129698ecdea62eee0f003b9360923eb3ac79e /workspace/tools/uvr5/uvr5_weights/VR-DeEchoNormal.pth
+233bb5c6aaa365e568659a0a81211746fa881f8f47f82d9e864fce1f7692db80 /workspace/tools/uvr5/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx

Docker/links.txt

@@ -0,0 +1,34 @@
+# GPT-SoVITS models
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/s1bert25hz-2kh-longer-epoch%3D68e-step%3D50232.ckpt
+  out=GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/s2D488k.pth
+  out=GPT_SoVITS/pretrained_models/s2D488k.pth
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/s2G488k.pth
+  out=GPT_SoVITS/pretrained_models/s2G488k.pth
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-hubert-base/config.json
+  out=GPT_SoVITS/pretrained_models/chinese-hubert-base/config.json
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-hubert-base/preprocessor_config.json
+  out=GPT_SoVITS/pretrained_models/chinese-hubert-base/preprocessor_config.json
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-hubert-base/pytorch_model.bin
+  out=GPT_SoVITS/pretrained_models/chinese-hubert-base/pytorch_model.bin
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-roberta-wwm-ext-large/config.json
+  out=GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large/config.json
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-roberta-wwm-ext-large/pytorch_model.bin
+  out=GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large/pytorch_model.bin
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-roberta-wwm-ext-large/tokenizer.json
+  out=GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large/tokenizer.json
+# UVR5
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth
+  out=tools/uvr5/uvr5_weights/HP2_all_vocals.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth
+  out=tools/uvr5/uvr5_weights/HP3_all_vocals.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth
+  out=tools/uvr5/uvr5_weights/HP5_only_main_vocal.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth
+  out=tools/uvr5/uvr5_weights/VR-DeEchoAggressive.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth
+  out=tools/uvr5/uvr5_weights/VR-DeEchoDeReverb.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth
+  out=tools/uvr5/uvr5_weights/VR-DeEchoNormal.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx
+  out=tools/uvr5/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx

Docker/miniconda_install.sh

@@ -1,70 +0,0 @@
-#!/bin/bash
-
-set -e
-
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" &>/dev/null && pwd)"
-
-cd "$SCRIPT_DIR" || exit 1
-
-cd .. || exit 1
-
-if [ -d "$HOME/miniconda3" ]; then
-    exit 0
-fi
-
-WORKFLOW=${WORKFLOW:-"false"}
-TARGETPLATFORM=${TARGETPLATFORM:-"linux/amd64"}
-
-if [ "$WORKFLOW" = "true" ]; then
-    WGET_CMD=(wget -nv --tries=25 --wait=5 --read-timeout=40 --retry-on-http-error=404)
-else
-    WGET_CMD=(wget --tries=25 --wait=5 --read-timeout=40 --retry-on-http-error=404)
-fi
-
-if [ "$TARGETPLATFORM" = "linux/amd64" ]; then
-    "${WGET_CMD[@]}" -O miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py311_25.3.1-1-Linux-x86_64.sh
-elif [ "$TARGETPLATFORM" = "linux/arm64" ]; then
-    "${WGET_CMD[@]}" -O miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py311_25.3.1-1-Linux-aarch64.sh
-else
-    exit 1
-fi
-
-LOG_PATH="/tmp/miniconda-install.log"
-
-bash miniconda.sh -b -p "$HOME/miniconda3" >"$LOG_PATH" 2>&1
-
-if [ $? -eq 0 ]; then
-    echo "== Miniconda Installed =="
-else
-    echo "Failed to Install miniconda"
-    tail -n 50 "$LOG_PATH"
-    exit 1
-fi
-
-rm miniconda.sh
-
-source "$HOME/miniconda3/etc/profile.d/conda.sh"
-
-"$HOME/miniconda3/bin/conda" config --add channels conda-forge
-
-"$HOME/miniconda3/bin/conda" update -q --all -y 1>/dev/null
-
-"$HOME/miniconda3/bin/conda" install python=3.11 -q -y
-
-"$HOME/miniconda3/bin/conda" install gcc=14 gxx ffmpeg cmake make unzip -q -y
-
-if [ "$CUDA_VERSION" = "12.8" ]; then
-    "$HOME/miniconda3/bin/pip" install torch torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/cu128
-elif [ "$CUDA_VERSION" = "12.6" ]; then
-    "$HOME/miniconda3/bin/pip" install torch==2.6 torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/cu126
-fi
-
-"$HOME/miniconda3/bin/pip" cache purge
-
-rm $LOG_PATH
-
-rm -rf "$HOME/miniconda3/pkgs"
-
-mkdir -p "$HOME/miniconda3/pkgs"
-
-rm -rf "$HOME/.conda" "$HOME/.cache"

Dockerfile

@@ -1,62 +1,42 @@
-ARG CUDA_VERSION=12.6
-ARG TORCH_BASE=full
+# Base CUDA image
+FROM cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04
 
-FROM xxxxrt666/torch-base:cu${CUDA_VERSION}-${TORCH_BASE}
-
-LABEL maintainer="XXXXRT"
-LABEL version="V4"
+LABEL maintainer="breakstring@hotmail.com"
+LABEL version="dev-20240209"
 LABEL description="Docker image for GPT-SoVITS"
 
-ARG CUDA_VERSION=12.6
 
-ENV CUDA_VERSION=${CUDA_VERSION}
+# Install 3rd party apps
+ENV DEBIAN_FRONTEND=noninteractive
+ENV TZ=Etc/UTC
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends tzdata ffmpeg libsox-dev parallel aria2 git git-lfs && \
+    git lfs install && \
+    rm -rf /var/lib/apt/lists/*
 
-SHELL ["/bin/bash", "-c"]
+# Copy only requirements.txt initially to leverage Docker cache
+WORKDIR /workspace
+COPY requirements.txt /workspace/
+RUN pip install --no-cache-dir -r requirements.txt
 
-WORKDIR /workspace/GPT-SoVITS
+# Define a build-time argument for image type
+ARG IMAGE_TYPE=full
 
-COPY Docker /workspace/GPT-SoVITS/Docker/
+# Conditional logic based on the IMAGE_TYPE argument
+# Always copy the Docker directory, but only use it if IMAGE_TYPE is not "elite"
+COPY ./Docker /workspace/Docker 
+# elite 类型的镜像里面不包含额外的模型
+RUN if [ "$IMAGE_TYPE" != "elite" ]; then \
+        chmod +x /workspace/Docker/download.sh && \
+        /workspace/Docker/download.sh && \
+        python /workspace/Docker/download.py && \
+        python -m nltk.downloader averaged_perceptron_tagger cmudict; \
+    fi
 
-ARG LITE=false
-ENV LITE=${LITE}
 
-ARG WORKFLOW=false
-ENV WORKFLOW=${WORKFLOW}
-
-ARG TARGETPLATFORM
-ENV TARGETPLATFORM=${TARGETPLATFORM}
-
-RUN bash Docker/miniconda_install.sh
-
-COPY extra-req.txt /workspace/GPT-SoVITS/
-
-COPY requirements.txt /workspace/GPT-SoVITS/
-
-COPY install.sh /workspace/GPT-SoVITS/
-
-RUN bash Docker/install_wrapper.sh
+# Copy the rest of the application
+COPY . /workspace
 
 EXPOSE 9871 9872 9873 9874 9880
 
-ENV PYTHONPATH="/workspace/GPT-SoVITS"
-
-RUN conda init bash && echo "conda activate base" >> ~/.bashrc
-
-WORKDIR /workspace
-
-RUN rm -rf /workspace/GPT-SoVITS
-
-WORKDIR /workspace/GPT-SoVITS
-
-COPY . /workspace/GPT-SoVITS
-
-CMD ["/bin/bash", "-c", "\
-  rm -rf /workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models && \
-  rm -rf /workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel && \
-  rm -rf /workspace/GPT-SoVITS/tools/asr/models && \
-  rm -rf /workspace/GPT-SoVITS/tools/uvr5/uvr5_weights && \
-  ln -s /workspace/models/pretrained_models /workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models && \
-  ln -s /workspace/models/G2PWModel /workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel && \
-  ln -s /workspace/models/asr_models /workspace/GPT-SoVITS/tools/asr/models && \
-  ln -s /workspace/models/uvr5_weights /workspace/GPT-SoVITS/tools/uvr5/uvr5_weights && \
-  exec bash"]
+CMD ["python", "webui.py"]

GPT_SoVITS/AR/data/bucket_sampler.py

@@ -4,11 +4,14 @@ import itertools
 import math
 import random
 from random import shuffle
-from typing import Iterator, Optional, TypeVar
+from typing import Iterator
+from typing import Optional
+from typing import TypeVar
 
 import torch
 import torch.distributed as dist
-from torch.utils.data import Dataset, Sampler
+from torch.utils.data import Dataset
+from torch.utils.data import Sampler
 
 __all__ = [
     "DistributedBucketSampler",
@@ -47,7 +50,10 @@ class DistributedBucketSampler(Sampler[T_co]):
             if torch.cuda.is_available():
                 torch.cuda.set_device(rank)
         if rank >= num_replicas or rank < 0:
-            raise ValueError("Invalid rank {}, rank should be in the interval [0, {}]".format(rank, num_replicas - 1))
+            raise ValueError(
+                "Invalid rank {}, rank should be in the interval"
+                " [0, {}]".format(rank, num_replicas - 1)
+            )
         self.dataset = dataset
         self.num_replicas = num_replicas
         self.rank = rank
@@ -55,16 +61,19 @@ class DistributedBucketSampler(Sampler[T_co]):
         self.drop_last = drop_last
         # If the dataset length is evenly divisible by # of replicas, then there
         # is no need to drop any data, since the dataset will be split equally.
-        if self.drop_last and len(self.dataset) % self.num_replicas != 0:  # type: ignore[arg-type]
+        if (
+            self.drop_last and len(self.dataset) % self.num_replicas != 0
+        ):  # type: ignore[arg-type]
             # Split to nearest available length that is evenly divisible.
             # This is to ensure each rank receives the same amount of data when
             # using this Sampler.
             self.num_samples = math.ceil(
-                (len(self.dataset) - self.num_replicas) / self.num_replicas,  # type: ignore[arg-type]
+                (len(self.dataset) - self.num_replicas)
+                / self.num_replicas  # type: ignore[arg-type]
             )
         else:
             self.num_samples = math.ceil(
-                len(self.dataset) / self.num_replicas,
+                len(self.dataset) / self.num_replicas
             )  # type: ignore[arg-type]
         self.total_size = self.num_samples * self.num_replicas
         self.shuffle = shuffle
@@ -109,7 +118,10 @@ class DistributedBucketSampler(Sampler[T_co]):
             grouped_batch_size = self.batch_size * self.num_replicas
             shuffled_bucket = list(itertools.chain(*shuffled_bucket))
             n_batch = int(math.ceil(len(shuffled_bucket) / grouped_batch_size))
-            batches = [shuffled_bucket[b * grouped_batch_size : (b + 1) * grouped_batch_size] for b in range(n_batch)]
+            batches = [
+                shuffled_bucket[b * grouped_batch_size : (b + 1) * grouped_batch_size]
+                for b in range(n_batch)
+            ]
             shuffle(batches)
             indices = list(itertools.chain(*batches))
         else:
@@ -122,7 +134,9 @@ class DistributedBucketSampler(Sampler[T_co]):
             if padding_size <= len(indices):
                 indices += indices[:padding_size]
             else:
-                indices += (indices * math.ceil(padding_size / len(indices)))[:padding_size]
+                indices += (indices * math.ceil(padding_size / len(indices)))[
+                    :padding_size
+                ]
         else:
             # remove tail of data to make it evenly divisible.
             indices = indices[: self.total_size]

GPT_SoVITS/AR/data/data_module.py

@@ -1,10 +1,9 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/data/data_module.py
 # reference: https://github.com/lifeiteng/vall-e
 from pytorch_lightning import LightningDataModule
-from torch.utils.data import DataLoader
-
 from AR.data.bucket_sampler import DistributedBucketSampler
 from AR.data.dataset import Text2SemanticDataset
+from torch.utils.data import DataLoader
 
 
 class Text2SemanticDataModule(LightningDataModule):
@@ -43,12 +42,8 @@ class Text2SemanticDataModule(LightningDataModule):
         #     pad_val=self.config['data']['pad_val'])
 
     def train_dataloader(self):
-        batch_size = (
-            self.config["train"]["batch_size"] // 2
-            if self.config["train"].get("if_dpo", False) is True
-            else self.config["train"]["batch_size"]
-        )
-        batch_size = max(min(batch_size, len(self._train_dataset) // 4), 1)  # 防止不保存
+        batch_size=self.config["train"]["batch_size"]//2 if self.config["train"].get("if_dpo",False)==True else self.config["train"]["batch_size"]
+        batch_size = max(min(batch_size,len(self._train_dataset)//4),1)#防止不保存
         sampler = DistributedBucketSampler(self._train_dataset, batch_size=batch_size)
         return DataLoader(
             self._train_dataset,

GPT_SoVITS/AR/data/dataset.py

@@ -1,17 +1,19 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/data/dataset.py
 # reference: https://github.com/lifeiteng/vall-e
+import pdb
+import sys
 
 # sys.path.append("/data/docker/liujing04/gpt-vits/mq-vits-s1bert_no_bert")
-import os
-import traceback
-from typing import Dict, List
+import traceback, os
+from typing import Dict
+from typing import List
 
 import numpy as np
 import pandas as pd
-import torch
-from torch.utils.data import DataLoader, Dataset
-
-version = os.environ.get("version", None)
+import torch, json
+from torch.utils.data import DataLoader
+from torch.utils.data import Dataset
+from transformers import AutoTokenizer
 
 from text import cleaned_text_to_sequence
 
@@ -30,7 +32,9 @@ def batch_sequences(sequences: List[np.array], axis: int = 0, pad_value: int = 0
 
     padded_sequences = []
     for seq, length in zip(sequences, seq_lengths):
-        padding = [(0, 0)] * axis + [(0, max_length - length)] + [(0, 0)] * (ndim - axis - 1)
+        padding = (
+            [(0, 0)] * axis + [(0, max_length - length)] + [(0, 0)] * (ndim - axis - 1)
+        )
         padded_seq = np.pad(seq, padding, mode="constant", constant_values=pad_value)
         padded_sequences.append(padded_seq)
     batch = np.stack(padded_sequences)
@@ -55,16 +59,12 @@ class Text2SemanticDataset(Dataset):
         super().__init__()
 
         self.semantic_data = pd.read_csv(
-            semantic_path,
-            delimiter="\t",
-            encoding="utf-8",
+            semantic_path, delimiter="\t", encoding="utf-8"
         )
         # get dict
         self.path2 = phoneme_path  # "%s/2-name2text.txt"%exp_dir#phoneme_path
         self.path3 = "%s/3-bert" % (
-            os.path.dirname(
-                phoneme_path,
-            )
+            os.path.dirname(phoneme_path)
         )  # "%s/3-bert"%exp_dir#bert_dir
         self.path6 = semantic_path  # "%s/6-name2semantic.tsv"%exp_dir#semantic_path
         assert os.path.exists(self.path2)
@@ -125,7 +125,7 @@ class Text2SemanticDataset(Dataset):
         for i in range(semantic_data_len):
             # 先依次遍历
             # get str
-            item_name = self.semantic_data.iloc[i, 0]
+            item_name = self.semantic_data.iloc[i,0]
             # print(self.phoneme_data)
             try:
                 phoneme, word2ph, text = self.phoneme_data[item_name]
@@ -135,7 +135,7 @@ class Text2SemanticDataset(Dataset):
                 num_not_in += 1
                 continue
 
-            semantic_str = self.semantic_data.iloc[i, 1]
+            semantic_str = self.semantic_data.iloc[i,1]
             # get token list
             semantic_ids = [int(idx) for idx in semantic_str.split(" ")]
             # (T), 是否需要变成 (1, T) -> 不需要，因为需要求 len
@@ -149,14 +149,16 @@ class Text2SemanticDataset(Dataset):
             phoneme = phoneme.split(" ")
 
             try:
-                phoneme_ids = cleaned_text_to_sequence(phoneme, version)
+                phoneme_ids = cleaned_text_to_sequence(phoneme)
             except:
                 traceback.print_exc()
                 # print(f"{item_name} not in self.phoneme_data !")
                 num_not_in += 1
                 continue
             # if len(phoneme_ids) >400:###########2：改为恒定限制为semantic/2.5就行
-            if len(phoneme_ids) > self.max_sec * self.hz / 2.5:  ###########2：改为恒定限制为semantic/2.5就行
+            if (
+                len(phoneme_ids) > self.max_sec * self.hz / 2.5
+            ):  ###########2：改为恒定限制为semantic/2.5就行
                 num_deleted_ps += 1
                 continue
             # if len(semantic_ids) > 1000:###########3
@@ -165,7 +167,9 @@ class Text2SemanticDataset(Dataset):
 
             ps_ratio = len(phoneme_ids) / (len(semantic_ids) / self.hz)
 
-            if ps_ratio > self.max_ps_ratio or ps_ratio < self.min_ps_ratio:  ##########4#3~25#每秒多少个phone
+            if (
+                ps_ratio > self.max_ps_ratio or ps_ratio < self.min_ps_ratio
+            ):  ##########4#3~25#每秒多少个phone
                 num_deleted_ps += 1
                 # print(item_name)
                 continue
@@ -188,12 +192,12 @@ class Text2SemanticDataset(Dataset):
             print(f"there are {num_not_in} semantic datas not in phoneme datas")
         if num_deleted_bigger > 0:
             print(
-                f"deleted {num_deleted_bigger} audios who's duration are bigger than {self.max_sec} seconds",
+                f"deleted {num_deleted_bigger} audios who's duration are bigger than {self.max_sec} seconds"
             )
         if num_deleted_ps > 0:
             # 4702 for LibriTTS, LirbriTTS 是标注数据, 是否需要筛？=> 需要，有值为 100 的极端值
             print(
-                f"deleted {num_deleted_ps} audios who's phoneme/sec are bigger than {self.max_ps_ratio} or smaller than {self.min_ps_ratio}",
+                f"deleted {num_deleted_ps} audios who's phoneme/sec are bigger than {self.max_ps_ratio} or smaller than {self.min_ps_ratio}"
             )
         """
         there are 31 semantic datas not in phoneme datas
@@ -300,10 +304,7 @@ if __name__ == "__main__":
 
     batch_size = 12
     dataloader = DataLoader(
-        dataset,
-        batch_size=batch_size,
-        collate_fn=dataset.collate,
-        shuffle=False,
+        dataset, batch_size=batch_size, collate_fn=dataset.collate, shuffle=False
     )
     for i, batch in enumerate(dataloader):
         if i % 1000 == 0:

GPT_SoVITS/AR/models/t2s_lightning_module.py

@@ -1,7 +1,6 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_lightning_module.py
 # reference: https://github.com/lifeiteng/vall-e
-import os
-import sys
+import os, sys
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
@@ -9,12 +8,10 @@ from typing import Dict
 
 import torch
 from pytorch_lightning import LightningModule
-
 from AR.models.t2s_model import Text2SemanticDecoder
 from AR.modules.lr_schedulers import WarmupCosineLRSchedule
 from AR.modules.optim import ScaledAdam
 
-
 class Text2SemanticLightningModule(LightningModule):
     def __init__(self, config, output_dir, is_train=True):
         super().__init__()
@@ -26,10 +23,7 @@ class Text2SemanticLightningModule(LightningModule):
             # print(self.load_state_dict(torch.load(pretrained_s1,map_location="cpu")["state_dict"]))
             print(
                 self.load_state_dict(
-                    torch.load(
-                        pretrained_s1,
-                        map_location="cpu", weights_only=False,
-                    )["weight"],
+                    torch.load(pretrained_s1, map_location="cpu")["weight"]
                 )
             )
         if is_train:
@@ -41,7 +35,7 @@ class Text2SemanticLightningModule(LightningModule):
     def training_step(self, batch: Dict, batch_idx: int):
         opt = self.optimizers()
         scheduler = self.lr_schedulers()
-        forward = self.model.forward if self.config["train"].get("if_dpo", False) == True else self.model.forward_old
+        forward=self.model.forward if self.config["train"].get("if_dpo",False)==True else self.model.forward_old
         loss, acc = forward(
             batch["phoneme_ids"],
             batch["phoneme_ids_len"],
@@ -119,7 +113,9 @@ class Text2SemanticLightningModule(LightningModule):
     def configure_optimizers(self):
         model_parameters = self.model.parameters()
         parameters_names = []
-        parameters_names.append([name_param_pair[0] for name_param_pair in self.model.named_parameters()])
+        parameters_names.append(
+            [name_param_pair[0] for name_param_pair in self.model.named_parameters()]
+        )
         lm_opt = ScaledAdam(
             model_parameters,
             lr=0.01,

GPT_SoVITS/AR/models/t2s_lightning_module_onnx.py

@@ -1,7 +1,6 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_lightning_module.py
 # reference: https://github.com/lifeiteng/vall-e
-import os
-import sys
+import os, sys
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
@@ -9,7 +8,6 @@ from typing import Dict
 
 import torch
 from pytorch_lightning import LightningModule
-
 from AR.models.t2s_model_onnx import Text2SemanticDecoder
 from AR.modules.lr_schedulers import WarmupCosineLRSchedule
 from AR.modules.optim import ScaledAdam
@@ -26,11 +24,8 @@ class Text2SemanticLightningModule(LightningModule):
             # print(self.load_state_dict(torch.load(pretrained_s1,map_location="cpu")["state_dict"]))
             print(
                 self.load_state_dict(
-                    torch.load(
-                        pretrained_s1,
-                        map_location="cpu",
-                    )["weight"],
-                ),
+                    torch.load(pretrained_s1, map_location="cpu")["weight"]
+                )
             )
         if is_train:
             self.automatic_optimization = False
@@ -84,7 +79,9 @@ class Text2SemanticLightningModule(LightningModule):
     def configure_optimizers(self):
         model_parameters = self.model.parameters()
         parameters_names = []
-        parameters_names.append([name_param_pair[0] for name_param_pair in self.model.named_parameters()])
+        parameters_names.append(
+            [name_param_pair[0] for name_param_pair in self.model.named_parameters()]
+        )
         lm_opt = ScaledAdam(
             model_parameters,
             lr=0.01,

GPT_SoVITS/AR/models/t2s_model.py

@@ -1,25 +1,31 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
 # reference: https://github.com/lifeiteng/vall-e
 import math
+import os, sys
+now_dir = os.getcwd()
+sys.path.append(now_dir)
 from typing import List, Optional
-
 import torch
+from tqdm import tqdm
+
+from AR.models.utils import make_pad_mask
+from AR.models.utils import (
+    topk_sampling,
+    sample,
+    logits_to_probs,
+    multinomial_sample_one_no_sync,
+    dpo_loss,
+    make_reject_y, 
+    get_batch_logps
+)
+from AR.modules.embedding import SinePositionalEmbedding
+from AR.modules.embedding import TokenEmbedding
+from AR.modules.transformer import LayerNorm
+from AR.modules.transformer import TransformerEncoder
+from AR.modules.transformer import TransformerEncoderLayer
 from torch import nn
 from torch.nn import functional as F
 from torchmetrics.classification import MulticlassAccuracy
-from tqdm import tqdm
-
-from AR.models.utils import (
-    dpo_loss,
-    get_batch_logps,
-    make_pad_mask,
-    make_pad_mask_left,
-    make_reject_y,
-    sample,
-    topk_sampling,
-)
-from AR.modules.embedding import SinePositionalEmbedding, TokenEmbedding
-from AR.modules.transformer import LayerNorm, TransformerEncoder, TransformerEncoderLayer
 
 default_config = {
     "embedding_dim": 512,
@@ -33,17 +39,10 @@ default_config = {
     "EOS": 1024,
 }
 
-
-# @torch.jit.script ## 使用的话首次推理会非常慢，而且推理速度不稳定
+@torch.jit.script
 # Efficient implementation equivalent to the following:
-def scaled_dot_product_attention(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: Optional[torch.Tensor] = None,
-    scale: Optional[torch.Tensor] = None,
-) -> torch.Tensor:
-    B, H, L, S = query.size(0), query.size(1), query.size(-2), key.size(-2)
+def scaled_dot_product_attention(query:torch.Tensor, key:torch.Tensor, value:torch.Tensor, attn_mask:Optional[torch.Tensor]=None, scale:Optional[torch.Tensor]=None) -> torch.Tensor:
+    B, H, L, S =query.size(0), query.size(1), query.size(-2), key.size(-2)
     if scale is None:
         scale_factor = torch.tensor(1 / math.sqrt(query.size(-1)))
     else:
@@ -63,13 +62,12 @@ def scaled_dot_product_attention(
         if attn_mask.dtype == torch.bool:
             attn_weight.masked_fill_(attn_mask, 0)
         else:
-            attn_mask[attn_mask != float("-inf")] = 0
-            attn_mask[attn_mask == float("-inf")] = 1
+            attn_mask[attn_mask!=float("-inf")] =0
+            attn_mask[attn_mask==float("-inf")] =1
             attn_weight.masked_fill_(attn_mask, 0)
 
     return attn_weight @ value
 
-
 @torch.jit.script
 class T2SMLP:
     def __init__(self, w1, b1, w2, b2):
@@ -119,32 +117,24 @@ class T2SBlock:
         self.false = torch.tensor(False, dtype=torch.bool)
 
     @torch.jit.ignore
-    def to_mask(
-        self,
-        x: torch.Tensor,
-        padding_mask: Optional[torch.Tensor],
-    ):
+    def to_mask(self, x:torch.Tensor, padding_mask:Optional[torch.Tensor]):
         if padding_mask is None:
             return x
-
+        
         if padding_mask.dtype == torch.bool:
             return x.masked_fill(padding_mask, 0)
         else:
             return x * padding_mask
+        
+    def process_prompt(self, x:torch.Tensor, attn_mask : torch.Tensor, padding_mask:Optional[torch.Tensor]=None):
 
-    def process_prompt(
-        self,
-        x: torch.Tensor,
-        attn_mask: torch.Tensor,
-        padding_mask: Optional[torch.Tensor] = None,
-        torch_sdpa: bool = True,
-    ):
+            
         q, k, v = F.linear(self.to_mask(x, padding_mask), self.qkv_w, self.qkv_b).chunk(3, dim=-1)
 
         batch_size = q.shape[0]
         q_len = q.shape[1]
         kv_len = k.shape[1]
-
+        
         q = self.to_mask(q, padding_mask)
         k_cache = self.to_mask(k, padding_mask)
         v_cache = self.to_mask(v, padding_mask)
@@ -153,39 +143,55 @@ class T2SBlock:
         k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
         v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
 
-        if torch_sdpa:
-            attn = F.scaled_dot_product_attention(q, k, v, ~attn_mask)
-        else:
-            attn = scaled_dot_product_attention(q, k, v, attn_mask)
+        attn = scaled_dot_product_attention(q, k, v, attn_mask)
 
-        attn = attn.transpose(1, 2).reshape(batch_size, q_len, -1)
+        attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
+        attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
         attn = F.linear(self.to_mask(attn, padding_mask), self.out_w, self.out_b)
 
-        x = x + attn
-        x = F.layer_norm(x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1)
-        x = x + self.mlp.forward(x)
-        x = F.layer_norm(
-            x,
-            [self.hidden_dim],
-            self.norm_w2,
-            self.norm_b2,
-            self.norm_eps2,
-        )
+        if padding_mask is not None:
+            for i in range(batch_size):
+                # mask = padding_mask[i,:,0]
+                if self.false.device!= padding_mask.device:
+                    self.false = self.false.to(padding_mask.device)
+                idx = torch.where(padding_mask[i,:,0]==self.false)[0]
+                x_item = x[i,idx,:].unsqueeze(0)
+                attn_item = attn[i,idx,:].unsqueeze(0)
+                x_item = x_item + attn_item
+                x_item = F.layer_norm(
+                    x_item, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
+                )
+                x_item = x_item + self.mlp.forward(x_item)
+                x_item = F.layer_norm(
+                    x_item,
+                    [self.hidden_dim],
+                    self.norm_w2,
+                    self.norm_b2,
+                    self.norm_eps2,
+                )
+                x[i,idx,:] = x_item.squeeze(0)
+            x = self.to_mask(x, padding_mask)
+        else:
+            x = x + attn
+            x = F.layer_norm(
+                x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
+            )
+            x = x + self.mlp.forward(x)
+            x = F.layer_norm(
+                x,
+                [self.hidden_dim],
+                self.norm_w2,
+                self.norm_b2,
+                self.norm_eps2,
+            )
         return x, k_cache, v_cache
-
-    def decode_next_token(
-        self,
-        x: torch.Tensor,
-        k_cache: torch.Tensor,
-        v_cache: torch.Tensor,
-        attn_mask: torch.Tensor = None,
-        torch_sdpa: bool = True,
-    ):
+    
+    def decode_next_token(self, x:torch.Tensor, k_cache:torch.Tensor, v_cache:torch.Tensor, attn_mask:Optional[torch.Tensor]=None):
         q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1)
 
         k_cache = torch.cat([k_cache, k], dim=1)
         v_cache = torch.cat([v_cache, v], dim=1)
-
+        
         batch_size = q.shape[0]
         q_len = q.shape[1]
         kv_len = k_cache.shape[1]
@@ -194,21 +200,16 @@ class T2SBlock:
         k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
         v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
 
-        if torch_sdpa:
-            attn = F.scaled_dot_product_attention(q, k, v, (~attn_mask) if attn_mask is not None else None)
-        else:
-            attn = scaled_dot_product_attention(q, k, v, attn_mask)
 
-        attn = attn.transpose(1, 2).reshape(batch_size, q_len, -1)
+        attn = scaled_dot_product_attention(q, k, v, attn_mask)
+
+        attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
+        attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
         attn = F.linear(attn, self.out_w, self.out_b)
 
         x = x + attn
         x = F.layer_norm(
-            x,
-            [self.hidden_dim],
-            self.norm_w1,
-            self.norm_b1,
-            self.norm_eps1,
+            x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
         )
         x = x + self.mlp.forward(x)
         x = F.layer_norm(
@@ -223,37 +224,27 @@ class T2SBlock:
 
 @torch.jit.script
 class T2STransformer:
-    def __init__(self, num_blocks: int, blocks: List[T2SBlock]):
-        self.num_blocks: int = num_blocks
+    def __init__(self, num_blocks : int, blocks: List[T2SBlock]):
+        self.num_blocks : int = num_blocks
         self.blocks = blocks
 
     def process_prompt(
-        self,
-        x: torch.Tensor,
-        attn_mask: torch.Tensor,
-        padding_mask: Optional[torch.Tensor] = None,
-        torch_sdpa: bool = True,
-    ):
-        k_cache: List[torch.Tensor] = []
-        v_cache: List[torch.Tensor] = []
+        self, x:torch.Tensor, attn_mask : torch.Tensor,
+        padding_mask : Optional[torch.Tensor]=None,
+        ):
+        k_cache : List[torch.Tensor] = []
+        v_cache : List[torch.Tensor] = []
         for i in range(self.num_blocks):
-            x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask, padding_mask, torch_sdpa)
+            x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask, padding_mask)
             k_cache.append(k_cache_)
             v_cache.append(v_cache_)
         return x, k_cache, v_cache
 
     def decode_next_token(
-        self,
-        x: torch.Tensor,
-        k_cache: List[torch.Tensor],
-        v_cache: List[torch.Tensor],
-        attn_mask: torch.Tensor = None,
-        torch_sdpa: bool = True,
+        self, x:torch.Tensor, k_cache: List[torch.Tensor], v_cache: List[torch.Tensor], attn_mask : Optional[torch.Tensor]=None,
     ):
         for i in range(self.num_blocks):
-            x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(
-                x, k_cache[i], v_cache[i], attn_mask, torch_sdpa
-            )
+            x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i], attn_mask)
         return x, k_cache, v_cache
 
 
@@ -275,26 +266,16 @@ class Text2SemanticDecoder(nn.Module):
         # assert self.EOS == 1024
         self.bert_proj = nn.Linear(1024, self.embedding_dim)
         self.ar_text_embedding = TokenEmbedding(
-            self.embedding_dim,
-            self.phoneme_vocab_size,
-            self.p_dropout,
+            self.embedding_dim, self.phoneme_vocab_size, self.p_dropout
         )
         self.ar_text_position = SinePositionalEmbedding(
-            self.embedding_dim,
-            dropout=0.1,
-            scale=False,
-            alpha=True,
+            self.embedding_dim, dropout=0.1, scale=False, alpha=True
         )
         self.ar_audio_embedding = TokenEmbedding(
-            self.embedding_dim,
-            self.vocab_size,
-            self.p_dropout,
+            self.embedding_dim, self.vocab_size, self.p_dropout
         )
         self.ar_audio_position = SinePositionalEmbedding(
-            self.embedding_dim,
-            dropout=0.1,
-            scale=False,
-            alpha=True,
+            self.embedding_dim, dropout=0.1, scale=False, alpha=True
         )
 
         self.h = TransformerEncoder(
@@ -329,7 +310,7 @@ class Text2SemanticDecoder(nn.Module):
                 layer.linear1.weight,
                 layer.linear1.bias,
                 layer.linear2.weight,
-                layer.linear2.bias,
+                layer.linear2.bias
             )
 
             block = T2SBlock(
@@ -345,11 +326,11 @@ class Text2SemanticDecoder(nn.Module):
                 layer.norm1.eps,
                 layer.norm2.weight,
                 layer.norm2.bias,
-                layer.norm2.eps,
+                layer.norm2.eps
             )
 
             blocks.append(block)
-
+        
         self.t2s_transformer = T2STransformer(self.num_layers, blocks)
 
     def make_input_data(self, x, x_lens, y, y_lens, bert_feature):
@@ -423,9 +404,7 @@ class Text2SemanticDecoder(nn.Module):
         logits = self.ar_predict_layer(xy_dec[:, x_len:])
 
         ###### DPO #############
-        reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data(
-            x, x_lens, reject_y, reject_y_lens, bert_feature
-        )
+        reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data(x, x_lens, reject_y, reject_y_lens, bert_feature)
 
         reject_xy_dec, _ = self.h(
             (reject_xy_pos, None),
@@ -442,7 +421,7 @@ class Text2SemanticDecoder(nn.Module):
 
         A_logits, R_logits = get_batch_logps(logits, reject_logits, targets, reject_targets)
         loss_2, _, _ = dpo_loss(A_logits, R_logits, 0, 0, 0.2, reference_free=True)
-
+        
         loss = loss_1 + loss_2
 
         return loss, acc
@@ -477,6 +456,7 @@ class Text2SemanticDecoder(nn.Module):
             (0, y_len),
             value=True,
         )
+        # x_attn_mask[:, x_len]=False 
         y_attn_mask = F.pad(
             torch.triu(
                 torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
@@ -542,18 +522,22 @@ class Text2SemanticDecoder(nn.Module):
                 value=True,
             )
             y_attn_mask = F.pad(
-                torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+                torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=0),
                 (x_len, 0),
                 value=False,
             )
-            xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(y.device)
+            xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
+                y.device
+            )
 
             xy_dec, _ = self.h(
                 (xy_pos, None),
                 mask=xy_attn_mask,
             )
             logits = self.ar_predict_layer(xy_dec[:, -1])
-            samples = topk_sampling(logits, top_k=top_k, top_p=1.0, temperature=temperature)
+            samples = topk_sampling(
+                logits, top_k=top_k, top_p=1.0, temperature=temperature
+            )
 
             if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
                 print("use early stop num:", early_stop_num)
@@ -576,16 +560,18 @@ class Text2SemanticDecoder(nn.Module):
         return y
 
     def pad_y_eos(self, y, y_mask_int, eos_id):
-        targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(y_mask_int, (0, 1), value=1)
+        targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(
+            y_mask_int, (0, 1), value=1
+        )
         # 错位
         return targets[:, :-1], targets[:, 1:]
 
     def infer_panel_batch_infer(
         self,
-        x: List[torch.LongTensor],  #####全部文本token
-        x_lens: torch.LongTensor,
-        prompts: torch.LongTensor,  ####参考音频token
-        bert_feature: List[torch.LongTensor],
+        x:List[torch.LongTensor],  #####全部文本token
+        x_lens:torch.LongTensor,
+        prompts:torch.LongTensor,  ####参考音频token
+        bert_feature:List[torch.LongTensor],
         top_k: int = -100,
         top_p: int = 100,
         early_stop_num: int = -1,
@@ -595,168 +581,149 @@ class Text2SemanticDecoder(nn.Module):
     ):
         if prompts is None:
             print("Warning: Prompt free is not supported batch_infer! switch to naive_infer")
-            return self.infer_panel_naive_batched(
-                x,
-                x_lens,
-                prompts,
-                bert_feature,
-                top_k=top_k,
-                top_p=top_p,
-                early_stop_num=early_stop_num,
-                temperature=temperature,
-                **kwargs,
-            )
+            return self.infer_panel_0307(x, x_lens, prompts, bert_feature, top_k=top_k, top_p=top_p, early_stop_num=early_stop_num, temperature=temperature, **kwargs)
 
-        max_len = kwargs.get("max_len", x_lens.max())
+        max_len = kwargs.get("max_len",x_lens.max())
         x_list = []
         for x_item, bert_item in zip(x, bert_feature):
             # max_len = max(max_len, x_item.shape[0], bert_item.shape[1])
             x_item = self.ar_text_embedding(x_item.unsqueeze(0))
             x_item = x_item + self.bert_proj(bert_item.transpose(0, 1).unsqueeze(0))
             x_item = self.ar_text_position(x_item).squeeze(0)
-            # x_item = F.pad(x_item,(0,0,0,max_len-x_item.shape[0]),value=0) if x_item.shape[0]<max_len else x_item  ### padding right
-            x_item = (
-                F.pad(x_item, (0, 0, max_len - x_item.shape[0], 0), value=0) if x_item.shape[0] < max_len else x_item
-            )  ### padding left
+            x_item = F.pad(x_item,(0,0,0,max_len-x_item.shape[0]),value=0) if x_item.shape[0]<max_len else x_item
             x_list.append(x_item)
-        x: torch.Tensor = torch.stack(x_list, dim=0)
+        x = torch.stack(x_list, dim=0)
+
 
         # AR Decoder
         y = prompts
-
+        
         x_len = x.shape[1]
+        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
         stop = False
 
         k_cache = None
         v_cache = None
         ###################  first step ##########################
-        assert y is not None, "Error: Prompt free is not supported batch_infer!"
-        ref_free = False
+        if y is not None:
+            y_emb = self.ar_audio_embedding(y)
+            y_len = y_emb.shape[1]
+            prefix_len = y.shape[1]
+            y_lens = torch.LongTensor([y_emb.shape[1]]*y_emb.shape[0]).to(x.device)
+            y_pos = self.ar_audio_position(y_emb)
+            xy_pos = torch.concat([x, y_pos], dim=1)
+            ref_free = False
+        else:
+            y_emb = None
+            y_len = 0
+            prefix_len = 0
+            y_lens = torch.LongTensor([y_len]*x.shape[0]).to(x.device)
+            y_pos = None
+            xy_pos = x
+            y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
+            ref_free = True
 
-        y_emb = self.ar_audio_embedding(y)
-        y_len = y_emb.shape[1]
-        prefix_len = y.shape[1]
-        y_lens = torch.LongTensor([y_emb.shape[1]] * y_emb.shape[0]).to(x.device)
-        y_pos = self.ar_audio_position(y_emb)
-        xy_pos = torch.concat([x, y_pos], dim=1)
 
         ##### create mask #####
         bsz = x.shape[0]
         src_len = x_len + y_len
-        y_paddind_mask = make_pad_mask_left(y_lens, y_len)
-        x_paddind_mask = make_pad_mask_left(x_lens, max_len)
-
+        y_paddind_mask = make_pad_mask(y_lens, y_len)
+        x_paddind_mask = make_pad_mask(x_lens, max_len)
+        
         # (bsz, x_len + y_len)
-        padding_mask = torch.concat([x_paddind_mask, y_paddind_mask], dim=1)
+        xy_padding_mask = torch.concat([x_paddind_mask, y_paddind_mask], dim=1)
 
         x_mask = F.pad(
-            torch.zeros(x_len, x_len, dtype=torch.bool, device=x.device),
-            (0, y_len),
+            x_attn_mask,
+            (0, y_len),  ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
             value=True,
         )
-
         y_mask = F.pad(  ###yy的右上1扩展到左边xy的0,(y,x+y)
-            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool, device=x.device), diagonal=1),
+            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1), 
             (x_len, 0),
             value=False,
         )
-
-        causal_mask = torch.concat([x_mask, y_mask], dim=0).view(1, src_len, src_len).repeat(bsz, 1, 1).to(x.device)
-        # padding_mask = padding_mask.unsqueeze(1) * padding_mask.unsqueeze(2) ### [b, x+y, x+y]
-        ### 上面是错误的，会导致padding的token被"看见"
-
-        # 正确的padding_mask应该是：
-        # |   pad_len   |  x_len  |  y_len  |
-        # [[PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
-        # [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
-        # [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],  前3行按理说也应该被mask掉，但是为了防止计算attention时不出现nan，还是保留了，不影响结果
-        # [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
-        # [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
-        # [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
-        # [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
-        # [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
-        # [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6]]
-
-        padding_mask = padding_mask.view(bsz, 1, src_len).repeat(1, src_len, 1)
-
-        attn_mask: torch.Tensor = causal_mask.logical_or(padding_mask)
-        attn_mask = attn_mask.unsqueeze(1).expand(-1, self.num_head, -1, -1).bool()
-
-        # 正确的attn_mask应该是这样的：
-        # |   pad_len   |  x_len  |  y_len  |
-        # [[PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],
-        # [PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],
-        # [PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],  前3行按理说也应该被mask掉，但是为了防止计算attention时不出现nan，还是保留了，不影响结果
-        # [PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],
-        # [PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],
-        # [PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],
-        # [PAD, PAD, PAD, 1, 2, 3,   4, EOS, EOS],
-        # [PAD, PAD, PAD, 1, 2, 3,   4,   5, EOS],
-        # [PAD, PAD, PAD, 1, 2, 3,   4,   5,   6]]
+        
+        xy_mask = torch.concat([x_mask, y_mask], dim=0).view(1 , src_len, src_len).repeat(bsz, 1, 1).to(x.device)
+        _xy_padding_mask = xy_padding_mask.view(bsz, 1, src_len).repeat(1, src_len, 1)
+        
+        for i in range(bsz):
+            l = x_lens[i]
+            _xy_padding_mask[i,l:max_len,:]=True
+            
+        xy_attn_mask = xy_mask.logical_or(_xy_padding_mask)
+        xy_attn_mask = xy_attn_mask.unsqueeze(1).expand(-1, self.num_head, -1, -1)
+        xy_attn_mask = xy_attn_mask.bool()
+        xy_padding_mask = xy_padding_mask.view(bsz, src_len, 1).expand(-1, -1, self.model_dim)
 
         ###### decode #####
-        y_list = [None] * y.shape[0]
+        y_list = [None]*y.shape[0]
         batch_idx_map = list(range(y.shape[0]))
-        idx_list = [None] * y.shape[0]
+        idx_list = [None]*y.shape[0]
         for idx in tqdm(range(1500)):
             if idx == 0:
-                xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, attn_mask, None)
+                xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, xy_padding_mask)
             else:
-                xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache, attn_mask)
-            logits = self.ar_predict_layer(xy_dec[:, -1])
+                xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache, xy_attn_mask)
+
+            logits = self.ar_predict_layer(
+                xy_dec[:, -1]
+            )
 
             if idx == 0:
-                attn_mask = F.pad(attn_mask[:, :, -1].unsqueeze(-2), (0, 1), value=False)
+                xy_attn_mask = F.pad(xy_attn_mask[:,:,-1].unsqueeze(-2),(0,1),value=False)
                 logits = logits[:, :-1]
             else:
-                attn_mask = F.pad(attn_mask, (0, 1), value=False)
+                xy_attn_mask = F.pad(xy_attn_mask,(0,1),value=False)
 
             samples = sample(
-                logits, y, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature
-            )[0]
+                    logits, y, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature
+                )[0]
 
             y = torch.concat([y, samples], dim=1)
-
+            
             ####### 移除batch中已经生成完毕的序列,进一步优化计算量
             tokens = torch.argmax(logits, dim=-1)
             reserved_idx_of_batch_for_y = None
-            if (self.EOS in samples[:, 0]) or (self.EOS in tokens):  ###如果生成到EOS，则停止
-                l1 = samples[:, 0] == self.EOS
-                l2 = tokens == self.EOS
-                l = l1.logical_or(l2)
-                removed_idx_of_batch_for_y = torch.where(l == True)[0].tolist()
-                reserved_idx_of_batch_for_y = torch.where(l == False)[0]
-                # batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y]
-                for i in removed_idx_of_batch_for_y:
-                    batch_index = batch_idx_map[i]
-                    idx_list[batch_index] = idx
-                    y_list[batch_index] = y[i, :-1]
-
-                batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()]
-
-            # 只保留batch中未生成完毕的序列
+            if (self.EOS in samples[:, 0]) or \
+                (self.EOS in tokens):  ###如果生成到EOS，则停止
+                    l1 = samples[:, 0]==self.EOS
+                    l2 = tokens==self.EOS
+                    l = l1.logical_or(l2)
+                    removed_idx_of_batch_for_y = torch.where(l==True)[0].tolist()
+                    reserved_idx_of_batch_for_y = torch.where(l==False)[0]
+                    # batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y]
+                    for i in removed_idx_of_batch_for_y:
+                        batch_index = batch_idx_map[i]
+                        idx_list[batch_index] = idx - 1
+                        y_list[batch_index] = y[i, :-1]
+                
+                    batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()]
+                
+            # 只保留batch中未生成完毕的序列 
             if reserved_idx_of_batch_for_y is not None:
                 # index = torch.LongTensor(batch_idx_map).to(y.device)
                 y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y)
-                attn_mask = torch.index_select(attn_mask, dim=0, index=reserved_idx_of_batch_for_y)
-                if k_cache is not None:
+                xy_attn_mask = torch.index_select(xy_attn_mask, dim=0, index=reserved_idx_of_batch_for_y)
+                if k_cache is not None :
                     for i in range(len(k_cache)):
                         k_cache[i] = torch.index_select(k_cache[i], dim=0, index=reserved_idx_of_batch_for_y)
                         v_cache[i] = torch.index_select(v_cache[i], dim=0, index=reserved_idx_of_batch_for_y)
-
-            if (early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num) or idx == 1499:
+                
+                
+            if (early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num) or idx==1499:
                 print("use early stop num:", early_stop_num)
                 stop = True
                 for i, batch_index in enumerate(batch_idx_map):
                     batch_index = batch_idx_map[i]
                     idx_list[batch_index] = idx
                     y_list[batch_index] = y[i, :-1]
-
-            if None not in idx_list:
+                
+            if not (None in idx_list):
                 stop = True
-
+                
             if stop:
-                if y.shape[1] == 0:
+                if y.shape[1]==0:
                     y = torch.concat([y, torch.zeros_like(samples)], dim=1)
                     print("bad zero prediction")
                 print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
@@ -764,65 +731,60 @@ class Text2SemanticDecoder(nn.Module):
 
             ####################### update next step ###################################
             y_emb = self.ar_audio_embedding(y[:, -1:])
-            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[
-                :, y_len + idx
-            ].to(dtype=y_emb.dtype, device=y_emb.device)
+            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to( dtype= y_emb.dtype,device=y_emb.device)            
 
-        if None in idx_list:
+        if (None in idx_list):
             for i in range(x.shape[0]):
                 if idx_list[i] is None:
-                    idx_list[i] = 1500 - 1  ###如果没有生成到EOS，就用最大长度代替
-
+                    idx_list[i] = 1500-1  ###如果没有生成到EOS，就用最大长度代替
+                    
         if ref_free:
-            return y_list, [0] * x.shape[0]
+            return y_list, [0]*x.shape[0]
         # print(idx_list)
         return y_list, idx_list
-
-    def infer_panel_naive_batched(
-        self,
-        x: List[torch.LongTensor],  #####全部文本token
-        x_lens: torch.LongTensor,
-        prompts: torch.LongTensor,  ####参考音频token
-        bert_feature: List[torch.LongTensor],
+    
+    def infer_panel_0307(self,
+        x:List[torch.LongTensor],  #####全部文本token
+        x_lens:torch.LongTensor,
+        prompts:torch.LongTensor,  ####参考音频token
+        bert_feature:List[torch.LongTensor],
         top_k: int = -100,
         top_p: int = 100,
         early_stop_num: int = -1,
         temperature: float = 1.0,
         repetition_penalty: float = 1.35,
-        **kwargs,
-    ):
+        **kwargs
+        ):
         y_list = []
         idx_list = []
         for i in range(len(x)):
-            y, idx = self.infer_panel_naive(
-                x[i].unsqueeze(0),
-                x_lens[i],
-                prompts[i].unsqueeze(0) if prompts is not None else None,
-                bert_feature[i].unsqueeze(0),
-                top_k,
-                top_p,
-                early_stop_num,
-                temperature,
-                repetition_penalty,
-                **kwargs,
-            )
+            y, idx = self.infer_panel_naive(x[i].unsqueeze(0), 
+                                                  x_lens[i], 
+                                                  prompts[i].unsqueeze(0) if prompts is not None else None, 
+                                                  bert_feature[i].unsqueeze(0), 
+                                                  top_k, 
+                                                  top_p, 
+                                                  early_stop_num, 
+                                                  temperature,
+                                                  repetition_penalty,
+                                                  **kwargs)
             y_list.append(y[0])
             idx_list.append(idx)
-
+        
         return y_list, idx_list
-
+    
     def infer_panel_naive(
         self,
-        x: torch.LongTensor,  #####全部文本token
-        x_lens: torch.LongTensor,
-        prompts: torch.LongTensor,  ####参考音频token
-        bert_feature: torch.LongTensor,
+        x:torch.LongTensor,  #####全部文本token
+        x_lens:torch.LongTensor,
+        prompts:torch.LongTensor,  ####参考音频token
+        bert_feature:torch.LongTensor,
         top_k: int = -100,
         top_p: int = 100,
         early_stop_num: int = -1,
         temperature: float = 1.0,
         repetition_penalty: float = 1.35,
-        **kwargs,
+        **kwargs
     ):
         x = self.ar_text_embedding(x)
         x = x + self.bert_proj(bert_feature.transpose(1, 2))
@@ -830,7 +792,7 @@ class Text2SemanticDecoder(nn.Module):
 
         # AR Decoder
         y = prompts
-
+        
         x_len = x.shape[1]
         x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
         stop = False
@@ -867,25 +829,22 @@ class Text2SemanticDecoder(nn.Module):
             (x_len, 0),
             value=False,
         )
-        xy_attn_mask = (
-            torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)
-            .unsqueeze(0)
-            .expand(bsz * self.num_head, -1, -1)
-            .view(bsz, self.num_head, src_len, src_len)
-            .to(device=x.device, dtype=torch.bool)
-        )
-
+        xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).unsqueeze(0).expand(bsz*self.num_head, -1, -1).view(bsz, self.num_head, src_len, src_len).to(x.device)
+        xy_attn_mask = xy_attn_mask.bool()
+        # new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
+        # xy_attn_mask = new_attn_mask.masked_fill(xy_attn_mask, float("-inf"))
         for idx in tqdm(range(1500)):
             if xy_attn_mask is not None:
                 xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None)
             else:
                 xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache)
 
-            logits = self.ar_predict_layer(xy_dec[:, -1])
+            logits = self.ar_predict_layer(
+                xy_dec[:, -1]
+            )
 
             if idx == 0:
                 xy_attn_mask = None
-            if idx < 11:  ###至少预测出10个token不然不给停止（0.4s）
                 logits = logits[:, :-1]
 
             samples = sample(
@@ -901,7 +860,7 @@ class Text2SemanticDecoder(nn.Module):
             if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
                 stop = True
             if stop:
-                if y.shape[1] == 0:
+                if y.shape[1]==0:
                     y = torch.concat([y, torch.zeros_like(samples)], dim=1)
                     print("bad zero prediction")
                 print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
@@ -909,27 +868,23 @@ class Text2SemanticDecoder(nn.Module):
 
             ####################### update next step ###################################
             y_emb = self.ar_audio_embedding(y[:, -1:])
-            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[
-                :, y_len + idx
-            ].to(dtype=y_emb.dtype, device=y_emb.device)
+            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device)
 
         if ref_free:
             return y[:, :-1], 0
-        return y[:, :-1], idx
-
+        return y[:, :-1], idx - 1
+    
     def infer_panel(
         self,
-        x: torch.LongTensor,  #####全部文本token
-        x_lens: torch.LongTensor,
-        prompts: torch.LongTensor,  ####参考音频token
-        bert_feature: torch.LongTensor,
+        x:torch.LongTensor,  #####全部文本token
+        x_lens:torch.LongTensor,
+        prompts:torch.LongTensor,  ####参考音频token
+        bert_feature:torch.LongTensor,
         top_k: int = -100,
         top_p: int = 100,
         early_stop_num: int = -1,
         temperature: float = 1.0,
         repetition_penalty: float = 1.35,
-        **kwargs,
+        **kwargs
     ):
-        return self.infer_panel_naive(
-            x, x_lens, prompts, bert_feature, top_k, top_p, early_stop_num, temperature, repetition_penalty, **kwargs
-        )
+        return self.infer_panel_naive(x, x_lens, prompts, bert_feature, top_k, top_p, early_stop_num, temperature, repetition_penalty, **kwargs)

GPT_SoVITS/AR/models/t2s_model_batch_only.py

@@ -0,0 +1,483 @@
+# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_model.py
+import torch
+from tqdm import tqdm
+
+from AR.models.utils import make_pad_mask
+from AR.models.utils import (
+    topk_sampling,
+    sample,
+    logits_to_probs,
+    multinomial_sample_one_no_sync,
+    dpo_loss,
+    make_reject_y, 
+    get_batch_logps
+)
+from AR.modules.embedding import SinePositionalEmbedding
+from AR.modules.embedding import TokenEmbedding
+from AR.modules.transformer import LayerNorm
+from AR.modules.transformer import TransformerEncoder
+from AR.modules.transformer import TransformerEncoderLayer
+from torch import nn
+from torch.nn import functional as F
+from torchmetrics.classification import MulticlassAccuracy
+
+default_config = {
+    "embedding_dim": 512,
+    "hidden_dim": 512,
+    "num_head": 8,
+    "num_layers": 12,
+    "num_codebook": 8,
+    "p_dropout": 0.0,
+    "vocab_size": 1024 + 1,
+    "phoneme_vocab_size": 512,
+    "EOS": 1024,
+}
+
+
+class Text2SemanticDecoder(nn.Module):
+    def __init__(self, config, norm_first=False, top_k=3):
+        super(Text2SemanticDecoder, self).__init__()
+        self.model_dim = config["model"]["hidden_dim"]
+        self.embedding_dim = config["model"]["embedding_dim"]
+        self.num_head = config["model"]["head"]
+        self.num_layers = config["model"]["n_layer"]
+        self.norm_first = norm_first
+        self.vocab_size = config["model"]["vocab_size"]
+        self.phoneme_vocab_size = config["model"]["phoneme_vocab_size"]
+        self.p_dropout = config["model"]["dropout"]
+        self.EOS = config["model"]["EOS"]
+        self.norm_first = norm_first
+        assert self.EOS == self.vocab_size - 1
+        # should be same as num of kmeans bin
+        # assert self.EOS == 1024
+        self.bert_proj = nn.Linear(1024, self.embedding_dim)
+        self.ar_text_embedding = TokenEmbedding(
+            self.embedding_dim, self.phoneme_vocab_size, self.p_dropout
+        )
+        self.ar_text_position = SinePositionalEmbedding(
+            self.embedding_dim, dropout=0.1, scale=False, alpha=True
+        )
+        self.ar_audio_embedding = TokenEmbedding(
+            self.embedding_dim, self.vocab_size, self.p_dropout
+        )
+        self.ar_audio_position = SinePositionalEmbedding(
+            self.embedding_dim, dropout=0.1, scale=False, alpha=True
+        )
+
+        self.h = TransformerEncoder(
+            TransformerEncoderLayer(
+                d_model=self.model_dim,
+                nhead=self.num_head,
+                dim_feedforward=self.model_dim * 4,
+                dropout=0.1,
+                batch_first=True,
+                norm_first=norm_first,
+            ),
+            num_layers=self.num_layers,
+            norm=LayerNorm(self.model_dim) if norm_first else None,
+        )
+
+        self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False)
+        self.loss_fct = nn.CrossEntropyLoss(reduction="sum")
+
+        self.ar_accuracy_metric = MulticlassAccuracy(
+            self.vocab_size,
+            top_k=top_k,
+            average="micro",
+            multidim_average="global",
+            ignore_index=self.EOS,
+        )
+
+    def make_input_data(self, x, x_lens, y, y_lens, bert_feature):
+        x = self.ar_text_embedding(x)
+        x = x + self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_position(x)
+        x_mask = make_pad_mask(x_lens)
+
+        y_mask = make_pad_mask(y_lens)
+        y_mask_int = y_mask.type(torch.int64)
+        codes = y.type(torch.int64) * (1 - y_mask_int)
+
+        # Training
+        # AR Decoder
+        y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS)
+        x_len = x_lens.max()
+        y_len = y_lens.max()
+        y_emb = self.ar_audio_embedding(y)
+        y_pos = self.ar_audio_position(y_emb)
+
+        xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
+
+        ar_xy_padding_mask = xy_padding_mask
+
+        x_attn_mask = F.pad(
+            torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),
+            (0, y_len),
+            value=True,
+        )
+        
+        y_attn_mask = F.pad(
+            torch.triu(
+                torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
+                diagonal=1,
+            ),
+            (x_len, 0),
+            value=False,
+        )
+
+        xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)
+        bsz, src_len = x.shape[0], x_len + y_len
+        _xy_padding_mask = (
+            ar_xy_padding_mask.view(bsz, 1, 1, src_len)
+            .expand(-1, self.num_head, -1, -1)
+            .reshape(bsz * self.num_head, 1, src_len)
+        )
+        xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
+        new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
+        new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
+        xy_attn_mask = new_attn_mask
+        # x 和完整的 y 一次性输入模型
+        xy_pos = torch.concat([x, y_pos], dim=1)
+
+        return xy_pos, xy_attn_mask, targets
+
+    def forward(self, x, x_lens, y, y_lens, bert_feature):
+        """
+        x: phoneme_ids
+        y: semantic_ids
+        """
+
+        reject_y, reject_y_lens = make_reject_y(y, y_lens)
+
+        xy_pos, xy_attn_mask, targets = self.make_input_data(x, x_lens, y, y_lens, bert_feature)
+
+        xy_dec, _ = self.h(
+            (xy_pos, None),
+            mask=xy_attn_mask,
+        )
+        x_len = x_lens.max()
+        logits = self.ar_predict_layer(xy_dec[:, x_len:])
+
+        ###### DPO #############
+        reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data(x, x_lens, reject_y, reject_y_lens, bert_feature)
+
+        reject_xy_dec, _ = self.h(
+            (reject_xy_pos, None),
+            mask=reject_xy_attn_mask,
+        )
+        x_len = x_lens.max()
+        reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len:])
+
+        # loss
+        # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
+
+        loss_1 = F.cross_entropy(logits.permute(0, 2, 1), targets, reduction="sum")
+        acc = self.ar_accuracy_metric(logits.permute(0, 2, 1).detach(), targets).item()
+
+        A_logits, R_logits = get_batch_logps(logits, reject_logits, targets, reject_targets)
+        loss_2, _, _ = dpo_loss(A_logits, R_logits, 0, 0, 0.2, reference_free=True)
+        
+        loss = loss_1 + loss_2
+
+        return loss, acc
+
+    def forward_old(self, x, x_lens, y, y_lens, bert_feature):
+        """
+        x: phoneme_ids
+        y: semantic_ids
+        """
+        x = self.ar_text_embedding(x)
+        x = x + self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_position(x)
+        x_mask = make_pad_mask(x_lens)
+
+        y_mask = make_pad_mask(y_lens)
+        y_mask_int = y_mask.type(torch.int64)
+        codes = y.type(torch.int64) * (1 - y_mask_int)
+
+        # Training
+        # AR Decoder
+        y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS)
+        x_len = x_lens.max()
+        y_len = y_lens.max()
+        y_emb = self.ar_audio_embedding(y)
+        y_pos = self.ar_audio_position(y_emb)
+
+        xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
+        ar_xy_padding_mask = xy_padding_mask
+
+        x_attn_mask = F.pad(
+            torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),
+            (0, y_len),
+            value=True,
+        )
+        y_attn_mask = F.pad(
+            torch.triu(
+                torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
+                diagonal=1,
+            ),
+            (x_len, 0),
+            value=False,
+        )
+        xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)
+        bsz, src_len = x.shape[0], x_len + y_len
+        _xy_padding_mask = (
+            ar_xy_padding_mask.view(bsz, 1, 1, src_len)
+            .expand(-1, self.num_head, -1, -1)
+            .reshape(bsz * self.num_head, 1, src_len)
+        )
+        xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
+        new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
+        new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
+        xy_attn_mask = new_attn_mask
+        # x 和完整的 y 一次性输入模型
+        xy_pos = torch.concat([x, y_pos], dim=1)
+        xy_dec, _ = self.h(
+            (xy_pos, None),
+            mask=xy_attn_mask,
+        )
+        logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1)
+        # loss
+        # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
+        loss = F.cross_entropy(logits, targets, reduction="sum")
+        acc = self.ar_accuracy_metric(logits.detach(), targets).item()
+        return loss, acc
+
+    # 需要看下这个函数和 forward 的区别以及没有 semantic 的时候 prompts 输入什么
+    def infer(
+        self,
+        x,
+        x_lens,
+        prompts,
+        bert_feature,
+        top_k: int = -100,
+        early_stop_num: int = -1,
+        temperature: float = 1.0,
+    ):
+        x = self.ar_text_embedding(x)
+        x = x + self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_position(x)
+
+        # AR Decoder
+        y = prompts
+        prefix_len = y.shape[1]
+        x_len = x.shape[1]
+        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
+        stop = False
+        for _ in tqdm(range(1500)):
+            y_emb = self.ar_audio_embedding(y)
+            y_pos = self.ar_audio_position(y_emb)
+            # x 和逐渐增长的 y 一起输入给模型
+            xy_pos = torch.concat([x, y_pos], dim=1)
+            y_len = y.shape[1]
+            x_attn_mask_pad = F.pad(
+                x_attn_mask,
+                (0, y_len),
+                value=True,
+            )
+            y_attn_mask = F.pad(
+                torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+                (x_len, 0),
+                value=False,
+            )
+            xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
+                y.device
+            )
+
+            xy_dec, _ = self.h(
+                (xy_pos, None),
+                mask=xy_attn_mask,
+            )
+            logits = self.ar_predict_layer(xy_dec[:, -1])
+            samples = topk_sampling(
+                logits, top_k=top_k, top_p=1.0, temperature=temperature
+            )
+
+            if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+                print("use early stop num:", early_stop_num)
+                stop = True
+
+            if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
+                # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
+                stop = True
+            if stop:
+                if prompts.shape[1] == y.shape[1]:
+                    y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+                    print("bad zero prediction")
+                print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
+                break
+            # 本次生成的 semantic_ids 和之前的 y 构成新的 y
+            # print(samples.shape)#[1,1]#第一个1是bs
+            # import os
+            # os._exit(2333)
+            y = torch.concat([y, samples], dim=1)
+        return y
+
+    def pad_y_eos(self, y, y_mask_int, eos_id):
+        targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(
+            y_mask_int, (0, 1), value=1
+        )
+        # 错位
+        return targets[:, :-1], targets[:, 1:]
+
+    def infer_panel(
+        self,
+        x,  #####全部文本token
+        x_lens,
+        prompts,  ####参考音频token
+        bert_feature,
+        top_k: int = -100,
+        top_p: int = 100,
+        early_stop_num: int = -1,
+        temperature: float = 1.0,
+    ):
+        x = self.ar_text_embedding(x)
+        x = x + self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_position(x)
+
+        # AR Decoder
+        y = prompts
+        
+        x_len = x.shape[1]
+        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
+        stop = False
+        # print(1111111,self.num_layers)
+        cache = {
+            "all_stage": self.num_layers,
+            "k": [None] * self.num_layers,  ###根据配置自己手写
+            "v": [None] * self.num_layers,
+            # "xy_pos":None,##y_pos位置编码每次都不一样的没法缓存，每次都要重新拼xy_pos.主要还是写法原因，其实是可以历史统一一样的，但也没啥计算量就不管了
+            "y_emb": None,  ##只需要对最新的samples求emb，再拼历史的就行
+            # "logits":None,###原版就已经只对结尾求再拼接了，不用管
+            # "xy_dec":None,###不需要，本来只需要最后一个做logits
+            "first_infer": 1,
+            "stage": 0,
+        }
+        ###################  first step ##########################
+        if y is not None:
+            y_emb = self.ar_audio_embedding(y)
+            y_len = y_emb.shape[1]
+            prefix_len = y.shape[1]
+            y_pos = self.ar_audio_position(y_emb)
+            xy_pos = torch.concat([x, y_pos], dim=1)
+            cache["y_emb"] = y_emb
+            ref_free = False
+        else:
+            y_emb = None
+            y_len = 0
+            prefix_len = 0
+            y_pos = None
+            xy_pos = x
+            y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
+            ref_free = True
+
+        x_attn_mask_pad = F.pad(
+                    x_attn_mask,
+                    (0, y_len),  ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
+                    value=True,
+                )
+        y_attn_mask = F.pad(  ###yy的右上1扩展到左边xy的0,(y,x+y)
+            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+            (x_len, 0),
+            value=False,
+        )
+        xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
+            x.device
+        )
+        
+        y_list = [None]*y.shape[0]
+        batch_idx_map = list(range(y.shape[0]))
+        idx_list = [None]*y.shape[0]
+        for idx in tqdm(range(1500)):
+            
+            xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
+            logits = self.ar_predict_layer(
+                xy_dec[:, -1]
+            )  ##不用改，如果用了cache的默认就是只有一帧，取最后一帧一样的
+            # samples = topk_sampling(logits, top_k=top_k, top_p=1.0, temperature=temperature)
+            if(idx==0):###第一次跑不能EOS否则没有了
+                logits = logits[:, :-1]  ###刨除1024终止符号的概率
+            samples = sample(
+                logits, y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
+            )[0]
+            # 本次生成的 semantic_ids 和之前的 y 构成新的 y
+            # print(samples.shape)#[1,1]#第一个1是bs
+            y = torch.concat([y, samples], dim=1) 
+
+            # 移除已经生成完毕的序列
+            reserved_idx_of_batch_for_y = None
+            if (self.EOS in torch.argmax(logits, dim=-1)) or \
+                (self.EOS in samples[:, 0]):  ###如果生成到EOS，则停止
+                    l = samples[:, 0]==self.EOS
+                    removed_idx_of_batch_for_y = torch.where(l==True)[0].tolist()
+                    reserved_idx_of_batch_for_y = torch.where(l==False)[0]
+                    # batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y]
+                    for i in removed_idx_of_batch_for_y:
+                        batch_index = batch_idx_map[i]
+                        idx_list[batch_index] = idx - 1
+                        y_list[batch_index] = y[i, :-1]
+                
+                    batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()]
+                
+            # 只保留未生成完毕的序列 
+            if reserved_idx_of_batch_for_y is not None:
+                # index = torch.LongTensor(batch_idx_map).to(y.device)
+                y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y)
+                if cache["y_emb"] is not None:
+                    cache["y_emb"] = torch.index_select(cache["y_emb"], dim=0, index=reserved_idx_of_batch_for_y)
+                if cache["k"] is not None:
+                    for i in range(self.num_layers):
+                        # 因为kv转置了，所以batch dim是1
+                        cache["k"][i] = torch.index_select(cache["k"][i], dim=1, index=reserved_idx_of_batch_for_y)
+                        cache["v"][i] = torch.index_select(cache["v"][i], dim=1, index=reserved_idx_of_batch_for_y)
+                
+                
+            if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+                print("use early stop num:", early_stop_num)
+                stop = True
+                
+            if not (None in idx_list):
+                # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
+                stop = True
+            if stop:
+                # if prompts.shape[1] == y.shape[1]:
+                #     y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+                #     print("bad zero prediction")
+                if y.shape[1]==0:
+                    y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+                    print("bad zero prediction")
+                print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
+                break
+            
+            ####################### update next step ###################################
+            cache["first_infer"] = 0
+            if cache["y_emb"] is not None:
+                y_emb = torch.cat(
+                    [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], dim = 1
+                )
+                cache["y_emb"] = y_emb
+                y_pos = self.ar_audio_position(y_emb)
+                xy_pos = y_pos[:, -1:]
+            else:
+                y_emb = self.ar_audio_embedding(y[:, -1:])
+                cache["y_emb"] = y_emb
+                y_pos = self.ar_audio_position(y_emb)
+                xy_pos = y_pos
+            y_len = y_pos.shape[1]
+
+            ###最右边一列（是错的）
+            # xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device)
+            # xy_attn_mask[:,-1]=False
+            ###最下面一行（是对的）
+            xy_attn_mask = torch.zeros(
+                (1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
+            )
+            
+        if (None in idx_list):
+            for i in range(x.shape[0]):
+                if idx_list[i] is None:
+                    idx_list[i] = 1500-1  ###如果没有生成到EOS，就用最大长度代替
+                    
+        if ref_free:
+            return y_list, [0]*x.shape[0]
+        return y_list, idx_list

GPT_SoVITS/AR/models/t2s_model_onnx.py

@@ -1,13 +1,17 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
 # reference: https://github.com/lifeiteng/vall-e
 import torch
+from tqdm import tqdm
+
+from AR.modules.embedding_onnx import SinePositionalEmbedding
+from AR.modules.embedding_onnx import TokenEmbedding
+from AR.modules.transformer_onnx import LayerNorm
+from AR.modules.transformer_onnx import TransformerEncoder
+from AR.modules.transformer_onnx import TransformerEncoderLayer
 from torch import nn
 from torch.nn import functional as F
 from torchmetrics.classification import MulticlassAccuracy
 
-from AR.modules.embedding_onnx import SinePositionalEmbedding, TokenEmbedding
-from AR.modules.transformer_onnx import LayerNorm, TransformerEncoder, TransformerEncoderLayer
-
 default_config = {
     "embedding_dim": 512,
     "hidden_dim": 512,
@@ -22,13 +26,12 @@ default_config = {
 
 inf_tensor_value = torch.FloatTensor([-float("Inf")]).float()
 
-
 def logits_to_probs(
     logits,
-    previous_tokens=None,
+    previous_tokens = None,
     temperature: float = 1.0,
-    top_k=None,
-    top_p=None,
+    top_k = None,
+    top_p = None,
     repetition_penalty: float = 1.0,
 ):
     previous_tokens = previous_tokens.squeeze()
@@ -36,27 +39,19 @@ def logits_to_probs(
         previous_tokens = previous_tokens.long()
         score = torch.gather(logits, dim=0, index=previous_tokens)
         score = torch.where(
-            score < 0,
-            score * repetition_penalty,
-            score / repetition_penalty,
+            score < 0, score * repetition_penalty, score / repetition_penalty
         )
         logits.scatter_(dim=0, index=previous_tokens, src=score)
 
     if top_p is not None and top_p < 1.0:
         sorted_logits, sorted_indices = torch.sort(logits, descending=True)
         cum_probs = torch.cumsum(
-            torch.nn.functional.softmax(
-                sorted_logits,
-                dim=-1,
-            ),
-            dim=-1,
+            torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
         )
         sorted_indices_to_remove = cum_probs > top_p
         sorted_indices_to_remove[0] = False  # keep at least one option
         indices_to_remove = sorted_indices_to_remove.scatter(
-            dim=0,
-            index=sorted_indices,
-            src=sorted_indices_to_remove,
+            dim=0, index=sorted_indices, src=sorted_indices_to_remove
         )
         logits = logits.masked_fill(indices_to_remove, -float("Inf"))
 
@@ -72,7 +67,7 @@ def logits_to_probs(
 
 
 def multinomial_sample_one_no_sync(
-    probs_sort,
+    probs_sort
 ):  # Does multinomial sampling without a cuda synchronization
     q = torch.randn_like(probs_sort)
     return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
@@ -84,9 +79,7 @@ def sample(
     **sampling_kwargs,
 ):
     probs = logits_to_probs(
-        logits=logits,
-        previous_tokens=previous_tokens,
-        **sampling_kwargs,
+        logits=logits, previous_tokens=previous_tokens, **sampling_kwargs
     )
     idx_next = multinomial_sample_one_no_sync(probs)
     return idx_next, probs
@@ -98,7 +91,7 @@ class OnnxEncoder(nn.Module):
         self.ar_text_embedding = ar_text_embedding
         self.bert_proj = bert_proj
         self.ar_text_position = ar_text_position
-
+    
     def forward(self, x, bert_feature):
         x = self.ar_text_embedding(x)
         x = x + self.bert_proj(bert_feature.transpose(1, 2))
@@ -106,18 +99,8 @@ class OnnxEncoder(nn.Module):
 
 
 class T2SFirstStageDecoder(nn.Module):
-    def __init__(
-        self,
-        ar_audio_embedding,
-        ar_audio_position,
-        h,
-        ar_predict_layer,
-        loss_fct,
-        ar_accuracy_metric,
-        top_k,
-        early_stop_num,
-        num_layers,
-    ):
+    def __init__(self, ar_audio_embedding, ar_audio_position, h, ar_predict_layer, loss_fct, ar_accuracy_metric,
+    top_k, early_stop_num, num_layers):
         super().__init__()
         self.ar_audio_embedding = ar_audio_embedding
         self.ar_audio_position = ar_audio_position
@@ -128,11 +111,11 @@ class T2SFirstStageDecoder(nn.Module):
         self.top_k = top_k
         self.early_stop_num = early_stop_num
         self.num_layers = num_layers
-
+    
     def forward(self, x, prompt):
         y = prompt
-        x_example = x[:, :, 0] * 0.0
-        # N, 1, 512
+        x_example = x[:,:,0] * 0.0
+        #N, 1, 512
         cache = {
             "all_stage": self.num_layers,
             "k": None,
@@ -149,15 +132,11 @@ class T2SFirstStageDecoder(nn.Module):
 
         xy_pos = torch.concat([x, y_pos], dim=1)
 
-        y_example = y_pos[:, :, 0] * 0.0
-        x_attn_mask = torch.matmul(x_example.transpose(0, 1), x_example).bool()
+        y_example = y_pos[:,:,0] * 0.0
+        x_attn_mask = torch.matmul(x_example.transpose(0, 1) , x_example).bool()
         y_attn_mask = torch.ones_like(torch.matmul(y_example.transpose(0, 1), y_example), dtype=torch.int64)
         y_attn_mask = torch.cumsum(y_attn_mask, dim=1) - torch.cumsum(
-            torch.ones_like(
-                y_example.transpose(0, 1),
-                dtype=torch.int64,
-            ),
-            dim=0,
+            torch.ones_like(y_example.transpose(0, 1), dtype=torch.int64), dim=0
         )
         y_attn_mask = y_attn_mask > 0
 
@@ -166,16 +145,10 @@ class T2SFirstStageDecoder(nn.Module):
         x_attn_mask_pad = torch.cat([x_attn_mask, torch.ones_like(x_y_pad)], dim=1)
         y_attn_mask = torch.cat([y_x_pad, y_attn_mask], dim=1)
         xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)
-        cache["k"] = (
-            torch.matmul(x_attn_mask_pad[0].float().unsqueeze(-1), torch.zeros((1, 512)))
-            .unsqueeze(1)
-            .repeat(self.num_layers, 1, 1, 1)
-        )
-        cache["v"] = (
-            torch.matmul(x_attn_mask_pad[0].float().unsqueeze(-1), torch.zeros((1, 512)))
-            .unsqueeze(1)
-            .repeat(self.num_layers, 1, 1, 1)
-        )
+        cache["k"] = torch.matmul(x_attn_mask_pad[0].float().unsqueeze(-1), torch.zeros((1, 512)))\
+        .unsqueeze(1).repeat(self.num_layers, 1, 1, 1)
+        cache["v"] = torch.matmul(x_attn_mask_pad[0].float().unsqueeze(-1), torch.zeros((1, 512)))\
+        .unsqueeze(1).repeat(self.num_layers, 1, 1, 1)
 
         xy_dec = self.h(xy_pos, mask=xy_attn_mask, cache=cache)
         logits = self.ar_predict_layer(xy_dec[:, -1])
@@ -187,18 +160,8 @@ class T2SFirstStageDecoder(nn.Module):
 
 
 class T2SStageDecoder(nn.Module):
-    def __init__(
-        self,
-        ar_audio_embedding,
-        ar_audio_position,
-        h,
-        ar_predict_layer,
-        loss_fct,
-        ar_accuracy_metric,
-        top_k,
-        early_stop_num,
-        num_layers,
-    ):
+    def __init__(self, ar_audio_embedding, ar_audio_position, h, ar_predict_layer, loss_fct, ar_accuracy_metric,
+    top_k, early_stop_num, num_layers):
         super().__init__()
         self.ar_audio_embedding = ar_audio_embedding
         self.ar_audio_position = ar_audio_position
@@ -221,18 +184,14 @@ class T2SStageDecoder(nn.Module):
         }
 
         y_emb = torch.cat(
-            [
-                cache["y_emb"],
-                self.ar_audio_embedding(y[:, -1:]),
-            ],
-            1,
+            [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1
         )
         cache["y_emb"] = y_emb
         y_pos = self.ar_audio_position(y_emb)
 
         xy_pos = y_pos[:, -1:]
-
-        y_example = y_pos[:, :, 0] * 0.0
+        
+        y_example = y_pos[:,:,0] * 0.0
 
         xy_attn_mask = torch.cat([x_example, y_example], dim=1)
         xy_attn_mask = torch.zeros_like(xy_attn_mask, dtype=torch.bool)
@@ -291,28 +250,12 @@ class Text2SemanticDecoder(nn.Module):
 
     def init_onnx(self):
         self.onnx_encoder = OnnxEncoder(self.ar_text_embedding, self.bert_proj, self.ar_text_position)
-        self.first_stage_decoder = T2SFirstStageDecoder(
-            self.ar_audio_embedding,
-            self.ar_audio_position,
-            self.h,
-            self.ar_predict_layer,
-            self.loss_fct,
-            self.ar_accuracy_metric,
-            self.top_k,
-            self.early_stop_num,
-            self.num_layers,
-        )
-        self.stage_decoder = T2SStageDecoder(
-            self.ar_audio_embedding,
-            self.ar_audio_position,
-            self.h,
-            self.ar_predict_layer,
-            self.loss_fct,
-            self.ar_accuracy_metric,
-            self.top_k,
-            self.early_stop_num,
-            self.num_layers,
-        )
+        self.first_stage_decoder = T2SFirstStageDecoder(self.ar_audio_embedding, self.ar_audio_position, self.h, 
+            self.ar_predict_layer, self.loss_fct, self.ar_accuracy_metric, self.top_k, self.early_stop_num,
+            self.num_layers)
+        self.stage_decoder = T2SStageDecoder(self.ar_audio_embedding, self.ar_audio_position, self.h, 
+            self.ar_predict_layer, self.loss_fct, self.ar_accuracy_metric, self.top_k, self.early_stop_num,
+            self.num_layers)
 
     def forward(self, x, prompts, bert_feature):
         early_stop_num = self.early_stop_num
@@ -343,7 +286,7 @@ class Text2SemanticDecoder(nn.Module):
         y = prompts
         prefix_len = y.shape[1]
         x_len = x.shape[1]
-        x_example = x[:, :, 0] * 0.0
+        x_example = x[:,:,0] * 0.0
         x_attn_mask = torch.matmul(x_example.transpose(0, 1), x_example)
         x_attn_mask = torch.zeros_like(x_attn_mask, dtype=torch.bool)
 
@@ -360,7 +303,9 @@ class Text2SemanticDecoder(nn.Module):
             if cache["first_infer"] == 1:
                 y_emb = self.ar_audio_embedding(y)
             else:
-                y_emb = torch.cat([cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1)
+                y_emb = torch.cat(
+                    [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1
+                )
             cache["y_emb"] = y_emb
             y_pos = self.ar_audio_position(y_emb)
             if cache["first_infer"] == 1:
@@ -372,8 +317,7 @@ class Text2SemanticDecoder(nn.Module):
                 x_attn_mask_pad = F.pad(x_attn_mask, (0, y_len), value=True)
                 y_attn_mask = F.pad(
                     torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
-                    (x_len, 0),
-                    value=False,
+                    (x_len, 0), value=False
                 )
                 xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)
             else:
@@ -391,4 +335,4 @@ class Text2SemanticDecoder(nn.Module):
                 break
             y = torch.concat([y, samples], dim=1)
             cache["first_infer"] = 0
-        return y, idx
+        return y, idx

GPT_SoVITS/AR/models/utils.py

@@ -1,10 +1,8 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/utils.py
 # reference: https://github.com/lifeiteng/vall-e
-from typing import Tuple
-
 import torch
 import torch.nn.functional as F
-
+from typing import Tuple
 
 def sequence_mask(length, max_length=None):
     if max_length is None:
@@ -41,46 +39,9 @@ def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
     return expaned_lengths >= lengths.unsqueeze(-1)
 
 
-def make_pad_mask_left(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
-    """
-    Args:
-      lengths:
-        A 1-D tensor containing sentence lengths.
-      max_len:
-        The length of masks.
-    Returns:
-      Return a 2-D bool tensor, where masked positions
-      are filled with `True` and non-masked positions are
-      filled with `False`.
-
-    #>>> lengths = torch.tensor([1, 3, 2, 5])
-    #>>> make_pad_mask(lengths)
-    tensor(
-        [
-            [True,  True,  False],
-            [True, False, False],
-            [True,  True,  False],
-            ...
-        ]
-    )
-    """
-    assert lengths.ndim == 1, lengths.ndim
-    max_len = max(max_len, lengths.max())
-    n = lengths.size(0)
-    seq_range = torch.arange(0, max_len, device=lengths.device)
-    expaned_lengths = seq_range.unsqueeze(0).repeat(n, 1)
-    expaned_lengths -= (max_len - lengths).unsqueeze(-1)
-
-    return expaned_lengths < 0
-
-
 # https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py
 def top_k_top_p_filtering(
-    logits,
-    top_k=0,
-    top_p=1.0,
-    filter_value=-float("Inf"),
-    min_tokens_to_keep=1,
+    logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1
 ):
     """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
     Args:
@@ -111,7 +72,9 @@ def top_k_top_p_filtering(
         sorted_indices_to_remove[..., 0] = 0
 
         # scatter sorted tensors to original indexing
-        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+        indices_to_remove = sorted_indices_to_remove.scatter(
+            1, sorted_indices, sorted_indices_to_remove
+        )
         logits[indices_to_remove] = filter_value
     return logits
 
@@ -134,7 +97,7 @@ def topk_sampling(logits, top_k=10, top_p=1.0, temperature=1.0):
     return token
 
 
-from typing import Optional
+from typing import Optional, Tuple
 
 
 def multinomial_sample_one_no_sync(
@@ -160,21 +123,19 @@ def logits_to_probs(
         previous_tokens = previous_tokens.long()
         score = torch.gather(logits, dim=1, index=previous_tokens)
         score = torch.where(
-            score < 0,
-            score * repetition_penalty,
-            score / repetition_penalty,
+            score < 0, score * repetition_penalty, score / repetition_penalty
         )
         logits.scatter_(dim=1, index=previous_tokens, src=score)
 
     if top_p is not None and top_p < 1.0:
         sorted_logits, sorted_indices = torch.sort(logits, descending=True)
-        cum_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
+        cum_probs = torch.cumsum(
+            torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
+        )
         sorted_indices_to_remove = cum_probs > top_p
         sorted_indices_to_remove[:, 0] = False  # keep at least one option
         indices_to_remove = sorted_indices_to_remove.scatter(
-            dim=1,
-            index=sorted_indices,
-            src=sorted_indices_to_remove,
+            dim=1, index=sorted_indices, src=sorted_indices_to_remove
         )
         logits = logits.masked_fill(indices_to_remove, -float("Inf"))
 
@@ -182,7 +143,7 @@ def logits_to_probs(
 
     if top_k is not None:
         v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
-        pivot = v[:, -1].unsqueeze(-1)
+        pivot = v[: , -1].unsqueeze(-1)
         logits = torch.where(logits < pivot, -float("Inf"), logits)
 
     probs = torch.nn.functional.softmax(logits, dim=-1)
@@ -194,19 +155,18 @@ def sample(
     previous_tokens: Optional[torch.Tensor] = None,
     **sampling_kwargs,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
-    probs = logits_to_probs(logits=logits, previous_tokens=previous_tokens, **sampling_kwargs)
+    probs = logits_to_probs(
+        logits=logits, previous_tokens=previous_tokens, **sampling_kwargs
+    )
     idx_next = multinomial_sample_one_no_sync(probs)
     return idx_next, probs
 
-
-def dpo_loss(
-    policy_chosen_logps: torch.FloatTensor,
-    policy_rejected_logps: torch.FloatTensor,
-    reference_chosen_logps: torch.FloatTensor,
-    reference_rejected_logps: torch.FloatTensor,
-    beta: float,
-    reference_free: bool = False,
-) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+def dpo_loss(policy_chosen_logps: torch.FloatTensor,
+             policy_rejected_logps: torch.FloatTensor,
+             reference_chosen_logps: torch.FloatTensor,
+             reference_rejected_logps: torch.FloatTensor,
+             beta: float,
+             reference_free: bool = False) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
     pi_logratios = policy_chosen_logps - policy_rejected_logps
     ref_logratios = reference_chosen_logps - reference_rejected_logps
 
@@ -221,53 +181,40 @@ def dpo_loss(
 
     return losses.mean(), chosen_rewards, rejected_rewards
 
+def get_batch_logps(logits_target: torch.FloatTensor, logits_reject: torch.FloatTensor, labels_target: torch.LongTensor, labels_reject: torch.LongTensor, average_log_prob: bool = False) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
 
-def get_batch_logps(
-    logits_target: torch.FloatTensor,
-    logits_reject: torch.FloatTensor,
-    labels_target: torch.LongTensor,
-    labels_reject: torch.LongTensor,
-    average_log_prob: bool = False,
-) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
     # dummy token; we'll ignore the losses on these tokens later
 
-    per_token_logps_target = torch.gather(
-        logits_target.log_softmax(-1), dim=2, index=labels_target.unsqueeze(2)
-    ).squeeze(2)
-    per_token_logps_reject = torch.gather(
-        logits_reject.log_softmax(-1), dim=2, index=labels_reject.unsqueeze(2)
-    ).squeeze(2)
+    per_token_logps_target = torch.gather(logits_target.log_softmax(-1), dim=2, index=labels_target.unsqueeze(2)).squeeze(2)
+    per_token_logps_reject = torch.gather(logits_reject.log_softmax(-1), dim=2, index=labels_reject.unsqueeze(2)).squeeze(2)
 
     return per_token_logps_target.sum(-1), per_token_logps_reject.sum(-1)
 
-
 def make_reject_y(y_o, y_lens):
     def repeat_P(y):
         range_idx, _ = torch.randint(0, len(y), size=(2,)).sort()
-        pre = y[: range_idx[0]]
-        shf = y[range_idx[1] :]
-        range_text = y[range_idx[0] : range_idx[1]]
+        pre = y[:range_idx[0]]
+        shf = y[range_idx[1]:]
+        range_text = y[range_idx[0]:range_idx[1]]
         new_y = torch.cat([pre, range_text, range_text, shf])
         return new_y
-
     def lost_P(y):
         range_idx, _ = torch.randint(0, len(y), size=(2,)).sort()
-        pre = y[: range_idx[0]]
-        shf = y[range_idx[1] :]
-        range_text = y[range_idx[0] : range_idx[1]]
+        pre = y[:range_idx[0]]
+        shf = y[range_idx[1]:]
+        range_text = y[range_idx[0]:range_idx[1]]
         new_y = torch.cat([pre, shf])
         return new_y
-
     bs = len(y_lens)
     reject_y = []
     reject_y_lens = []
     for b in range(bs):
-        process_item_idx = torch.randint(0, 1, size=(1,))[0]
+        process_item_idx = torch.randint(0, 1, size=(1, ))[0]
         if process_item_idx == 0:
             new_y = repeat_P(y_o[b])
             reject_y.append(new_y)
             reject_y_lens.append(len(new_y))
-        elif process_item_idx == 1:
+        elif process_item_idx==1:
             new_y = lost_P(y_o[b])
             reject_y.append(new_y)
             reject_y_lens.append(len(new_y))
@@ -276,7 +223,7 @@ def make_reject_y(y_o, y_lens):
         pad_length = max_length - reject_y_lens[b]
         reject_y[b] = torch.cat([reject_y[b], torch.zeros(pad_length, dtype=y_o.dtype, device=y_o.device)], dim=0)
 
-    reject_y = torch.stack(reject_y, dim=0)
+    reject_y = torch.stack(reject_y, dim = 0)
     reject_y_lens = torch.tensor(reject_y_lens, device=y_lens.device)
 
     return reject_y, reject_y_lens

GPT_SoVITS/AR/modules/activation.py

@@ -1,14 +1,17 @@
 # modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/activation.py
-from typing import Optional, Tuple
-
+from typing import Optional
+from typing import Tuple
 import torch
 from torch import Tensor
-from torch.nn import Linear, Module
-from torch.nn import functional as F
-from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
+from torch.nn import Linear
+from torch.nn import Module
+from torch.nn.init import constant_
+from torch.nn.init import xavier_normal_
+from torch.nn.init import xavier_uniform_
 from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
 from torch.nn.parameter import Parameter
 
+from torch.nn import functional as F
 from AR.modules.patched_mha_with_cache import multi_head_attention_forward_patched
 
 F.multi_head_attention_forward = multi_head_attention_forward_patched
@@ -70,7 +73,6 @@ class MultiheadAttention(Module):
         >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
 
     """
-
     __constants__ = ["batch_first"]
     bias_k: Optional[torch.Tensor]
     bias_v: Optional[torch.Tensor]
@@ -102,7 +104,9 @@ class MultiheadAttention(Module):
         self.dropout = dropout
         self.batch_first = batch_first
         self.head_dim = embed_dim // num_heads
-        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        assert (
+            self.head_dim * num_heads == self.embed_dim
+        ), "embed_dim must be divisible by num_heads"
 
         if add_bias_kv:
             self.bias_k = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
@@ -113,32 +117,31 @@ class MultiheadAttention(Module):
         if linear1_cls == Linear:
             if not self._qkv_same_embed_dim:
                 self.q_proj_weight = Parameter(
-                    torch.empty((embed_dim, embed_dim), **factory_kwargs),
+                    torch.empty((embed_dim, embed_dim), **factory_kwargs)
                 )
                 self.k_proj_weight = Parameter(
-                    torch.empty((embed_dim, self.kdim), **factory_kwargs),
+                    torch.empty((embed_dim, self.kdim), **factory_kwargs)
                 )
                 self.v_proj_weight = Parameter(
-                    torch.empty((embed_dim, self.vdim), **factory_kwargs),
+                    torch.empty((embed_dim, self.vdim), **factory_kwargs)
                 )
                 self.register_parameter("in_proj_weight", None)
             else:
                 self.in_proj_weight = Parameter(
-                    torch.empty((3 * embed_dim, embed_dim), **factory_kwargs),
+                    torch.empty((3 * embed_dim, embed_dim), **factory_kwargs)
                 )
                 self.register_parameter("q_proj_weight", None)
                 self.register_parameter("k_proj_weight", None)
                 self.register_parameter("v_proj_weight", None)
 
             if bias:
-                self.in_proj_bias = Parameter(torch.empty(3 * embed_dim, **factory_kwargs))
+                self.in_proj_bias = Parameter(
+                    torch.empty(3 * embed_dim, **factory_kwargs)
+                )
             else:
                 self.register_parameter("in_proj_bias", None)
             self.out_proj = NonDynamicallyQuantizableLinear(
-                embed_dim,
-                embed_dim,
-                bias=bias,
-                **factory_kwargs,
+                embed_dim, embed_dim, bias=bias, **factory_kwargs
             )
 
             self._reset_parameters()
@@ -147,10 +150,7 @@ class MultiheadAttention(Module):
                 raise NotImplementedError
             else:
                 self.in_proj_linear = linear1_cls(
-                    embed_dim,
-                    3 * embed_dim,
-                    bias=bias,
-                    **factory_kwargs,
+                    embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs
                 )
                 self.in_proj_weight = self.in_proj_linear.weight
 
@@ -164,10 +164,7 @@ class MultiheadAttention(Module):
                     self.register_parameter("in_proj_bias", None)
 
             self.out_proj = linear2_cls(
-                embed_dim,
-                embed_dim,
-                bias=bias,
-                **factory_kwargs,
+                embed_dim, embed_dim, bias=bias, **factory_kwargs
             )
 
             if self.bias_k is not None:
@@ -264,26 +261,28 @@ class MultiheadAttention(Module):
         if key_padding_mask is not None:
             _kpm_dtype = key_padding_mask.dtype
             if _kpm_dtype != torch.bool and not torch.is_floating_point(
-                key_padding_mask,
+                key_padding_mask
             ):
-                raise AssertionError("only bool and floating types of key_padding_mask are supported")
+                raise AssertionError(
+                    "only bool and floating types of key_padding_mask are supported"
+                )
         why_not_fast_path = ""
         if not is_batched:
-            why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}"
+            why_not_fast_path = (
+                f"input not batched; expected query.dim() of 3 but got {query.dim()}"
+            )
         elif query is not key or key is not value:
             # When lifting this restriction, don't forget to either
             # enforce that the dtypes all match or test cases where
             # they don't!
             why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
         elif self.in_proj_bias is not None and query.dtype != self.in_proj_bias.dtype:
-            why_not_fast_path = (
-                f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
-            )
-        elif self.in_proj_weight is not None and query.dtype != self.in_proj_weight.dtype:
+            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
+        elif (
+            self.in_proj_weight is not None and query.dtype != self.in_proj_weight.dtype
+        ):
             # this case will fail anyway, but at least they'll get a useful error message.
-            why_not_fast_path = (
-                f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
-            )
+            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
         elif self.training:
             why_not_fast_path = "training is enabled"
         elif not self.batch_first:
@@ -301,7 +300,9 @@ class MultiheadAttention(Module):
         elif attn_mask is not None:
             why_not_fast_path = "attn_mask was not None"
         elif query.is_nested and key_padding_mask is not None:
-            why_not_fast_path = "key_padding_mask is not supported with NestedTensor input"
+            why_not_fast_path = (
+                "key_padding_mask is not supported with NestedTensor input"
+            )
         elif self.num_heads % 2 == 1:
             why_not_fast_path = "num_heads is odd"
         elif torch.is_autocast_enabled():
@@ -321,10 +322,20 @@ class MultiheadAttention(Module):
             # generator expressions.
             if torch.overrides.has_torch_function(tensor_args):
                 why_not_fast_path = "some Tensor argument has_torch_function"
-            elif not all([(x is None or x.is_cuda or "cpu" in str(x.device)) for x in tensor_args]):
+            elif not all(
+                [
+                    (x is None or x.is_cuda or "cpu" in str(x.device))
+                    for x in tensor_args
+                ]
+            ):
                 why_not_fast_path = "some Tensor argument is neither CUDA nor CPU"
-            elif torch.is_grad_enabled() and any([x is not None and x.requires_grad for x in tensor_args]):
-                why_not_fast_path = "grad is enabled and at least one of query or the input/output projection weights or biases requires_grad"
+            elif torch.is_grad_enabled() and any(
+                [x is not None and x.requires_grad for x in tensor_args]
+            ):
+                why_not_fast_path = (
+                    "grad is enabled and at least one of query or the "
+                    "input/output projection weights or biases requires_grad"
+                )
             if not why_not_fast_path:
                 return torch._native_multi_head_attention(
                     query,
@@ -339,7 +350,11 @@ class MultiheadAttention(Module):
                     key_padding_mask if key_padding_mask is not None else attn_mask,
                     need_weights,
                     average_attn_weights,
-                    1 if key_padding_mask is not None else 0 if attn_mask is not None else None,
+                    1
+                    if key_padding_mask is not None
+                    else 0
+                    if attn_mask is not None
+                    else None,
                 )
 
         any_nested = query.is_nested or key.is_nested or value.is_nested

GPT_SoVITS/AR/modules/activation_onnx.py

@@ -1,13 +1,17 @@
 # modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/activation.py
-from typing import Optional, Tuple
-
+from typing import Optional
+from typing import Tuple
 import torch
 from torch import Tensor
-from torch.nn import Linear, Module
-from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
+from torch.nn import Linear
+from torch.nn import Module
+from torch.nn.init import constant_
+from torch.nn.init import xavier_normal_
+from torch.nn.init import xavier_uniform_
 from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
 from torch.nn.parameter import Parameter
 
+from torch.nn import functional as F
 from AR.modules.patched_mha_with_cache_onnx import multi_head_attention_forward_patched
 
 
@@ -43,7 +47,9 @@ class MultiheadAttention(Module):
         self.dropout = dropout
         self.batch_first = batch_first
         self.head_dim = embed_dim // num_heads
-        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        assert (
+            self.head_dim * num_heads == self.embed_dim
+        ), "embed_dim must be divisible by num_heads"
 
         if add_bias_kv:
             self.bias_k = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
@@ -54,30 +60,18 @@ class MultiheadAttention(Module):
         if linear1_cls == Linear:
             if not self._qkv_same_embed_dim:
                 self.q_proj_weight = Parameter(
-                    torch.empty(
-                        (embed_dim, embed_dim),
-                        **factory_kwargs,
-                    )
+                    torch.empty((embed_dim, embed_dim), **factory_kwargs)
                 )
                 self.k_proj_weight = Parameter(
-                    torch.empty(
-                        (embed_dim, self.kdim),
-                        **factory_kwargs,
-                    )
+                    torch.empty((embed_dim, self.kdim), **factory_kwargs)
                 )
                 self.v_proj_weight = Parameter(
-                    torch.empty(
-                        (embed_dim, self.vdim),
-                        **factory_kwargs,
-                    )
+                    torch.empty((embed_dim, self.vdim), **factory_kwargs)
                 )
                 self.register_parameter("in_proj_weight", None)
             else:
                 self.in_proj_weight = Parameter(
-                    torch.empty(
-                        (3 * embed_dim, embed_dim),
-                        **factory_kwargs,
-                    )
+                    torch.empty((3 * embed_dim, embed_dim), **factory_kwargs)
                 )
                 self.register_parameter("q_proj_weight", None)
                 self.register_parameter("k_proj_weight", None)
@@ -85,11 +79,13 @@ class MultiheadAttention(Module):
 
             if bias:
                 self.in_proj_bias = Parameter(
-                    torch.empty(3 * embed_dim, **factory_kwargs),
+                    torch.empty(3 * embed_dim, **factory_kwargs)
                 )
             else:
                 self.register_parameter("in_proj_bias", None)
-            self.out_proj = NonDynamicallyQuantizableLinear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
+            self.out_proj = NonDynamicallyQuantizableLinear(
+                embed_dim, embed_dim, bias=bias, **factory_kwargs
+            )
 
             self._reset_parameters()
         else:
@@ -97,10 +93,7 @@ class MultiheadAttention(Module):
                 raise NotImplementedError
             else:
                 self.in_proj_linear = linear1_cls(
-                    embed_dim,
-                    3 * embed_dim,
-                    bias=bias,
-                    **factory_kwargs,
+                    embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs
                 )
                 self.in_proj_weight = self.in_proj_linear.weight
 
@@ -114,10 +107,7 @@ class MultiheadAttention(Module):
                     self.register_parameter("in_proj_bias", None)
 
             self.out_proj = linear2_cls(
-                embed_dim,
-                embed_dim,
-                bias=bias,
-                **factory_kwargs,
+                embed_dim, embed_dim, bias=bias, **factory_kwargs
             )
 
             if self.bias_k is not None:

GPT_SoVITS/AR/modules/embedding.py

@@ -60,11 +60,14 @@ class SinePositionalEmbedding(nn.Module):
                 return
         pe = torch.zeros(x.size(1), self.embedding_dim)
         if self.reverse:
-            position = torch.arange(x.size(1) - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1)
+            position = torch.arange(
+                x.size(1) - 1, -1, -1.0, dtype=torch.float32
+            ).unsqueeze(1)
         else:
             position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
         div_term = torch.exp(
-            torch.arange(0, self.embedding_dim, 2, dtype=torch.float32) * -(math.log(10000.0) / self.embedding_dim)
+            torch.arange(0, self.embedding_dim, 2, dtype=torch.float32)
+            * -(math.log(10000.0) / self.embedding_dim)
         )
         pe[:, 0::2] = torch.sin(position * div_term)
         pe[:, 1::2] = torch.cos(position * div_term)

GPT_SoVITS/AR/modules/embedding_onnx.py

@@ -50,7 +50,7 @@ class SinePositionalEmbedding(nn.Module):
         self.div_term = torch.exp(torch.arange(0, self.embedding_dim, 2) * -(math.log(10000.0) / self.embedding_dim))
 
     def extend_pe(self, x):
-        position = torch.cumsum(torch.ones_like(x[:, :, 0]), dim=1).transpose(0, 1)
+        position = torch.cumsum(torch.ones_like(x[:,:,0]), dim=1).transpose(0, 1)
         scpe = (position * self.div_term).unsqueeze(0)
         pe = torch.cat([torch.sin(scpe), torch.cos(scpe)]).permute(1, 2, 0)
         pe = pe.contiguous().view(1, -1, self.embedding_dim)

GPT_SoVITS/AR/modules/lr_schedulers.py

@@ -49,9 +49,13 @@ class WarmupCosineLRSchedule(torch.optim.lr_scheduler._LRScheduler):
             lr = self.end_lr
 
         else:
-            decay_ratio = (self._current_step - self.warmup_steps) / (self.total_steps - self.warmup_steps)
+            decay_ratio = (self._current_step - self.warmup_steps) / (
+                self.total_steps - self.warmup_steps
+            )
             if decay_ratio < 0.0 or decay_ratio > 1.0:
-                raise RuntimeError("Decay ratio must be in [0.0, 1.0]. Fix LR scheduler settings.")
+                raise RuntimeError(
+                    "Decay ratio must be in [0.0, 1.0]. Fix LR scheduler settings."
+                )
             coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
             lr = self.end_lr + coeff * (self.peak_lr - self.end_lr)
 
@@ -66,13 +70,7 @@ if __name__ == "__main__":
     m = nn.Linear(10, 10)
     opt = Adam(m.parameters(), lr=1e-4)
     s = WarmupCosineLRSchedule(
-        opt,
-        1e-6,
-        2e-4,
-        1e-6,
-        warmup_steps=2000,
-        total_steps=20000,
-        current_step=0,
+        opt, 1e-6, 2e-4, 1e-6, warmup_steps=2000, total_steps=20000, current_step=0
     )
     lrs = []
     for i in range(25000):

GPT_SoVITS/AR/modules/optim.py

@@ -16,7 +16,8 @@
 import contextlib
 import logging
 from collections import defaultdict
-from typing import List, Tuple
+from typing import List
+from typing import Tuple
 
 import torch
 from torch import Tensor
@@ -70,8 +71,12 @@ class BatchedOptimizer(Optimizer):
           group_params_names: name for each parameter in group,
                 which is List[str].
         """
-        batches = defaultdict(list)  # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter
-        batches_names = defaultdict(list)  # `batches` maps from tuple (dtype_as_str,*shape) to list of str
+        batches = defaultdict(
+            list
+        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter
+        batches_names = defaultdict(
+            list
+        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of str
 
         assert len(param_group) == len(group_params_names)
         for p, named_p in zip(param_group, group_params_names):
@@ -80,8 +85,11 @@ class BatchedOptimizer(Optimizer):
             batches_names[key].append(named_p)
 
         batches_names_keys = list(batches_names.keys())
-        sorted_idx = sorted(range(len(batches_names)), key=lambda i: batches_names_keys[i])
-        batches_names = [batches_names[batches_names_keys[idx]] for idx in sorted_idx]
+        sorted_idx = sorted(
+            range(len(batches_names)), key=lambda i: batches_names_keys[i])
+        batches_names = [
+            batches_names[batches_names_keys[idx]] for idx in sorted_idx
+        ]
         batches = [batches[batches_names_keys[idx]] for idx in sorted_idx]
 
         stacked_params_dict = dict()
@@ -98,14 +106,16 @@ class BatchedOptimizer(Optimizer):
             # group.  class Optimizer will take care of saving/loading state.
             state = self.state[p]
             p_stacked = torch.stack(batch)
-            grad = torch.stack([torch.zeros_like(p) if p.grad is None else p.grad for p in batch])
+            grad = torch.stack([
+                torch.zeros_like(p) if p.grad is None else p.grad for p in batch
+            ])
             p_stacked.grad = grad
             stacked_params_dict[key] = p_stacked
             tuples.append((p_stacked, state, batch_names))
 
         yield tuples  # <-- calling code will do the actual optimization here!
 
-        for (stacked_params, _state, _names), batch in zip(tuples, batches):
+        for ((stacked_params, _state, _names), batch) in zip(tuples, batches):
             for i, p in enumerate(batch):  # batch is list of Parameter
                 p.copy_(stacked_params[i])
 
@@ -154,24 +164,25 @@ class ScaledAdam(BatchedOptimizer):
     """
 
     def __init__(
-        self,
-        params,
-        lr=3e-02,
-        clipping_scale=None,
-        betas=(0.9, 0.98),
-        scalar_lr_scale=0.1,
-        eps=1.0e-08,
-        param_min_rms=1.0e-05,
-        param_max_rms=3.0,
-        scalar_max=10.0,
-        size_update_period=4,
-        clipping_update_period=100,
-        parameters_names=None,
-        show_dominant_parameters=True,
-    ):
+            self,
+            params,
+            lr=3e-02,
+            clipping_scale=None,
+            betas=(0.9, 0.98),
+            scalar_lr_scale=0.1,
+            eps=1.0e-08,
+            param_min_rms=1.0e-05,
+            param_max_rms=3.0,
+            scalar_max=10.0,
+            size_update_period=4,
+            clipping_update_period=100,
+            parameters_names=None,
+            show_dominant_parameters=True, ):
+
         assert parameters_names is not None, (
-            "Please prepare parameters_names,which is a List[List[str]]. Each List[str] is for a groupand each str is for a parameter"
-        )
+            "Please prepare parameters_names,"
+            "which is a List[List[str]]. Each List[str] is for a group"
+            "and each str is for a parameter")
         defaults = dict(
             lr=lr,
             clipping_scale=clipping_scale,
@@ -182,8 +193,7 @@ class ScaledAdam(BatchedOptimizer):
             param_max_rms=param_max_rms,
             scalar_max=scalar_max,
             size_update_period=size_update_period,
-            clipping_update_period=clipping_update_period,
-        )
+            clipping_update_period=clipping_update_period, )
 
         super(ScaledAdam, self).__init__(params, defaults)
         assert len(self.param_groups) == len(parameters_names)
@@ -208,13 +218,18 @@ class ScaledAdam(BatchedOptimizer):
 
         batch = True
 
-        for group, group_params_names in zip(self.param_groups, self.parameters_names):
-            with self.batched_params(group["params"], group_params_names) as batches:
+        for group, group_params_names in zip(self.param_groups,
+                                             self.parameters_names):
+
+            with self.batched_params(group["params"],
+                                     group_params_names) as batches:
+
                 # batches is list of pairs (stacked_param, state).  stacked_param is like
                 # a regular parameter, and will have a .grad, but the 1st dim corresponds to
                 # a stacking dim, it is not a real dim.
 
-                if len(batches[0][1]) == 0:  # if len(first state) == 0: not yet initialized
+                if (len(batches[0][1]) ==
+                        0):  # if len(first state) == 0: not yet initialized
                     clipping_scale = 1
                 else:
                     clipping_scale = self._get_clipping_scale(group, batches)
@@ -224,7 +239,9 @@ class ScaledAdam(BatchedOptimizer):
                     # grad is not going to be None, we handled that when creating the batches.
                     grad = p.grad
                     if grad.is_sparse:
-                        raise RuntimeError("ScaledAdam optimizer does not support sparse gradients")
+                        raise RuntimeError(
+                            "ScaledAdam optimizer does not support sparse gradients"
+                        )
                     # State initialization
                     if len(state) == 0:
                         self._init_state(group, p, state)
@@ -257,7 +274,8 @@ class ScaledAdam(BatchedOptimizer):
         # parameter-change "delta", which combines all forms of
         # update.  this is equivalent to how it's done in Adam,
         # except for the first few steps.
-        state["delta"] = torch.zeros_like(p, memory_format=torch.preserve_format)
+        state["delta"] = torch.zeros_like(
+            p, memory_format=torch.preserve_format)
 
         batch_size = p.shape[0]
         numel = p.numel() // batch_size
@@ -267,16 +285,22 @@ class ScaledAdam(BatchedOptimizer):
             # "param_rms" just periodically records the scalar root-mean-square value of
             # the parameter tensor.
             # it has a shape like (batch_size, 1, 1, 1, 1)
-            param_rms = (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt()
+            param_rms = (
+                (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt())
             state["param_rms"] = param_rms
 
             state["scale_exp_avg_sq"] = torch.zeros_like(param_rms)
-            state["scale_grads"] = torch.zeros(size_update_period, *param_rms.shape, **kwargs)
+            state["scale_grads"] = torch.zeros(size_update_period,
+                                               *param_rms.shape, **kwargs)
 
         # exp_avg_sq is the weighted sum of scaled gradients. as in Adam.
-        state["exp_avg_sq"] = torch.zeros_like(p, memory_format=torch.preserve_format)
+        state["exp_avg_sq"] = torch.zeros_like(
+            p, memory_format=torch.preserve_format)
 
-    def _get_clipping_scale(self, group: dict, tuples: List[Tuple[Tensor, dict, List[str]]]) -> float:
+    def _get_clipping_scale(self,
+                            group: dict,
+                            tuples: List[Tuple[Tensor, dict, List[str]]]
+                            ) -> float:
         """
         Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients
         by this amount before applying the rest of the update.
@@ -301,18 +325,20 @@ class ScaledAdam(BatchedOptimizer):
         clipping_update_period = group["clipping_update_period"]
 
         tot_sumsq = torch.tensor(0.0, device=first_p.device)
-        for p, state, param_names in tuples:
+        for (p, state, param_names) in tuples:
             grad = p.grad
             if grad.is_sparse:
-                raise RuntimeError("ScaledAdam optimizer does not support sparse gradients")
+                raise RuntimeError(
+                    "ScaledAdam optimizer does not support sparse gradients")
             if p.numel() == p.shape[0]:  # a batch of scalars
                 tot_sumsq += (grad**2).sum()  # sum() to change shape [1] to []
             else:
-                tot_sumsq += ((grad * state["param_rms"]) ** 2).sum()
+                tot_sumsq += ((grad * state["param_rms"])**2).sum()
 
         tot_norm = tot_sumsq.sqrt()
         if "model_norms" not in first_state:
-            first_state["model_norms"] = torch.zeros(clipping_update_period, device=p.device)
+            first_state["model_norms"] = torch.zeros(
+                clipping_update_period, device=p.device)
         first_state["model_norms"][step % clipping_update_period] = tot_norm
 
         if step % clipping_update_period == 0:
@@ -324,20 +350,20 @@ class ScaledAdam(BatchedOptimizer):
             for n in range(0, 5):
                 index = min(
                     clipping_update_period - 1,
-                    (clipping_update_period // 4) * n,
-                )
+                    (clipping_update_period // 4) * n, )
                 quartiles.append(sorted_norms[index].item())
 
             median = quartiles[2]
             threshold = clipping_scale * median
             first_state["model_norm_threshold"] = threshold
-            percent_clipped = (
-                first_state["num_clipped"] * 100.0 / clipping_update_period if "num_clipped" in first_state else 0.0
-            )
+            percent_clipped = (first_state["num_clipped"] * 100.0 /
+                               clipping_update_period
+                               if "num_clipped" in first_state else 0.0)
             first_state["num_clipped"] = 0
             quartiles = " ".join(["%.3e" % x for x in quartiles])
             logging.info(
-                f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}"
+                f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, "
+                f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}"
             )
 
         if step < clipping_update_period:
@@ -347,20 +373,25 @@ class ScaledAdam(BatchedOptimizer):
                 model_norm_threshold = first_state["model_norm_threshold"]
             except KeyError:
                 logging.info(
-                    "Warning: model_norm_threshold not in state: possibly you changed config when restarting, adding clipping_scale option?"
+                    "Warning: model_norm_threshold not in state: possibly "
+                    "you changed config when restarting, adding clipping_scale option?"
                 )
                 return 1.0
             ans = min(1.0, (model_norm_threshold / (tot_norm + 1.0e-20)).item())
             if ans < 1.0:
                 first_state["num_clipped"] += 1
             if ans < 0.1:
-                logging.warn(f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}")
+                logging.warn(
+                    f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}"
+                )
                 if self.show_dominant_parameters:
                     assert p.shape[0] == len(param_names)
                     self._show_gradient_dominating_parameter(tuples, tot_sumsq)
             return ans
 
-    def _show_gradient_dominating_parameter(self, tuples: List[Tuple[Tensor, dict, List[str]]], tot_sumsq: Tensor):
+    def _show_gradient_dominating_parameter(
+            self, tuples: List[Tuple[Tensor, dict, List[str]]],
+            tot_sumsq: Tensor):
         """
         Show information of parameter wihch dominanting tot_sumsq.
 
@@ -375,7 +406,7 @@ class ScaledAdam(BatchedOptimizer):
                 from tuples, we still pass it to save some time.
         """
         all_sumsq_orig = {}
-        for p, state, batch_param_names in tuples:
+        for (p, state, batch_param_names) in tuples:
             # p is a stacked batch parameters.
             batch_grad = p.grad
             if p.numel() == p.shape[0]:  # a batch of scalars
@@ -384,46 +415,41 @@ class ScaledAdam(BatchedOptimizer):
                 batch_rms_orig = torch.ones(p.shape[0])
             else:
                 batch_rms_orig = state["param_rms"]
-                batch_sumsq_orig = ((batch_grad * batch_rms_orig) ** 2).sum(dim=list(range(1, batch_grad.ndim)))
+                batch_sumsq_orig = ((batch_grad * batch_rms_orig)**2).sum(
+                    dim=list(range(1, batch_grad.ndim)))
+
+            for name, sumsq_orig, rms, grad in zip(batch_param_names,
+                                                   batch_sumsq_orig,
+                                                   batch_rms_orig, batch_grad):
 
-            for name, sumsq_orig, rms, grad in zip(
-                batch_param_names,
-                batch_sumsq_orig,
-                batch_rms_orig,
-                batch_grad,
-            ):
                 proportion_orig = sumsq_orig / tot_sumsq
                 all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad)
 
         assert torch.isclose(
             sum([value[0] for value in all_sumsq_orig.values()]).cpu(),
-            torch.tensor(1.0),
-        )
+            torch.tensor(1.0), )
         sorted_by_proportion = {
             k: v
             for k, v in sorted(
                 all_sumsq_orig.items(),
                 key=lambda item: item[1][0],
-                reverse=True,
-            )
+                reverse=True, )
         }
         dominant_param_name = next(iter(sorted_by_proportion))
-        (
-            dominant_proportion,
-            dominant_sumsq,
-            dominant_rms,
-            dominant_grad,
-        ) = sorted_by_proportion[dominant_param_name]
-        logging.info(
-            f"Parameter Dominanting tot_sumsq {dominant_param_name}"
-            f" with proportion {dominant_proportion:.2f},"
-            f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)"
-            f"={dominant_sumsq:.3e},"
-            f" grad_sumsq = {(dominant_grad**2).sum():.3e},"
-            f" orig_rms_sq={(dominant_rms**2).item():.3e}"
-        )
+        (dominant_proportion, dominant_sumsq, dominant_rms,
+         dominant_grad, ) = sorted_by_proportion[dominant_param_name]
+        logging.info(f"Parameter Dominanting tot_sumsq {dominant_param_name}"
+                     f" with proportion {dominant_proportion:.2f},"
+                     f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)"
+                     f"={dominant_sumsq:.3e},"
+                     f" grad_sumsq = {(dominant_grad**2).sum():.3e},"
+                     f" orig_rms_sq={(dominant_rms**2).item():.3e}")
 
-    def _step_one_batch(self, group: dict, p: Tensor, state: dict, clipping_scale: float):
+    def _step_one_batch(self,
+                        group: dict,
+                        p: Tensor,
+                        state: dict,
+                        clipping_scale: float):
         """
         Do the step for one parameter, which is actually going to be a batch of
         `real` parameters, with dim 0 as the batch dim.
@@ -449,10 +475,13 @@ class ScaledAdam(BatchedOptimizer):
         if numel > 1:
             # Update the size/scale of p, and set param_rms
             scale_grads = state["scale_grads"]
-            scale_grads[step % size_update_period] = (p * grad).sum(dim=list(range(1, p.ndim)), keepdim=True)
+            scale_grads[step % size_update_period] = (p * grad).sum(
+                dim=list(range(1, p.ndim)), keepdim=True)
             if step % size_update_period == size_update_period - 1:
                 param_rms = state["param_rms"]  # shape: (batch_size, 1, 1, ..)
-                param_rms.copy_((p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt())
+                param_rms.copy_((p**2)
+                                .mean(dim=list(range(1, p.ndim)), keepdim=True)
+                                .sqrt())
                 if step > 0:
                     # self._size_update() learns the overall scale on the
                     # parameter, by shrinking or expanding it.
@@ -467,13 +496,11 @@ class ScaledAdam(BatchedOptimizer):
 
         state["step"] = step + 1
 
-    def _size_update(
-        self,
-        group: dict,
-        scale_grads: Tensor,
-        p: Tensor,
-        state: dict,
-    ) -> None:
+    def _size_update(self,
+                     group: dict,
+                     scale_grads: Tensor,
+                     p: Tensor,
+                     state: dict) -> None:
         """
                Called only where p.numel() > 1, this updates the scale of the parameter.
                If we imagine: p =  underlying_param * scale.exp(), and we are doing
@@ -502,11 +529,11 @@ class ScaledAdam(BatchedOptimizer):
         # faster decay at this level.
         beta2_corr = beta2**size_update_period
 
-        scale_exp_avg_sq = state["scale_exp_avg_sq"]  # shape: (batch_size, 1, 1, ..)
+        scale_exp_avg_sq = state[
+            "scale_exp_avg_sq"]  # shape: (batch_size, 1, 1, ..)
         scale_exp_avg_sq.mul_(beta2_corr).add_(
             (scale_grads**2).mean(dim=0),  # mean over dim `size_update_period`
-            alpha=1 - beta2_corr,
-        )  # shape is (batch_size, 1, 1, ...)
+            alpha=1 - beta2_corr, )  # shape is (batch_size, 1, 1, ...)
 
         # The 1st time we reach here is when size_step == 1.
         size_step = (step + 1) // size_update_period
@@ -516,7 +543,8 @@ class ScaledAdam(BatchedOptimizer):
 
         denom = scale_exp_avg_sq.sqrt() + eps
 
-        scale_step = -size_lr * (bias_correction2**0.5) * scale_grads.sum(dim=0) / denom
+        scale_step = (-size_lr * (bias_correction2**0.5) *
+                      scale_grads.sum(dim=0) / denom)
 
         is_too_small = param_rms < param_min_rms
         is_too_large = param_rms > param_max_rms
@@ -552,8 +580,9 @@ class ScaledAdam(BatchedOptimizer):
         exp_avg_sq = state["exp_avg_sq"]
         exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1 - beta2))
 
-        this_step = state["step"] - (state["zero_step"] if "zero_step" in state else 0)
-        bias_correction2 = 1 - beta2 ** (this_step + 1)
+        this_step = state["step"] - (state["zero_step"]
+                                     if "zero_step" in state else 0)
+        bias_correction2 = 1 - beta2**(this_step + 1)
         if bias_correction2 < 0.99:
             # note: not in-place.
             exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2)
@@ -584,7 +613,7 @@ class ScaledAdam(BatchedOptimizer):
 
         # bias_correction2 is like in Adam.  Don't bother with bias_correction1;
         # slower update at the start will help stability anyway.
-        bias_correction2 = 1 - beta2 ** (state["step"] + 1)
+        bias_correction2 = 1 - beta2**(state["step"] + 1)
         denom = (exp_avg_sq / bias_correction2).sqrt() + eps
 
         delta = state["delta"]

GPT_SoVITS/AR/modules/patched_mha_with_cache.py

@@ -5,39 +5,40 @@ from torch.nn.functional import (
     _none_or_dtype,
     _in_projection_packed,
 )
+from torch.nn import functional as F
 import torch
 # Tensor = torch.Tensor
 # from typing import Callable, List, Optional, Tuple, Union
 
 
 def multi_head_attention_forward_patched(
-    query,
-    key,
-    value,
-    embed_dim_to_check,
-    num_heads,
-    in_proj_weight,
-    in_proj_bias,
-    bias_k,
-    bias_v,
-    add_zero_attn,
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    embed_dim_to_check: int,
+    num_heads: int,
+    in_proj_weight: Optional[Tensor],
+    in_proj_bias: Optional[Tensor],
+    bias_k: Optional[Tensor],
+    bias_v: Optional[Tensor],
+    add_zero_attn: bool,
     dropout_p: float,
-    out_proj_weight,
-    out_proj_bias,
-    training=True,
-    key_padding_mask=None,
-    need_weights=True,
-    attn_mask=None,
-    use_separate_proj_weight=False,
-    q_proj_weight=None,
-    k_proj_weight=None,
-    v_proj_weight=None,
-    static_k=None,
-    static_v=None,
-    average_attn_weights=True,
-    is_causal=False,
+    out_proj_weight: Tensor,
+    out_proj_bias: Optional[Tensor],
+    training: bool = True,
+    key_padding_mask: Optional[Tensor] = None,
+    need_weights: bool = True,
+    attn_mask: Optional[Tensor] = None,
+    use_separate_proj_weight: bool = False,
+    q_proj_weight: Optional[Tensor] = None,
+    k_proj_weight: Optional[Tensor] = None,
+    v_proj_weight: Optional[Tensor] = None,
+    static_k: Optional[Tensor] = None,
+    static_v: Optional[Tensor] = None,
+    average_attn_weights: bool = True,
+    is_causal: bool = False,
     cache=None,
-):
+) -> Tuple[Tensor, Optional[Tensor]]:
     r"""
     Args:
         query, key, value: map a query and a set of key-value pairs to an output.
@@ -155,7 +156,9 @@ def multi_head_attention_forward_patched(
             cache=cache,
         )
 
-    is_batched = _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads)
+    is_batched = _mha_shape_check(
+        query, key, value, key_padding_mask, attn_mask, num_heads
+    )
 
     # For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input
     # is batched, run the computation and before returning squeeze the
@@ -208,33 +211,45 @@ def multi_head_attention_forward_patched(
             # longer causal.
             is_causal = False
 
-    assert embed_dim == embed_dim_to_check, (
-        f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
-    )
+    assert (
+        embed_dim == embed_dim_to_check
+    ), f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
     if isinstance(embed_dim, torch.Tensor):
         # embed_dim can be a tensor when JIT tracing
         head_dim = embed_dim.div(num_heads, rounding_mode="trunc")
     else:
         head_dim = embed_dim // num_heads
-    assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
+    assert (
+        head_dim * num_heads == embed_dim
+    ), f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
     if use_separate_proj_weight:
         # allow MHA to have different embedding dimensions when separate projection weights are used
-        assert key.shape[:2] == value.shape[:2], (
-            f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
-        )
+        assert (
+            key.shape[:2] == value.shape[:2]
+        ), f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
     else:
-        assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}"
+        assert (
+            key.shape == value.shape
+        ), f"key shape {key.shape} does not match value shape {value.shape}"
 
     #
     # compute in-projection
     #
     if not use_separate_proj_weight:
-        assert in_proj_weight is not None, "use_separate_proj_weight is False but in_proj_weight is None"
+        assert (
+            in_proj_weight is not None
+        ), "use_separate_proj_weight is False but in_proj_weight is None"
         q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
     else:
-        assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None"
-        assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None"
-        assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None"
+        assert (
+            q_proj_weight is not None
+        ), "use_separate_proj_weight is True but q_proj_weight is None"
+        assert (
+            k_proj_weight is not None
+        ), "use_separate_proj_weight is True but k_proj_weight is None"
+        assert (
+            v_proj_weight is not None
+        ), "use_separate_proj_weight is True but v_proj_weight is None"
         if in_proj_bias is None:
             b_q = b_k = b_v = None
         else:
@@ -297,7 +312,9 @@ def multi_head_attention_forward_patched(
                     f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}."
                 )
         else:
-            raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")
+            raise RuntimeError(
+                f"attn_mask's dimension {attn_mask.dim()} is not supported"
+            )
 
     # add bias along batch dimension (currently second)
     if bias_k is not None and bias_v is not None:
@@ -321,26 +338,34 @@ def multi_head_attention_forward_patched(
         k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
     else:
         # TODO finish disentangling control flow so we don't do in-projections when statics are passed
-        assert static_k.size(0) == bsz * num_heads, (
-            f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
-        )
-        assert static_k.size(2) == head_dim, f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
+        assert (
+            static_k.size(0) == bsz * num_heads
+        ), f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
+        assert (
+            static_k.size(2) == head_dim
+        ), f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
         k = static_k
     if static_v is None:
         v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
     else:
         # TODO finish disentangling control flow so we don't do in-projections when statics are passed
-        assert static_v.size(0) == bsz * num_heads, (
-            f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
-        )
-        assert static_v.size(2) == head_dim, f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
+        assert (
+            static_v.size(0) == bsz * num_heads
+        ), f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
+        assert (
+            static_v.size(2) == head_dim
+        ), f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
         v = static_v
 
     # add zero attention along batch dimension (now first)
     if add_zero_attn:
         zero_attn_shape = (bsz * num_heads, 1, head_dim)
-        k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1)
-        v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1)
+        k = torch.cat(
+            [k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1
+        )
+        v = torch.cat(
+            [v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1
+        )
         if attn_mask is not None:
             attn_mask = pad(attn_mask, (0, 1))
         if key_padding_mask is not None:
@@ -356,7 +381,9 @@ def multi_head_attention_forward_patched(
             src_len,
         ), f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
         key_padding_mask = (
-            key_padding_mask.view(bsz, 1, 1, src_len).expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len)
+            key_padding_mask.view(bsz, 1, 1, src_len)
+            .expand(-1, num_heads, -1, -1)
+            .reshape(bsz * num_heads, 1, src_len)
         )
         if attn_mask is None:
             attn_mask = key_padding_mask
@@ -375,10 +402,14 @@ def multi_head_attention_forward_patched(
         B, Nt, E = q.shape
         q_scaled = q / math.sqrt(E)
 
-        assert not (is_causal and attn_mask is None), "FIXME: is_causal not implemented for need_weights"
+        assert not (
+            is_causal and attn_mask is None
+        ), "FIXME: is_causal not implemented for need_weights"
 
         if attn_mask is not None:
-            attn_output_weights = torch.baddbmm(attn_mask, q_scaled, k.transpose(-2, -1))
+            attn_output_weights = torch.baddbmm(
+                attn_mask, q_scaled, k.transpose(-2, -1)
+            )
         else:
             attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1))
         attn_output_weights = softmax(attn_output_weights, dim=-1)
@@ -387,7 +418,9 @@ def multi_head_attention_forward_patched(
 
         attn_output = torch.bmm(attn_output_weights, v)
 
-        attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
+        attn_output = (
+            attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
+        )
         attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
         attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
 
@@ -416,9 +449,13 @@ def multi_head_attention_forward_patched(
         v = v.view(bsz, num_heads, src_len, head_dim)
 
         # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
-        attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
+        attn_output = scaled_dot_product_attention(
+            q, k, v, attn_mask, dropout_p, is_causal
+        )
 
-        attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
+        attn_output = (
+            attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
+        )
 
         attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
         attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))

GPT_SoVITS/AR/modules/patched_mha_with_cache_onnx.py

@@ -1,9 +1,11 @@
 from torch.nn.functional import *
 from torch.nn.functional import (
+    _mha_shape_check,
     _canonical_mask,
+    _none_or_dtype,
+    _in_projection_packed,
 )
 
-
 def multi_head_attention_forward_patched(
     query,
     key,
@@ -32,6 +34,7 @@ def multi_head_attention_forward_patched(
     is_causal: bool = False,
     cache=None,
 ) -> Tuple[Tensor, Optional[Tensor]]:
+
     # set up shape vars
     _, _, embed_dim = query.shape
     attn_mask = _canonical_mask(
@@ -77,8 +80,12 @@ def multi_head_attention_forward_patched(
     q = q.view(num_heads, -1, head_dim).unsqueeze(0)
     k = k.view(num_heads, -1, head_dim).unsqueeze(0)
     v = v.view(num_heads, -1, head_dim).unsqueeze(0)
-    attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
-    attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(-1, embed_dim)
+    attn_output = scaled_dot_product_attention(
+        q, k, v, attn_mask, dropout_p, is_causal
+    )
+    attn_output = (
+        attn_output.permute(2, 0, 1, 3).contiguous().view(-1, embed_dim)
+    )
     attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
     attn_output = attn_output.view(-1, 1, attn_output.size(1))
 

GPT_SoVITS/AR/modules/scaling.py

@@ -13,9 +13,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import logging
+import math
 import random
 from typing import Optional
 from typing import Tuple
+from typing import Union
 
 import torch
 import torch.nn as nn
@@ -58,7 +61,9 @@ class DoubleSwishFunction(torch.autograd.Function):
             # floors), should be expectation-preserving.
             floor = -0.043637
             ceil = 1.2
-            d_scaled = (deriv - floor) * (255.0 / (ceil - floor)) + torch.rand_like(deriv)
+            d_scaled = (deriv - floor) * (255.0 / (ceil - floor)) + torch.rand_like(
+                deriv
+            )
             if __name__ == "__main__":
                 # for self-testing only.
                 assert d_scaled.min() >= 0.0
@@ -148,9 +153,13 @@ def _compute_scale_factor(
     else:
         # below_threshold is 0 if x_abs_mean > min_abs, can be at most max_factor if
         # x_abs)_mean , min_abs.
-        below_threshold = ((min_abs - x_abs_mean) * (gain_factor / min_abs)).clamp(min=0, max=max_factor)
+        below_threshold = ((min_abs - x_abs_mean) * (gain_factor / min_abs)).clamp(
+            min=0, max=max_factor
+        )
 
-    above_threshold = ((x_abs_mean - max_abs) * (gain_factor / max_abs)).clamp(min=0, max=max_factor)
+    above_threshold = ((x_abs_mean - max_abs) * (gain_factor / max_abs)).clamp(
+        min=0, max=max_factor
+    )
 
     return below_threshold - above_threshold
 
@@ -172,16 +181,18 @@ def _compute_sign_factor(
     else:
         # 0 if proportion_positive >= min_positive, else can be
         # as large as max_factor.
-        factor1 = ((min_positive - proportion_positive) * (gain_factor / min_positive)).clamp_(min=0, max=max_factor)
+        factor1 = (
+            (min_positive - proportion_positive) * (gain_factor / min_positive)
+        ).clamp_(min=0, max=max_factor)
 
     if max_positive == 1.0:
         factor2 = 0.0
     else:
         # 0 if self.proportion_positive <= max_positive, else can be
         # as large as -max_factor.
-        factor2 = ((proportion_positive - max_positive) * (gain_factor / (1.0 - max_positive))).clamp_(
-            min=0, max=max_factor
-        )
+        factor2 = (
+            (proportion_positive - max_positive) * (gain_factor / (1.0 - max_positive))
+        ).clamp_(min=0, max=max_factor)
     sign_factor = factor1 - factor2
     # require min_positive != 0 or max_positive != 1:
     assert not isinstance(sign_factor, float)
@@ -309,11 +320,15 @@ class ActivationBalancer(torch.nn.Module):
             return _no_op(x)
 
 
-def BalancedDoubleSwish(d_model, channel_dim=-1, max_abs=10.0, min_prob=0.25) -> nn.Sequential:
+def BalancedDoubleSwish(
+    d_model, channel_dim=-1, max_abs=10.0, min_prob=0.25
+) -> nn.Sequential:
     """
     ActivationBalancer -> DoubleSwish
     """
-    balancer = ActivationBalancer(d_model, channel_dim=channel_dim, max_abs=max_abs, min_prob=min_prob)
+    balancer = ActivationBalancer(
+        d_model, channel_dim=channel_dim, max_abs=max_abs, min_prob=min_prob
+    )
     return nn.Sequential(
         balancer,
         DoubleSwish(),

GPT_SoVITS/AR/modules/transformer.py

@@ -42,8 +42,12 @@ class LayerNorm(nn.Module):
         self.eps = eps
         self.elementwise_affine = elementwise_affine
         if self.elementwise_affine:
-            self.weight = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
-            self.bias = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
+            self.weight = nn.Parameter(
+                torch.empty(self.normalized_shape, **factory_kwargs)
+            )
+            self.bias = nn.Parameter(
+                torch.empty(self.normalized_shape, **factory_kwargs)
+            )
         else:
             self.register_parameter("weight", None)
             self.register_parameter("bias", None)
@@ -70,10 +74,15 @@ class LayerNorm(nn.Module):
             )
 
         assert embedding is None
-        return F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps)
+        return F.layer_norm(
+            input, self.normalized_shape, self.weight, self.bias, self.eps
+        )
 
     def extra_repr(self) -> str:
-        return "{normalized_shape}, eps={eps}, elementwise_affine={elementwise_affine}".format(**self.__dict__)
+        return (
+            "{normalized_shape}, eps={eps}, "
+            "elementwise_affine={elementwise_affine}".format(**self.__dict__)
+        )
 
 
 class IdentityNorm(nn.Module):
@@ -112,7 +121,6 @@ class TransformerEncoder(nn.Module):
         >>> src = torch.rand(10, 32, 512)
         >>> out = transformer_encoder(src)
     """
-
     __constants__ = ["norm"]
 
     def __init__(self, encoder_layer, num_layers, norm=None):
@@ -210,9 +218,13 @@ class TransformerEncoderLayer(nn.Module):
         )
 
         # Implementation of Feedforward model
-        self.linear1 = linear1_feedforward_cls(d_model, dim_feedforward, **factory_kwargs)
+        self.linear1 = linear1_feedforward_cls(
+            d_model, dim_feedforward, **factory_kwargs
+        )
         self.dropout = nn.Dropout(dropout)
-        self.linear2 = linear2_feedforward_cls(dim_feedforward, d_model, **factory_kwargs)
+        self.linear2 = linear2_feedforward_cls(
+            dim_feedforward, d_model, **factory_kwargs
+        )
 
         self.norm_first = norm_first
         self.dropout1 = nn.Dropout(dropout)
@@ -279,8 +291,12 @@ class TransformerEncoderLayer(nn.Module):
 
         if src_key_padding_mask is not None:
             _skpm_dtype = src_key_padding_mask.dtype
-            if _skpm_dtype != torch.bool and not torch.is_floating_point(src_key_padding_mask):
-                raise AssertionError("only bool and floating types of key_padding_mask are supported")
+            if _skpm_dtype != torch.bool and not torch.is_floating_point(
+                src_key_padding_mask
+            ):
+                raise AssertionError(
+                    "only bool and floating types of key_padding_mask are supported"
+                )
 
         if self.norm_first:
             x = x + self._sa_block(

GPT_SoVITS/AR/modules/transformer_onnx.py

@@ -42,8 +42,12 @@ class LayerNorm(nn.Module):
         self.eps = eps
         self.elementwise_affine = elementwise_affine
         if self.elementwise_affine:
-            self.weight = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
-            self.bias = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
+            self.weight = nn.Parameter(
+                torch.empty(self.normalized_shape, **factory_kwargs)
+            )
+            self.bias = nn.Parameter(
+                torch.empty(self.normalized_shape, **factory_kwargs)
+            )
         else:
             self.register_parameter("weight", None)
             self.register_parameter("bias", None)
@@ -70,10 +74,15 @@ class LayerNorm(nn.Module):
             )
 
         assert embedding is None
-        return F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps)
+        return F.layer_norm(
+            input, self.normalized_shape, self.weight, self.bias, self.eps
+        )
 
     def extra_repr(self) -> str:
-        return "{normalized_shape}, eps={eps}, elementwise_affine={elementwise_affine}".format(**self.__dict__)
+        return (
+            "{normalized_shape}, eps={eps}, "
+            "elementwise_affine={elementwise_affine}".format(**self.__dict__)
+        )
 
 
 class IdentityNorm(nn.Module):
@@ -112,7 +121,6 @@ class TransformerEncoder(nn.Module):
         >>> src = torch.rand(10, 32, 512)
         >>> out = transformer_encoder(src)
     """
-
     __constants__ = ["norm"]
 
     def __init__(self, encoder_layer, num_layers, norm=None):
@@ -146,7 +154,6 @@ class TransformerEncoder(nn.Module):
 
 class TransformerEncoderLayer(nn.Module):
     __constants__ = ["batch_first", "norm_first"]
-
     def __init__(
         self,
         d_model: int,
@@ -177,9 +184,13 @@ class TransformerEncoderLayer(nn.Module):
             linear2_cls=linear2_self_attention_cls,
             **factory_kwargs,
         )
-        self.linear1 = linear1_feedforward_cls(d_model, dim_feedforward, **factory_kwargs)
+        self.linear1 = linear1_feedforward_cls(
+            d_model, dim_feedforward, **factory_kwargs
+        )
         self.dropout = nn.Dropout(dropout)
-        self.linear2 = linear2_feedforward_cls(dim_feedforward, d_model, **factory_kwargs)
+        self.linear2 = linear2_feedforward_cls(
+            dim_feedforward, d_model, **factory_kwargs
+        )
         self.norm_first = norm_first
         self.dropout1 = nn.Dropout(dropout)
         self.dropout2 = nn.Dropout(dropout)

GPT_SoVITS/AR/text_processing/phonemizer.py

@@ -30,7 +30,9 @@ class GruutPhonemizer:
             "«": "«",
             "»": "»",
         }
-        self._punctuation_regexp: str = rf"([{''.join(self._special_cases_dict.keys())}])"
+        self._punctuation_regexp: str = (
+            rf"([{''.join(self._special_cases_dict.keys())}])"
+        )
 
     def _normalize_punctuation(self, text: str) -> str:
         text = regex.sub(rf"\pZ+{self._punctuation_regexp}", r"\1", text)
@@ -51,8 +53,13 @@ class GruutPhonemizer:
 
     def phonemize(self, text: str, espeak: bool = False) -> str:
         text_to_phonemize: str = self._normalize_punctuation(text)
-        sents: List[Sentence] = [sent for sent in self._phonemizer(text_to_phonemize, lang="en-us", espeak=espeak)]
-        words: List[str] = [self._convert_punctuation(word) for word in itertools.chain(*sents)]
+        sents: List[Sentence] = [
+            sent
+            for sent in self._phonemizer(text_to_phonemize, lang="en-us", espeak=espeak)
+        ]
+        words: List[str] = [
+            self._convert_punctuation(word) for word in itertools.chain(*sents)
+        ]
         return " ".join(words)
 
     def transform(self, phonemes):

GPT_SoVITS/AR/text_processing/symbols.py

@@ -3,9 +3,7 @@
 PAD = "_"
 PUNCTUATION = ';:,.!?¡¿—…"«»“” '
 LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
-IPA_LETTERS = (
-    "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
-)
+IPA_LETTERS = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
 SYMBOLS = [PAD] + list(PUNCTUATION) + list(LETTERS) + list(IPA_LETTERS)
 SPACE_ID = SYMBOLS.index(" ")
 SYMBOL_TO_ID = {s: i for i, s in enumerate(SYMBOLS)}

GPT_SoVITS/AR/utils/__init__.py

@@ -2,12 +2,12 @@ import re
 
 
 def str2bool(str):
-    return True if str.lower() == "true" else False
+    return True if str.lower() == 'true' else False
 
 
 def get_newest_ckpt(string_list):
     # 定义一个正则表达式模式，用于匹配字符串中的数字
-    pattern = r"epoch=(\d+)-step=(\d+)\.ckpt"
+    pattern = r'epoch=(\d+)-step=(\d+)\.ckpt'
 
     # 使用正则表达式提取每个字符串中的数字信息，并创建一个包含元组的列表
     extracted_info = []
@@ -18,7 +18,8 @@ def get_newest_ckpt(string_list):
             step = int(match.group(2))
             extracted_info.append((epoch, step, string))
     # 按照 epoch 后面的数字和 step 后面的数字进行排序
-    sorted_info = sorted(extracted_info, key=lambda x: (x[0], x[1]), reverse=True)
+    sorted_info = sorted(
+        extracted_info, key=lambda x: (x[0], x[1]), reverse=True)
     # 获取最新的 ckpt 文件名
     newest_ckpt = sorted_info[0][2]
     return newest_ckpt
@@ -27,9 +28,9 @@ def get_newest_ckpt(string_list):
 # 文本存在且不为空时 return True
 def check_txt_file(file_path):
     try:
-        with open(file_path, "r") as file:
+        with open(file_path, 'r') as file:
             text = file.readline().strip()
-        assert text.strip() != ""
+        assert text.strip() != ''
         return text
     except Exception:
         return False

GPT_SoVITS/AR/utils/initialize.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
 """Initialize modules for espnet2 neural networks."""
-
 import torch
 from typeguard import check_argument_types
 

GPT_SoVITS/AR/utils/io.py

@@ -18,10 +18,14 @@ def save_config_to_yaml(config, path):
 
 
 def write_args(args, path):
-    args_dict = dict((name, getattr(args, name)) for name in dir(args) if not name.startswith("_"))
+    args_dict = dict(
+        (name, getattr(args, name)) for name in dir(args) if not name.startswith("_")
+    )
     with open(path, "a") as args_file:
         args_file.write("==> torch version: {}\n".format(torch.__version__))
-        args_file.write("==> cudnn version: {}\n".format(torch.backends.cudnn.version()))
+        args_file.write(
+            "==> cudnn version: {}\n".format(torch.backends.cudnn.version())
+        )
         args_file.write("==> Cmd:\n")
         args_file.write(str(sys.argv))
         args_file.write("\n==> args:\n")

GPT_SoVITS/BigVGAN/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2024 NVIDIA CORPORATION.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

GPT_SoVITS/BigVGAN/README.md

@@ -1,266 +0,0 @@
-## BigVGAN: A Universal Neural Vocoder with Large-Scale Training
-
-#### Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon
-
-[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN)
-
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/bigvgan-a-universal-neural-vocoder-with-large/speech-synthesis-on-libritts)](https://paperswithcode.com/sota/speech-synthesis-on-libritts?p=bigvgan-a-universal-neural-vocoder-with-large)
-
-<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
-
-## News
-- **Sep 2024 (v2.4):**
-  - We have updated the pretrained checkpoints trained for 5M steps. This is final release of the BigVGAN-v2 checkpoints.
-
-- **Jul 2024 (v2.3):**
-  - General refactor and code improvements for improved readability.
-  - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark.
-
-- **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio.
-
-- **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.
-
-- **Jul 2024 (v2):** We release BigVGAN-v2 along with pretrained checkpoints. Below are the highlights:
-  - Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU.
-  - Improved discriminator and loss: BigVGAN-v2 is trained using a [multi-scale sub-band CQT discriminator](https://arxiv.org/abs/2311.14957) and a [multi-scale mel spectrogram loss](https://arxiv.org/abs/2306.06546).
-  - Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments.
-  - We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling ratio.
-
-## Installation
-
-The codebase has been tested on Python `3.10` and PyTorch `2.3.1` conda packages with either `pytorch-cuda=12.1` or `pytorch-cuda=11.8`. Below is an example command to create the conda environment:
-
-```shell
-conda create -n bigvgan python=3.10 pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
-conda activate bigvgan
-```
-
-Clone the repository and install dependencies:
-
-```shell
-git clone https://github.com/NVIDIA/BigVGAN
-cd BigVGAN
-pip install -r requirements.txt
-```
-
-## Inference Quickstart using 🤗 Hugging Face Hub
-
-Below example describes how you can use BigVGAN: load the pretrained BigVGAN generator from Hugging Face Hub, compute mel spectrogram from input waveform, and generate synthesized waveform using the mel spectrogram as the model's input.
-
-```python
-device = 'cuda'
-
-import torch
-import bigvgan
-import librosa
-from meldataset import get_mel_spectrogram
-
-# instantiate the model. You can optionally set use_cuda_kernel=True for faster inference.
-model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_24khz_100band_256x', use_cuda_kernel=False)
-
-# remove weight norm in the model and set to eval mode
-model.remove_weight_norm()
-model = model.eval().to(device)
-
-# load wav file and compute mel spectrogram
-wav_path = '/path/to/your/audio.wav'
-wav, sr = librosa.load(wav_path, sr=model.h.sampling_rate, mono=True) # wav is np.ndarray with shape [T_time] and values in [-1, 1]
-wav = torch.FloatTensor(wav).unsqueeze(0) # wav is FloatTensor with shape [B(1), T_time]
-
-# compute mel spectrogram from the ground truth audio
-mel = get_mel_spectrogram(wav, model.h).to(device) # mel is FloatTensor with shape [B(1), C_mel, T_frame]
-
-# generate waveform from mel
-with torch.inference_mode():
-    wav_gen = model(mel) # wav_gen is FloatTensor with shape [B(1), 1, T_time] and values in [-1, 1]
-wav_gen_float = wav_gen.squeeze(0).cpu() # wav_gen is FloatTensor with shape [1, T_time]
-
-# you can convert the generated waveform to 16 bit linear PCM
-wav_gen_int16 = (wav_gen_float * 32767.0).numpy().astype('int16') # wav_gen is now np.ndarray with shape [1, T_time] and int16 dtype
-```
-
-## Local gradio demo <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a>
-
-You can run a local gradio demo using below command:
-
-```python
-pip install -r demo/requirements.txt
-python demo/app.py
-```
-
-## Training
-
-Create symbolic link to the root of the dataset. The codebase uses filelist with the relative path from the dataset. Below are the example commands for LibriTTS dataset:
-
-```shell
-cd filelists/LibriTTS && \
-ln -s /path/to/your/LibriTTS/train-clean-100 train-clean-100 && \
-ln -s /path/to/your/LibriTTS/train-clean-360 train-clean-360 && \
-ln -s /path/to/your/LibriTTS/train-other-500 train-other-500 && \
-ln -s /path/to/your/LibriTTS/dev-clean dev-clean && \
-ln -s /path/to/your/LibriTTS/dev-other dev-other && \
-ln -s /path/to/your/LibriTTS/test-clean test-clean && \
-ln -s /path/to/your/LibriTTS/test-other test-other && \
-cd ../..
-```
-
-Train BigVGAN model. Below is an example command for training BigVGAN-v2 using LibriTTS dataset at 24kHz with a full 100-band mel spectrogram as input:
-
-```shell
-python train.py \
---config configs/bigvgan_v2_24khz_100band_256x.json \
---input_wavs_dir filelists/LibriTTS \
---input_training_file filelists/LibriTTS/train-full.txt \
---input_validation_file filelists/LibriTTS/val-full.txt \
---list_input_unseen_wavs_dir filelists/LibriTTS filelists/LibriTTS \
---list_input_unseen_validation_file filelists/LibriTTS/dev-clean.txt filelists/LibriTTS/dev-other.txt \
---checkpoint_path exp/bigvgan_v2_24khz_100band_256x
-```
-
-## Synthesis
-
-Synthesize from BigVGAN model. Below is an example command for generating audio from the model.
-It computes mel spectrograms using wav files from `--input_wavs_dir` and saves the generated audio to `--output_dir`.
-
-```shell
-python inference.py \
---checkpoint_file /path/to/your/bigvgan_v2_24khz_100band_256x/bigvgan_generator.pt \
---input_wavs_dir /path/to/your/input_wav \
---output_dir /path/to/your/output_wav
-```
-
-`inference_e2e.py` supports synthesis directly from the mel spectrogram saved in `.npy` format, with shapes `[1, channel, frame]` or `[channel, frame]`.
-It loads mel spectrograms from `--input_mels_dir` and saves the generated audio to `--output_dir`.
-
-Make sure that the STFT hyperparameters for mel spectrogram are the same as the model, which are defined in `config.json` of the corresponding model.
-
-```shell
-python inference_e2e.py \
---checkpoint_file /path/to/your/bigvgan_v2_24khz_100band_256x/bigvgan_generator.pt \
---input_mels_dir /path/to/your/input_mel \
---output_dir /path/to/your/output_wav
-```
-
-## Using Custom CUDA Kernel for Synthesis
-
-You can apply the fast CUDA inference kernel by using a parameter `use_cuda_kernel` when instantiating BigVGAN:
-
-```python
-generator = BigVGAN(h, use_cuda_kernel=True)
-```
-
-You can also pass `--use_cuda_kernel` to `inference.py` and `inference_e2e.py` to enable this feature.
-
-When applied for the first time, it builds the kernel using `nvcc` and `ninja`. If the build succeeds, the kernel is saved to `alias_free_activation/cuda/build` and the model automatically loads the kernel. The codebase has been tested using CUDA `12.1`.
-
-Please make sure that both are installed in your system and `nvcc` installed in your system matches the version your PyTorch build is using.
-
-We recommend running `test_cuda_vs_torch_model.py` first to build and check the correctness of the CUDA kernel. See below example command and its output, where it returns `[Success] test CUDA fused vs. plain torch BigVGAN inference`:
-
-```python
-python tests/test_cuda_vs_torch_model.py \
---checkpoint_file /path/to/your/bigvgan_generator.pt
-```
-
-```shell
-loading plain Pytorch BigVGAN
-...
-loading CUDA kernel BigVGAN with auto-build
-Detected CUDA files, patching ldflags
-Emitting ninja build file /path/to/your/BigVGAN/alias_free_activation/cuda/build/build.ninja..
-Building extension module anti_alias_activation_cuda...
-...
-Loading extension module anti_alias_activation_cuda...
-...
-Loading '/path/to/your/bigvgan_generator.pt'
-...
-[Success] test CUDA fused vs. plain torch BigVGAN inference
- > mean_difference=0.0007238413265440613
-...
-```
-
-If you see `[Fail] test CUDA fused vs. plain torch BigVGAN inference`, it means that the CUDA kernel inference is incorrect. Please check if `nvcc` installed in your system is compatible with your PyTorch version.
-
-## Pretrained Models
-
-We provide the [pretrained models on Hugging Face Collections](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a).
-One can download the checkpoints of the generator weight (named `bigvgan_generator.pt`) and its discriminator/optimizer states (named `bigvgan_discriminator_optimizer.pt`) within the listed model repositories.
-
-| Model Name                                                                                               | Sampling Rate | Mel band | fmax  | Upsampling Ratio | Params | Dataset                    | Steps | Fine-Tuned |
-|:--------------------------------------------------------------------------------------------------------:|:-------------:|:--------:|:-----:|:----------------:|:------:|:--------------------------:|:-----:|:----------:|
-| [bigvgan_v2_44khz_128band_512x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x)             | 44 kHz        | 128      | 22050 | 512              | 122M   | Large-scale Compilation    | 5M    | No         |
-| [bigvgan_v2_44khz_128band_256x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_256x)             | 44 kHz        | 128      | 22050 | 256              | 112M   | Large-scale Compilation    | 5M    | No         |
-| [bigvgan_v2_24khz_100band_256x](https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x)             | 24 kHz        | 100      | 12000 | 256              | 112M   | Large-scale Compilation    | 5M    | No         |
-| [bigvgan_v2_22khz_80band_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_256x)               | 22 kHz        | 80       | 11025 | 256              | 112M   | Large-scale Compilation    | 5M    | No         |
-| [bigvgan_v2_22khz_80band_fmax8k_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_fmax8k_256x) | 22 kHz        | 80       | 8000  | 256              | 112M   | Large-scale Compilation    | 5M    | No         |
-| [bigvgan_24khz_100band](https://huggingface.co/nvidia/bigvgan_24khz_100band)                             | 24 kHz        | 100      | 12000 | 256              | 112M   | LibriTTS                   | 5M    | No         |
-| [bigvgan_base_24khz_100band](https://huggingface.co/nvidia/bigvgan_base_24khz_100band)                   | 24 kHz        | 100      | 12000 | 256              | 14M    | LibriTTS                   | 5M    | No         |
-| [bigvgan_22khz_80band](https://huggingface.co/nvidia/bigvgan_22khz_80band)                               | 22 kHz        | 80       | 8000  | 256              | 112M   | LibriTTS + VCTK + LJSpeech | 5M    | No         |
-| [bigvgan_base_22khz_80band](https://huggingface.co/nvidia/bigvgan_base_22khz_80band)                     | 22 kHz        | 80       | 8000  | 256              | 14M    | LibriTTS + VCTK + LJSpeech | 5M    | No         |
-
-The paper results are based on the original 24kHz BigVGAN models (`bigvgan_24khz_100band` and `bigvgan_base_24khz_100band`) trained on LibriTTS dataset.
-We also provide 22kHz BigVGAN models with band-limited setup (i.e., fmax=8000) for TTS applications.
-Note that the checkpoints use `snakebeta` activation with log scale parameterization, which have the best overall quality.
-
-You can fine-tune the models by:
-
-1. downloading the checkpoints (both the generator weight and its discriminator/optimizer states)
-2. resuming training using your audio dataset by specifying `--checkpoint_path` that includes the checkpoints when launching `train.py`
-
-## Training Details of BigVGAN-v2
-
-Comapred to the original BigVGAN, the pretrained checkpoints of BigVGAN-v2 used `batch_size=32` with a longer `segment_size=65536` and are trained using 8 A100 GPUs.
-
-Note that the BigVGAN-v2 `json` config files in `./configs` use `batch_size=4` as default to fit in a single A100 GPU for training. You can fine-tune the models adjusting `batch_size` depending on your GPUs.
-
-When training BigVGAN-v2 from scratch with small batch size, it can potentially encounter the early divergence problem mentioned in the paper. In such case, we recommend lowering the `clip_grad_norm` value (e.g. `100`) for the early training iterations (e.g. 20000 steps) and increase the value to the default `500`.
-
-## Evaluation Results of BigVGAN-v2
-
-Below are the objective results of the 24kHz model (`bigvgan_v2_24khz_100band_256x`) obtained from the LibriTTS `dev` sets. BigVGAN-v2 shows noticeable improvements of the metrics. The model also exhibits reduced perceptual artifacts, especially for non-speech audio.
-
-| Model      | Dataset                 | Steps | PESQ(↑)   | M-STFT(↓)  | MCD(↓)     | Periodicity(↓) | V/UV F1(↑) |
-|:----------:|:-----------------------:|:-----:|:---------:|:----------:|:----------:|:--------------:|:----------:|
-| BigVGAN    | LibriTTS                | 1M    | 4.027     | 0.7997     | 0.3745     | 0.1018         | 0.9598     |
-| BigVGAN    | LibriTTS                | 5M    | 4.256     | 0.7409     | 0.2988     | 0.0809         | 0.9698     |
-| BigVGAN-v2 | Large-scale Compilation | 3M    | 4.359     | 0.7134     | 0.3060     | 0.0621         | 0.9777     |
-| BigVGAN-v2 | Large-scale Compilation | 5M    | **4.362** | **0.7026** | **0.2903** | **0.0593**     | **0.9793** |
-
-## Speed Benchmark
-
-Below are the speed and VRAM usage benchmark results of BigVGAN from `tests/test_cuda_vs_torch_model.py`, using `bigvgan_v2_24khz_100band_256x` as a reference model.
-
-| GPU                        | num_mel_frame | use_cuda_kernel | Speed (kHz) | Real-time Factor | VRAM (GB) |
-|:--------------------------:|:-------------:|:---------------:|:-----------:|:----------------:|:---------:|
-| NVIDIA A100                | 256           | False           | 1672.1      | 69.7x            | 1.3       |
-|                            |               | True            | 3916.5      | 163.2x           | 1.3       |
-|                            | 2048          | False           | 1899.6      | 79.2x            | 1.7       |
-|                            |               | True            | 5330.1      | 222.1x           | 1.7       |
-|                            | 16384         | False           | 1973.8      | 82.2x            | 5.0       |
-|                            |               | True            | 5761.7      | 240.1x           | 4.4       |
-| NVIDIA GeForce RTX 3080    | 256           | False           | 841.1       | 35.0x            | 1.3       |
-|                            |               | True            | 1598.1      | 66.6x            | 1.3       |
-|                            | 2048          | False           | 929.9       | 38.7x            | 1.7       |
-|                            |               | True            | 1971.3      | 82.1x            | 1.6       |
-|                            | 16384         | False           | 943.4       | 39.3x            | 5.0       |
-|                            |               | True            | 2026.5      | 84.4x            | 3.9       |
-| NVIDIA GeForce RTX 2080 Ti | 256           | False           | 515.6       | 21.5x            | 1.3       |
-|                            |               | True            | 811.3       | 33.8x            | 1.3       |
-|                            | 2048          | False           | 576.5       | 24.0x            | 1.7       |
-|                            |               | True            | 1023.0      | 42.6x            | 1.5       |
-|                            | 16384         | False           | 589.4       | 24.6x            | 5.0       |
-|                            |               | True            | 1068.1      | 44.5x            | 3.2       |
-
-## Acknowledgements
-
-We thank Vijay Anand Korthikanti and Kevin J. Shih for their generous support in implementing the CUDA kernel for inference.
-
-## References
-
-- [HiFi-GAN](https://github.com/jik876/hifi-gan) (for generator and multi-period discriminator)
-- [Snake](https://github.com/EdwardDixon/snake) (for periodic activation)
-- [Alias-free-torch](https://github.com/junjun3518/alias-free-torch) (for anti-aliasing)
-- [Julius](https://github.com/adefossez/julius) (for low-pass filter)
-- [UnivNet](https://github.com/mindslab-ai/univnet) (for multi-resolution discriminator)
-- [descript-audio-codec](https://github.com/descriptinc/descript-audio-codec) and [vocos](https://github.com/gemelo-ai/vocos) (for multi-band multi-scale STFT discriminator and multi-scale mel spectrogram loss)
-- [Amphion](https://github.com/open-mmlab/Amphion) (for multi-scale sub-band CQT discriminator)

GPT_SoVITS/BigVGAN/activations.py

@@ -1,122 +0,0 @@
-# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-import torch
-from torch import nn, sin, pow
-from torch.nn import Parameter
-
-
-class Snake(nn.Module):
-    """
-    Implementation of a sine-based periodic activation function
-    Shape:
-        - Input: (B, C, T)
-        - Output: (B, C, T), same shape as the input
-    Parameters:
-        - alpha - trainable parameter
-    References:
-        - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
-        https://arxiv.org/abs/2006.08195
-    Examples:
-        >>> a1 = snake(256)
-        >>> x = torch.randn(256)
-        >>> x = a1(x)
-    """
-
-    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
-        """
-        Initialization.
-        INPUT:
-            - in_features: shape of the input
-            - alpha: trainable parameter
-            alpha is initialized to 1 by default, higher values = higher-frequency.
-            alpha will be trained along with the rest of your model.
-        """
-        super(Snake, self).__init__()
-        self.in_features = in_features
-
-        # Initialize alpha
-        self.alpha_logscale = alpha_logscale
-        if self.alpha_logscale:  # Log scale alphas initialized to zeros
-            self.alpha = Parameter(torch.zeros(in_features) * alpha)
-        else:  # Linear scale alphas initialized to ones
-            self.alpha = Parameter(torch.ones(in_features) * alpha)
-
-        self.alpha.requires_grad = alpha_trainable
-
-        self.no_div_by_zero = 0.000000001
-
-    def forward(self, x):
-        """
-        Forward pass of the function.
-        Applies the function to the input elementwise.
-        Snake ∶= x + 1/a * sin^2 (xa)
-        """
-        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # Line up with x to [B, C, T]
-        if self.alpha_logscale:
-            alpha = torch.exp(alpha)
-        x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
-
-        return x
-
-
-class SnakeBeta(nn.Module):
-    """
-    A modified Snake function which uses separate parameters for the magnitude of the periodic components
-    Shape:
-        - Input: (B, C, T)
-        - Output: (B, C, T), same shape as the input
-    Parameters:
-        - alpha - trainable parameter that controls frequency
-        - beta - trainable parameter that controls magnitude
-    References:
-        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
-        https://arxiv.org/abs/2006.08195
-    Examples:
-        >>> a1 = snakebeta(256)
-        >>> x = torch.randn(256)
-        >>> x = a1(x)
-    """
-
-    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
-        """
-        Initialization.
-        INPUT:
-            - in_features: shape of the input
-            - alpha - trainable parameter that controls frequency
-            - beta - trainable parameter that controls magnitude
-            alpha is initialized to 1 by default, higher values = higher-frequency.
-            beta is initialized to 1 by default, higher values = higher-magnitude.
-            alpha will be trained along with the rest of your model.
-        """
-        super(SnakeBeta, self).__init__()
-        self.in_features = in_features
-
-        # Initialize alpha
-        self.alpha_logscale = alpha_logscale
-        if self.alpha_logscale:  # Log scale alphas initialized to zeros
-            self.alpha = Parameter(torch.zeros(in_features) * alpha)
-            self.beta = Parameter(torch.zeros(in_features) * alpha)
-        else:  # Linear scale alphas initialized to ones
-            self.alpha = Parameter(torch.ones(in_features) * alpha)
-            self.beta = Parameter(torch.ones(in_features) * alpha)
-
-        self.alpha.requires_grad = alpha_trainable
-        self.beta.requires_grad = alpha_trainable
-
-        self.no_div_by_zero = 0.000000001
-
-    def forward(self, x):
-        """
-        Forward pass of the function.
-        Applies the function to the input elementwise.
-        SnakeBeta ∶= x + 1/b * sin^2 (xa)
-        """
-        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # Line up with x to [B, C, T]
-        beta = self.beta.unsqueeze(0).unsqueeze(-1)
-        if self.alpha_logscale:
-            alpha = torch.exp(alpha)
-            beta = torch.exp(beta)
-        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
-
-        return x

GPT_SoVITS/BigVGAN/alias_free_activation/cuda/activation1d.py

@@ -1,69 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-import torch
-import torch.nn as nn
-from alias_free_activation.torch.resample import UpSample1d, DownSample1d
-
-# load fused CUDA kernel: this enables importing anti_alias_activation_cuda
-from alias_free_activation.cuda import load
-
-anti_alias_activation_cuda = load.load()
-
-
-class FusedAntiAliasActivation(torch.autograd.Function):
-    """
-    Assumes filter size 12, replication padding on upsampling/downsampling, and logscale alpha/beta parameters as inputs.
-    The hyperparameters are hard-coded in the kernel to maximize speed.
-    NOTE: The fused kenrel is incorrect for Activation1d with different hyperparameters.
-    """
-
-    @staticmethod
-    def forward(ctx, inputs, up_ftr, down_ftr, alpha, beta):
-        activation_results = anti_alias_activation_cuda.forward(inputs, up_ftr, down_ftr, alpha, beta)
-
-        return activation_results
-
-    @staticmethod
-    def backward(ctx, output_grads):
-        raise NotImplementedError
-        return output_grads, None, None
-
-
-class Activation1d(nn.Module):
-    def __init__(
-        self,
-        activation,
-        up_ratio: int = 2,
-        down_ratio: int = 2,
-        up_kernel_size: int = 12,
-        down_kernel_size: int = 12,
-        fused: bool = True,
-    ):
-        super().__init__()
-        self.up_ratio = up_ratio
-        self.down_ratio = down_ratio
-        self.act = activation
-        self.upsample = UpSample1d(up_ratio, up_kernel_size)
-        self.downsample = DownSample1d(down_ratio, down_kernel_size)
-
-        self.fused = fused  # Whether to use fused CUDA kernel or not
-
-    def forward(self, x):
-        if not self.fused:
-            x = self.upsample(x)
-            x = self.act(x)
-            x = self.downsample(x)
-            return x
-        else:
-            if self.act.__class__.__name__ == "Snake":
-                beta = self.act.alpha.data  # Snake uses same params for alpha and beta
-            else:
-                beta = self.act.beta.data  # Snakebeta uses different params for alpha and beta
-            alpha = self.act.alpha.data
-            if not self.act.alpha_logscale:  # Exp baked into cuda kernel, cancel it out with a log
-                alpha = torch.log(alpha)
-                beta = torch.log(beta)
-
-            x = FusedAntiAliasActivation.apply(x, self.upsample.filter, self.downsample.lowpass.filter, alpha, beta)
-            return x

GPT_SoVITS/BigVGAN/alias_free_activation/cuda/anti_alias_activation.cpp

@@ -1,23 +0,0 @@
-/* coding=utf-8
- * Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
- #include <torch/extension.h>
-
-extern "C" torch::Tensor fwd_cuda(torch::Tensor const &input, torch::Tensor const &up_filter, torch::Tensor const &down_filter, torch::Tensor const &alpha, torch::Tensor const &beta);
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("forward", &fwd_cuda, "Anti-Alias Activation forward (CUDA)");
-}

GPT_SoVITS/BigVGAN/alias_free_activation/cuda/anti_alias_activation_cuda.cu

@@ -1,246 +0,0 @@
-/* coding=utf-8
- * Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <ATen/ATen.h>
-#include <cuda.h>
-#include <cuda_runtime.h>
-#include <cuda_fp16.h>
-#include <cuda_profiler_api.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <torch/extension.h>
-#include "type_shim.h"
-#include <assert.h>
-#include <cfloat>
-#include <limits>
-#include <stdint.h>
-#include <c10/macros/Macros.h>
-
-namespace
-{
-    // Hard-coded hyperparameters
-    // WARP_SIZE and WARP_BATCH must match the return values batches_per_warp and
-    constexpr int ELEMENTS_PER_LDG_STG = 1; //(WARP_ITERATIONS < 4) ? 1 : 4;
-    constexpr int BUFFER_SIZE = 32;
-    constexpr int FILTER_SIZE = 12;
-    constexpr int HALF_FILTER_SIZE = 6;
-    constexpr int UPSAMPLE_REPLICATION_PAD = 5; // 5 on each side, matching torch impl
-    constexpr int DOWNSAMPLE_REPLICATION_PAD_LEFT = 5; // matching torch impl
-    constexpr int DOWNSAMPLE_REPLICATION_PAD_RIGHT = 6; // matching torch impl
-
-    template <typename input_t, typename output_t, typename acc_t>
-    __global__ void anti_alias_activation_forward(
-        output_t *dst,
-        const input_t *src,
-        const input_t *up_ftr,
-        const input_t *down_ftr,
-        const input_t *alpha,
-        const input_t *beta,
-        int batch_size,
-        int channels,
-        int seq_len)
-    {
-        // Up and downsample filters
-        input_t up_filter[FILTER_SIZE];
-        input_t down_filter[FILTER_SIZE];
-
-        // Load data from global memory including extra indices reserved for replication paddings
-        input_t elements[2 * FILTER_SIZE + 2 * BUFFER_SIZE + 2 * UPSAMPLE_REPLICATION_PAD] = {0};
-        input_t intermediates[2 * FILTER_SIZE + 2 * BUFFER_SIZE + DOWNSAMPLE_REPLICATION_PAD_LEFT + DOWNSAMPLE_REPLICATION_PAD_RIGHT] = {0};
-
-        // Output stores downsampled output before writing to dst
-        output_t output[BUFFER_SIZE];
-
-        // blockDim/threadIdx = (128, 1, 1)
-        // gridDim/blockIdx = (seq_blocks, channels, batches)
-        int block_offset = (blockIdx.x * 128 * BUFFER_SIZE + seq_len * (blockIdx.y + gridDim.y * blockIdx.z));
-        int local_offset = threadIdx.x * BUFFER_SIZE;
-        int seq_offset = blockIdx.x * 128 * BUFFER_SIZE + local_offset;
-
-        // intermediate have double the seq_len
-        int intermediate_local_offset = threadIdx.x * BUFFER_SIZE * 2;
-        int intermediate_seq_offset = blockIdx.x * 128 * BUFFER_SIZE * 2 + intermediate_local_offset;
-
-        // Get values needed for replication padding before moving pointer
-        const input_t *right_most_pntr = src + (seq_len * (blockIdx.y + gridDim.y * blockIdx.z));
-        input_t seq_left_most_value = right_most_pntr[0];
-        input_t seq_right_most_value = right_most_pntr[seq_len - 1];
-
-        // Move src and dst pointers
-        src += block_offset + local_offset;
-        dst += block_offset + local_offset;
-
-        // Alpha and beta values for snake activatons. Applies exp by default
-        alpha = alpha + blockIdx.y;
-        input_t alpha_val = expf(alpha[0]);
-        beta = beta + blockIdx.y;
-        input_t beta_val = expf(beta[0]);
-
-        #pragma unroll
-        for (int it = 0; it < FILTER_SIZE; it += 1)
-        {
-            up_filter[it] = up_ftr[it];
-            down_filter[it] = down_ftr[it];
-        }
-
-        // Apply replication padding for upsampling, matching torch impl
-        #pragma unroll
-        for (int it = -HALF_FILTER_SIZE; it < BUFFER_SIZE + HALF_FILTER_SIZE; it += 1)
-        {
-            int element_index = seq_offset + it; // index for element
-            if ((element_index < 0) && (element_index >= -UPSAMPLE_REPLICATION_PAD))
-            {
-                elements[2 * (HALF_FILTER_SIZE + it)] = 2 * seq_left_most_value;
-            }
-            if ((element_index >= seq_len) && (element_index < seq_len + UPSAMPLE_REPLICATION_PAD))
-            {
-                elements[2 * (HALF_FILTER_SIZE + it)] = 2 * seq_right_most_value;
-            }
-            if ((element_index >= 0) && (element_index < seq_len))
-            {
-                elements[2 * (HALF_FILTER_SIZE + it)] = 2 * src[it];
-            }
-        }
-
-        // Apply upsampling strided convolution and write to intermediates. It reserves DOWNSAMPLE_REPLICATION_PAD_LEFT for replication padding of the downsampilng conv later
-        #pragma unroll
-        for (int it = 0; it < (2 * BUFFER_SIZE + 2 * FILTER_SIZE); it += 1)
-        {
-            input_t acc = 0.0;
-            int element_index = intermediate_seq_offset + it; // index for intermediate
-            #pragma unroll
-            for (int f_idx = 0; f_idx < FILTER_SIZE; f_idx += 1)
-            {
-                if ((element_index + f_idx) >= 0)
-                {
-                    acc += up_filter[f_idx] * elements[it + f_idx];
-                }
-            }
-            intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] = acc;
-        }
-
-        // Apply activation function. It reserves DOWNSAMPLE_REPLICATION_PAD_LEFT and DOWNSAMPLE_REPLICATION_PAD_RIGHT for replication padding of the downsampilng conv later
-        double no_div_by_zero = 0.000000001;
-        #pragma unroll
-        for (int it = 0; it < 2 * BUFFER_SIZE + 2 * FILTER_SIZE; it += 1)
-        {
-            intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] += (1.0 / (beta_val + no_div_by_zero)) * sinf(intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] * alpha_val) * sinf(intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] * alpha_val);
-        }
-
-        // Apply replication padding before downsampling conv from intermediates
-        #pragma unroll
-        for (int it = 0; it < DOWNSAMPLE_REPLICATION_PAD_LEFT; it += 1)
-        {
-            intermediates[it] = intermediates[DOWNSAMPLE_REPLICATION_PAD_LEFT];
-        }
-        #pragma unroll
-        for (int it = DOWNSAMPLE_REPLICATION_PAD_LEFT + 2 * BUFFER_SIZE + 2 * FILTER_SIZE; it < DOWNSAMPLE_REPLICATION_PAD_LEFT + 2 * BUFFER_SIZE + 2 * FILTER_SIZE + DOWNSAMPLE_REPLICATION_PAD_RIGHT; it += 1)
-        {
-            intermediates[it] = intermediates[DOWNSAMPLE_REPLICATION_PAD_LEFT + 2 * BUFFER_SIZE + 2 * FILTER_SIZE - 1];
-        }
-
-        // Apply downsample strided convolution (assuming stride=2) from intermediates
-        #pragma unroll
-        for (int it = 0; it < BUFFER_SIZE; it += 1)
-        {
-            input_t acc = 0.0;
-            #pragma unroll
-            for (int f_idx = 0; f_idx < FILTER_SIZE; f_idx += 1)
-            {
-                // Add constant DOWNSAMPLE_REPLICATION_PAD_RIGHT to match torch implementation
-                acc += down_filter[f_idx] * intermediates[it * 2 + f_idx + DOWNSAMPLE_REPLICATION_PAD_RIGHT];
-            }
-            output[it] = acc;
-        }
-
-        // Write output to dst
-        #pragma unroll
-        for (int it = 0;  it < BUFFER_SIZE;  it += ELEMENTS_PER_LDG_STG)
-        {
-            int element_index = seq_offset + it;
-            if (element_index < seq_len)
-            {
-                dst[it] = output[it];
-            }
-        }
-
-    }
-
-    template <typename input_t, typename output_t, typename acc_t>
-    void dispatch_anti_alias_activation_forward(
-        output_t *dst,
-        const input_t *src,
-        const input_t *up_ftr,
-        const input_t *down_ftr,
-        const input_t *alpha,
-        const input_t *beta,
-        int batch_size,
-        int channels,
-        int seq_len)
-    {
-        if (seq_len == 0)
-        {
-            return;
-        }
-        else
-        {
-            // Use 128 threads per block to maximimize gpu utilization
-            constexpr int threads_per_block = 128;
-            constexpr int seq_len_per_block = 4096;
-            int blocks_per_seq_len = (seq_len + seq_len_per_block - 1) / seq_len_per_block;
-            dim3 blocks(blocks_per_seq_len, channels, batch_size);
-            dim3 threads(threads_per_block, 1, 1);
-
-            anti_alias_activation_forward<input_t, output_t, acc_t>
-                <<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(dst, src, up_ftr, down_ftr, alpha, beta, batch_size, channels, seq_len);
-        }
-    }
-}
-
-extern "C" torch::Tensor fwd_cuda(torch::Tensor const &input, torch::Tensor const &up_filter, torch::Tensor const &down_filter, torch::Tensor const &alpha, torch::Tensor const &beta)
-{
-    // Input is a 3d tensor with dimensions [batches, channels, seq_len]
-    const int batches = input.size(0);
-    const int channels = input.size(1);
-    const int seq_len = input.size(2);
-
-    // Output
-    auto act_options = input.options().requires_grad(false);
-
-    torch::Tensor anti_alias_activation_results =
-        torch::empty({batches, channels, seq_len}, act_options);
-
-    void *input_ptr = static_cast<void *>(input.data_ptr());
-    void *up_filter_ptr = static_cast<void *>(up_filter.data_ptr());
-    void *down_filter_ptr = static_cast<void *>(down_filter.data_ptr());
-    void *alpha_ptr = static_cast<void *>(alpha.data_ptr());
-    void *beta_ptr = static_cast<void *>(beta.data_ptr());
-    void *anti_alias_activation_results_ptr = static_cast<void *>(anti_alias_activation_results.data_ptr());
-
-    DISPATCH_FLOAT_HALF_AND_BFLOAT(
-        input.scalar_type(),
-        "dispatch anti alias activation_forward",
-        dispatch_anti_alias_activation_forward<scalar_t, scalar_t, float>(
-            reinterpret_cast<scalar_t *>(anti_alias_activation_results_ptr),
-            reinterpret_cast<const scalar_t *>(input_ptr),
-            reinterpret_cast<const scalar_t *>(up_filter_ptr),
-            reinterpret_cast<const scalar_t *>(down_filter_ptr),
-            reinterpret_cast<const scalar_t *>(alpha_ptr),
-            reinterpret_cast<const scalar_t *>(beta_ptr),
-            batches,
-            channels,
-            seq_len););
-    return anti_alias_activation_results;
-}

GPT_SoVITS/BigVGAN/alias_free_activation/cuda/build/_

@@ -1 +0,0 @@
-

GPT_SoVITS/BigVGAN/alias_free_activation/cuda/compat.h

@@ -1,29 +0,0 @@
-/* coding=utf-8
- * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*This code is copied fron NVIDIA apex:
- *     https://github.com/NVIDIA/apex
- *     with minor changes. */
-
-#ifndef TORCH_CHECK
-#define TORCH_CHECK AT_CHECK
-#endif
-
-#ifdef VERSION_GE_1_3
-#define DATA_PTR data_ptr
-#else
-#define DATA_PTR data
-#endif

GPT_SoVITS/BigVGAN/alias_free_activation/cuda/load.py

@@ -1,82 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-import os
-import pathlib
-import subprocess
-
-from torch.utils import cpp_extension
-
-"""
-Setting this param to a list has a problem of generating different compilation commands (with diferent order of architectures) and leading to recompilation of fused kernels. 
-Set it to empty stringo avoid recompilation and assign arch flags explicity in extra_cuda_cflags below
-"""
-os.environ["TORCH_CUDA_ARCH_LIST"] = ""
-
-
-def load():
-    # Check if cuda 11 is installed for compute capability 8.0
-    cc_flag = []
-    _, bare_metal_major, _ = _get_cuda_bare_metal_version(cpp_extension.CUDA_HOME)
-    if int(bare_metal_major) >= 11:
-        cc_flag.append("-gencode")
-        cc_flag.append("arch=compute_80,code=sm_80")
-
-    # Build path
-    srcpath = pathlib.Path(__file__).parent.absolute()
-    buildpath = srcpath / "build"
-    _create_build_dir(buildpath)
-
-    # Helper function to build the kernels.
-    def _cpp_extention_load_helper(name, sources, extra_cuda_flags):
-        return cpp_extension.load(
-            name=name,
-            sources=sources,
-            build_directory=buildpath,
-            extra_cflags=[
-                "-O3",
-            ],
-            extra_cuda_cflags=[
-                "-O3",
-                "-gencode",
-                "arch=compute_70,code=sm_70",
-                "--use_fast_math",
-            ]
-            + extra_cuda_flags
-            + cc_flag,
-            verbose=True,
-        )
-
-    extra_cuda_flags = [
-        "-U__CUDA_NO_HALF_OPERATORS__",
-        "-U__CUDA_NO_HALF_CONVERSIONS__",
-        "--expt-relaxed-constexpr",
-        "--expt-extended-lambda",
-    ]
-
-    sources = [
-        srcpath / "anti_alias_activation.cpp",
-        srcpath / "anti_alias_activation_cuda.cu",
-    ]
-    anti_alias_activation_cuda = _cpp_extention_load_helper("anti_alias_activation_cuda", sources, extra_cuda_flags)
-
-    return anti_alias_activation_cuda
-
-
-def _get_cuda_bare_metal_version(cuda_dir):
-    raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
-    output = raw_output.split()
-    release_idx = output.index("release") + 1
-    release = output[release_idx].split(".")
-    bare_metal_major = release[0]
-    bare_metal_minor = release[1][0]
-
-    return raw_output, bare_metal_major, bare_metal_minor
-
-
-def _create_build_dir(buildpath):
-    try:
-        os.mkdir(buildpath)
-    except OSError:
-        if not os.path.isdir(buildpath):
-            print(f"Creation of the build directory {buildpath} failed")

GPT_SoVITS/BigVGAN/alias_free_activation/cuda/type_shim.h

@@ -1,92 +0,0 @@
-/* coding=utf-8
- * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <ATen/ATen.h>
-#include "compat.h"
-
-#define DISPATCH_FLOAT_HALF_AND_BFLOAT(TYPE, NAME, ...)                 \
-	switch (TYPE)                                                       \
-	{                                                                   \
-	case at::ScalarType::Float:                                         \
-	{                                                                   \
-		using scalar_t = float;                                         \
-		__VA_ARGS__;                                                    \
-		break;                                                          \
-	}                                                                   \
-	case at::ScalarType::Half:                                          \
-	{                                                                   \
-		using scalar_t = at::Half;                                      \
-		__VA_ARGS__;                                                    \
-		break;                                                          \
-	}                                                                   \
-	case at::ScalarType::BFloat16:                                      \
-	{                                                                   \
-		using scalar_t = at::BFloat16;                                  \
-		__VA_ARGS__;                                                    \
-		break;                                                          \
-	}                                                                   \
-	default:                                                            \
-		AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \
-	}
-
-#define DISPATCH_FLOAT_HALF_AND_BFLOAT_INOUT_TYPES(TYPEIN, TYPEOUT, NAME, ...) \
-	switch (TYPEIN)                                                            \
-	{                                                                          \
-	case at::ScalarType::Float:                                                \
-	{                                                                          \
-		using scalar_t_in = float;                                             \
-		switch (TYPEOUT)                                                       \
-		{                                                                      \
-		case at::ScalarType::Float:                                            \
-		{                                                                      \
-			using scalar_t_out = float;                                        \
-			__VA_ARGS__;                                                       \
-			break;                                                             \
-		}                                                                      \
-		case at::ScalarType::Half:                                             \
-		{                                                                      \
-			using scalar_t_out = at::Half;                                     \
-			__VA_ARGS__;                                                       \
-			break;                                                             \
-		}                                                                      \
-		case at::ScalarType::BFloat16:                                         \
-		{                                                                      \
-			using scalar_t_out = at::BFloat16;                                 \
-			__VA_ARGS__;                                                       \
-			break;                                                             \
-		}                                                                      \
-		default:                                                               \
-			AT_ERROR(#NAME, " not implemented for '", toString(TYPEOUT), "'"); \
-		}                                                                      \
-		break;                                                                 \
-	}                                                                          \
-	case at::ScalarType::Half:                                                 \
-	{                                                                          \
-		using scalar_t_in = at::Half;                                          \
-		using scalar_t_out = at::Half;                                         \
-		__VA_ARGS__;                                                           \
-		break;                                                                 \
-	}                                                                          \
-	case at::ScalarType::BFloat16:                                             \
-	{                                                                          \
-		using scalar_t_in = at::BFloat16;                                      \
-		using scalar_t_out = at::BFloat16;                                     \
-		__VA_ARGS__;                                                           \
-		break;                                                                 \
-	}                                                                          \
-	default:                                                                   \
-		AT_ERROR(#NAME, " not implemented for '", toString(TYPEIN), "'");      \
-	}

GPT_SoVITS/BigVGAN/alias_free_activation/torch/__init__.py

@@ -1,6 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-#   LICENSE is in incl_licenses directory.
-
-from .filter import *
-from .resample import *
-from .act import *

GPT_SoVITS/BigVGAN/alias_free_activation/torch/act.py

@@ -1,30 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-#   LICENSE is in incl_licenses directory.
-
-import torch.nn as nn
-from .resample import UpSample1d, DownSample1d
-
-
-class Activation1d(nn.Module):
-    def __init__(
-        self,
-        activation,
-        up_ratio: int = 2,
-        down_ratio: int = 2,
-        up_kernel_size: int = 12,
-        down_kernel_size: int = 12,
-    ):
-        super().__init__()
-        self.up_ratio = up_ratio
-        self.down_ratio = down_ratio
-        self.act = activation
-        self.upsample = UpSample1d(up_ratio, up_kernel_size)
-        self.downsample = DownSample1d(down_ratio, down_kernel_size)
-
-    # x: [B,C,T]
-    def forward(self, x):
-        x = self.upsample(x)
-        x = self.act(x)
-        x = self.downsample(x)
-
-        return x

GPT_SoVITS/BigVGAN/alias_free_activation/torch/filter.py

@@ -1,99 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-#   LICENSE is in incl_licenses directory.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-
-if "sinc" in dir(torch):
-    sinc = torch.sinc
-else:
-    # This code is adopted from adefossez's julius.core.sinc under the MIT License
-    # https://adefossez.github.io/julius/julius/core.html
-    #   LICENSE is in incl_licenses directory.
-    def sinc(x: torch.Tensor):
-        """
-        Implementation of sinc, i.e. sin(pi * x) / (pi * x)
-        __Warning__: Different to julius.sinc, the input is multiplied by `pi`!
-        """
-        return torch.where(
-            x == 0,
-            torch.tensor(1.0, device=x.device, dtype=x.dtype),
-            torch.sin(math.pi * x) / math.pi / x,
-        )
-
-
-# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
-# https://adefossez.github.io/julius/julius/lowpass.html
-#   LICENSE is in incl_licenses directory.
-def kaiser_sinc_filter1d(cutoff, half_width, kernel_size):  # return filter [1,1,kernel_size]
-    even = kernel_size % 2 == 0
-    half_size = kernel_size // 2
-
-    # For kaiser window
-    delta_f = 4 * half_width
-    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
-    if A > 50.0:
-        beta = 0.1102 * (A - 8.7)
-    elif A >= 21.0:
-        beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)
-    else:
-        beta = 0.0
-    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
-
-    # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
-    if even:
-        time = torch.arange(-half_size, half_size) + 0.5
-    else:
-        time = torch.arange(kernel_size) - half_size
-    if cutoff == 0:
-        filter_ = torch.zeros_like(time)
-    else:
-        filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
-        """
-        Normalize filter to have sum = 1, otherwise we will have a small leakage of the constant component in the input signal.
-        """
-        filter_ /= filter_.sum()
-        filter = filter_.view(1, 1, kernel_size)
-
-    return filter
-
-
-class LowPassFilter1d(nn.Module):
-    def __init__(
-        self,
-        cutoff=0.5,
-        half_width=0.6,
-        stride: int = 1,
-        padding: bool = True,
-        padding_mode: str = "replicate",
-        kernel_size: int = 12,
-    ):
-        """
-        kernel_size should be even number for stylegan3 setup, in this implementation, odd number is also possible.
-        """
-        super().__init__()
-        if cutoff < -0.0:
-            raise ValueError("Minimum cutoff must be larger than zero.")
-        if cutoff > 0.5:
-            raise ValueError("A cutoff above 0.5 does not make sense.")
-        self.kernel_size = kernel_size
-        self.even = kernel_size % 2 == 0
-        self.pad_left = kernel_size // 2 - int(self.even)
-        self.pad_right = kernel_size // 2
-        self.stride = stride
-        self.padding = padding
-        self.padding_mode = padding_mode
-        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
-        self.register_buffer("filter", filter)
-
-    # Input [B, C, T]
-    def forward(self, x):
-        _, C, _ = x.shape
-
-        if self.padding:
-            x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
-        out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
-
-        return out

GPT_SoVITS/BigVGAN/alias_free_activation/torch/resample.py

@@ -1,48 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-#   LICENSE is in incl_licenses directory.
-
-import torch.nn as nn
-from torch.nn import functional as F
-from .filter import LowPassFilter1d
-from .filter import kaiser_sinc_filter1d
-
-
-class UpSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        self.stride = ratio
-        self.pad = self.kernel_size // ratio - 1
-        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
-        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
-        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size)
-        self.register_buffer("filter", filter)
-
-    # x: [B, C, T]
-    def forward(self, x):
-        _, C, _ = x.shape
-
-        x = F.pad(x, (self.pad, self.pad), mode="replicate")
-        x = self.ratio * F.conv_transpose1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
-        x = x[..., self.pad_left : -self.pad_right]
-
-        return x
-
-
-class DownSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        self.lowpass = LowPassFilter1d(
-            cutoff=0.5 / ratio,
-            half_width=0.6 / ratio,
-            stride=ratio,
-            kernel_size=self.kernel_size,
-        )
-
-    def forward(self, x):
-        xx = self.lowpass(x)
-
-        return xx

GPT_SoVITS/BigVGAN/bigvgan.py

@@ -1,461 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-import os
-import json
-from pathlib import Path
-from typing import Optional, Union, Dict
-
-import torch
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from . import activations
-from .utils0 import init_weights, get_padding
-from .alias_free_activation.torch.act import Activation1d as TorchActivation1d
-from .env import AttrDict
-
-from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
-
-
-def load_hparams_from_json(path) -> AttrDict:
-    with open(path) as f:
-        data = f.read()
-    return AttrDict(json.loads(data))
-
-
-class AMPBlock1(torch.nn.Module):
-    """
-    AMPBlock applies Snake / SnakeBeta activation functions with trainable parameters that control periodicity, defined for each layer.
-    AMPBlock1 has additional self.convs2 that contains additional Conv1d layers with a fixed dilation=1 followed by each layer in self.convs1
-
-    Args:
-        h (AttrDict): Hyperparameters.
-        channels (int): Number of convolution channels.
-        kernel_size (int): Size of the convolution kernel. Default is 3.
-        dilation (tuple): Dilation rates for the convolutions. Each dilation layer has two convolutions. Default is (1, 3, 5).
-        activation (str): Activation function type. Should be either 'snake' or 'snakebeta'. Default is None.
-    """
-
-    def __init__(
-        self,
-        h: AttrDict,
-        channels: int,
-        kernel_size: int = 3,
-        dilation: tuple = (1, 3, 5),
-        activation: str = None,
-    ):
-        super().__init__()
-
-        self.h = h
-
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        stride=1,
-                        dilation=d,
-                        padding=get_padding(kernel_size, d),
-                    )
-                )
-                for d in dilation
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        stride=1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                )
-                for _ in range(len(dilation))
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-        self.num_layers = len(self.convs1) + len(self.convs2)  # Total number of conv layers
-
-        # Select which Activation1d, lazy-load cuda version to ensure backward compatibility
-        if self.h.get("use_cuda_kernel", False):
-            from .alias_free_activation.cuda.activation1d import (
-                Activation1d as CudaActivation1d,
-            )
-
-            Activation1d = CudaActivation1d
-        else:
-            Activation1d = TorchActivation1d
-
-        # Activation functions
-        if activation == "snake":
-            self.activations = nn.ModuleList(
-                [
-                    Activation1d(activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
-                    for _ in range(self.num_layers)
-                ]
-            )
-        elif activation == "snakebeta":
-            self.activations = nn.ModuleList(
-                [
-                    Activation1d(activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
-                    for _ in range(self.num_layers)
-                ]
-            )
-        else:
-            raise NotImplementedError(
-                "activation incorrectly specified. check the config file and look for 'activation'."
-            )
-
-    def forward(self, x):
-        acts1, acts2 = self.activations[::2], self.activations[1::2]
-        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
-            xt = a1(x)
-            xt = c1(xt)
-            xt = a2(xt)
-            xt = c2(xt)
-            x = xt + x
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class AMPBlock2(torch.nn.Module):
-    """
-    AMPBlock applies Snake / SnakeBeta activation functions with trainable parameters that control periodicity, defined for each layer.
-    Unlike AMPBlock1, AMPBlock2 does not contain extra Conv1d layers with fixed dilation=1
-
-    Args:
-        h (AttrDict): Hyperparameters.
-        channels (int): Number of convolution channels.
-        kernel_size (int): Size of the convolution kernel. Default is 3.
-        dilation (tuple): Dilation rates for the convolutions. Each dilation layer has two convolutions. Default is (1, 3, 5).
-        activation (str): Activation function type. Should be either 'snake' or 'snakebeta'. Default is None.
-    """
-
-    def __init__(
-        self,
-        h: AttrDict,
-        channels: int,
-        kernel_size: int = 3,
-        dilation: tuple = (1, 3, 5),
-        activation: str = None,
-    ):
-        super().__init__()
-
-        self.h = h
-
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        stride=1,
-                        dilation=d,
-                        padding=get_padding(kernel_size, d),
-                    )
-                )
-                for d in dilation
-            ]
-        )
-        self.convs.apply(init_weights)
-
-        self.num_layers = len(self.convs)  # Total number of conv layers
-
-        # Select which Activation1d, lazy-load cuda version to ensure backward compatibility
-        if self.h.get("use_cuda_kernel", False):
-            from .alias_free_activation.cuda.activation1d import (
-                Activation1d as CudaActivation1d,
-            )
-
-            Activation1d = CudaActivation1d
-        else:
-            Activation1d = TorchActivation1d
-
-        # Activation functions
-        if activation == "snake":
-            self.activations = nn.ModuleList(
-                [
-                    Activation1d(activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
-                    for _ in range(self.num_layers)
-                ]
-            )
-        elif activation == "snakebeta":
-            self.activations = nn.ModuleList(
-                [
-                    Activation1d(activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
-                    for _ in range(self.num_layers)
-                ]
-            )
-        else:
-            raise NotImplementedError(
-                "activation incorrectly specified. check the config file and look for 'activation'."
-            )
-
-    def forward(self, x):
-        for c, a in zip(self.convs, self.activations):
-            xt = a(x)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class BigVGAN(
-    torch.nn.Module,
-    PyTorchModelHubMixin,
-    # library_name="bigvgan",
-    # repo_url="https://github.com/NVIDIA/BigVGAN",
-    # docs_url="https://github.com/NVIDIA/BigVGAN/blob/main/README.md",
-    # pipeline_tag="audio-to-audio",
-    # license="mit",
-    # tags=["neural-vocoder", "audio-generation", "arxiv:2206.04658"],
-):
-    """
-    BigVGAN is a neural vocoder model that applies anti-aliased periodic activation for residual blocks (resblocks).
-    New in BigVGAN-v2: it can optionally use optimized CUDA kernels for AMP (anti-aliased multi-periodicity) blocks.
-
-    Args:
-        h (AttrDict): Hyperparameters.
-        use_cuda_kernel (bool): If set to True, loads optimized CUDA kernels for AMP. This should be used for inference only, as training is not supported with CUDA kernels.
-
-    Note:
-        - The `use_cuda_kernel` parameter should be used for inference only, as training with CUDA kernels is not supported.
-        - Ensure that the activation function is correctly specified in the hyperparameters (h.activation).
-    """
-
-    def __init__(self, h: AttrDict, use_cuda_kernel: bool = False):
-        super().__init__()
-        self.h = h
-        self.h["use_cuda_kernel"] = use_cuda_kernel
-
-        # Select which Activation1d, lazy-load cuda version to ensure backward compatibility
-        if self.h.get("use_cuda_kernel", False):
-            from .alias_free_activation.cuda.activation1d import (
-                Activation1d as CudaActivation1d,
-            )
-
-            Activation1d = CudaActivation1d
-        else:
-            Activation1d = TorchActivation1d
-
-        self.num_kernels = len(h.resblock_kernel_sizes)
-        self.num_upsamples = len(h.upsample_rates)
-
-        # Pre-conv
-        self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
-
-        # Define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
-        if h.resblock == "1":
-            resblock_class = AMPBlock1
-        elif h.resblock == "2":
-            resblock_class = AMPBlock2
-        else:
-            raise ValueError(f"Incorrect resblock class specified in hyperparameters. Got {h.resblock}")
-
-        # Transposed conv-based upsamplers. does not apply anti-aliasing
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-            self.ups.append(
-                nn.ModuleList(
-                    [
-                        weight_norm(
-                            ConvTranspose1d(
-                                h.upsample_initial_channel // (2**i),
-                                h.upsample_initial_channel // (2 ** (i + 1)),
-                                k,
-                                u,
-                                padding=(k - u) // 2,
-                            )
-                        )
-                    ]
-                )
-            )
-
-        # Residual blocks using anti-aliased multi-periodicity composition modules (AMP)
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h.upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
-                self.resblocks.append(resblock_class(h, ch, k, d, activation=h.activation))
-
-        # Post-conv
-        activation_post = (
-            activations.Snake(ch, alpha_logscale=h.snake_logscale)
-            if h.activation == "snake"
-            else (activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale) if h.activation == "snakebeta" else None)
-        )
-        if activation_post is None:
-            raise NotImplementedError(
-                "activation incorrectly specified. check the config file and look for 'activation'."
-            )
-
-        self.activation_post = Activation1d(activation=activation_post)
-
-        # Whether to use bias for the final conv_post. Default to True for backward compatibility
-        self.use_bias_at_final = h.get("use_bias_at_final", True)
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3, bias=self.use_bias_at_final))
-
-        # Weight initialization
-        for i in range(len(self.ups)):
-            self.ups[i].apply(init_weights)
-        self.conv_post.apply(init_weights)
-
-        # Final tanh activation. Defaults to True for backward compatibility
-        self.use_tanh_at_final = h.get("use_tanh_at_final", True)
-
-    def forward(self, x):
-        # Pre-conv
-        x = self.conv_pre(x)
-
-        for i in range(self.num_upsamples):
-            # Upsampling
-            for i_up in range(len(self.ups[i])):
-                x = self.ups[i][i_up](x)
-            # AMP blocks
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-
-        # Post-conv
-        x = self.activation_post(x)
-        x = self.conv_post(x)
-        # Final tanh activation
-        if self.use_tanh_at_final:
-            x = torch.tanh(x)
-        else:
-            x = torch.clamp(x, min=-1.0, max=1.0)  # Bound the output to [-1, 1]
-
-        return x
-
-    def remove_weight_norm(self):
-        try:
-            # print("Removing weight norm...")
-            for l in self.ups:
-                for l_i in l:
-                    remove_weight_norm(l_i)
-            for l in self.resblocks:
-                l.remove_weight_norm()
-            remove_weight_norm(self.conv_pre)
-            remove_weight_norm(self.conv_post)
-        except ValueError:
-            print("[INFO] Model already removed weight norm. Skipping!")
-            pass
-
-    # Additional methods for huggingface_hub support
-    def _save_pretrained(self, save_directory: Path) -> None:
-        """Save weights and config.json from a Pytorch model to a local directory."""
-
-        model_path = save_directory / "bigvgan_generator.pt"
-        torch.save({"generator": self.state_dict()}, model_path)
-
-        config_path = save_directory / "config.json"
-        with open(config_path, "w") as config_file:
-            json.dump(self.h, config_file, indent=4)
-
-    @classmethod
-    def _from_pretrained(
-        cls,
-        *,
-        model_id: str,
-        revision: str,
-        cache_dir: str,
-        force_download: bool,
-        proxies: Optional[Dict],
-        resume_download: bool,
-        local_files_only: bool,
-        token: Union[str, bool, None],
-        map_location: str = "cpu",  # Additional argument
-        strict: bool = False,  # Additional argument
-        use_cuda_kernel: bool = False,
-        **model_kwargs,
-    ):
-        """Load Pytorch pretrained weights and return the loaded model."""
-
-        # Download and load hyperparameters (h) used by BigVGAN
-        if os.path.isdir(model_id):
-            # print("Loading config.json from local directory")
-            config_file = os.path.join(model_id, "config.json")
-        else:
-            config_file = hf_hub_download(
-                repo_id=model_id,
-                filename="config.json",
-                revision=revision,
-                cache_dir=cache_dir,
-                force_download=force_download,
-                proxies=proxies,
-                resume_download=resume_download,
-                token=token,
-                local_files_only=local_files_only,
-            )
-        h = load_hparams_from_json(config_file)
-
-        # instantiate BigVGAN using h
-        if use_cuda_kernel:
-            print(
-                "[WARNING] You have specified use_cuda_kernel=True during BigVGAN.from_pretrained(). Only inference is supported (training is not implemented)!"
-            )
-            print(
-                "[WARNING] You need nvcc and ninja installed in your system that matches your PyTorch build is using to build the kernel. If not, the model will fail to initialize or generate incorrect waveform!"
-            )
-            print(
-                "[WARNING] For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis"
-            )
-        model = cls(h, use_cuda_kernel=use_cuda_kernel)
-
-        # Download and load pretrained generator weight
-        if os.path.isdir(model_id):
-            # print("Loading weights from local directory")
-            model_file = os.path.join(model_id, "bigvgan_generator.pt")
-        else:
-            # print(f"Loading weights from {model_id}")
-            model_file = hf_hub_download(
-                repo_id=model_id,
-                filename="bigvgan_generator.pt",
-                revision=revision,
-                cache_dir=cache_dir,
-                force_download=force_download,
-                proxies=proxies,
-                resume_download=resume_download,
-                token=token,
-                local_files_only=local_files_only,
-            )
-
-        checkpoint_dict = torch.load(model_file, map_location=map_location)
-
-        try:
-            model.load_state_dict(checkpoint_dict["generator"])
-        except RuntimeError:
-            print(
-                "[INFO] the pretrained checkpoint does not contain weight norm. Loading the checkpoint after removing weight norm!"
-            )
-            model.remove_weight_norm()
-            model.load_state_dict(checkpoint_dict["generator"])
-
-        return model

GPT_SoVITS/BigVGAN/configs/bigvgan_22khz_80band.json

@@ -1,45 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 32,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "segment_size": 8192,
-    "num_mels": 80,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 22050,
-
-    "fmin": 0,
-    "fmax": 8000,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_24khz_100band.json

@@ -1,45 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 32,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "segment_size": 8192,
-    "num_mels": 100,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 24000,
-
-    "fmin": 0,
-    "fmax": 12000,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_base_22khz_80band.json

@@ -1,45 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 32,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [8,8,2,2],
-    "upsample_kernel_sizes": [16,16,4,4],
-    "upsample_initial_channel": 512,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "segment_size": 8192,
-    "num_mels": 80,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 22050,
-
-    "fmin": 0,
-    "fmax": 8000,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_base_24khz_100band.json

@@ -1,45 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 32,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [8,8,2,2],
-    "upsample_kernel_sizes": [16,16,4,4],
-    "upsample_initial_channel": 512,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "segment_size": 8192,
-    "num_mels": 100,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 24000,
-
-    "fmin": 0,
-    "fmax": 12000,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_v2_22khz_80band_256x.json

@@ -1,61 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 4,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "use_tanh_at_final": false,
-    "use_bias_at_final": false,
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "use_cqtd_instead_of_mrd": true,
-    "cqtd_filters": 128,
-    "cqtd_max_filters": 1024,
-    "cqtd_filters_scale": 1,
-    "cqtd_dilations": [1, 2, 4],
-    "cqtd_hop_lengths": [512, 256, 256],
-    "cqtd_n_octaves": [9, 9, 9],
-    "cqtd_bins_per_octaves": [24, 36, 48],
-
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-    
-    "use_multiscale_melloss": true,
-    "lambda_melloss": 15,
-
-    "clip_grad_norm": 500,
-
-    "segment_size": 65536,
-    "num_mels": 80,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 22050,
-
-    "fmin": 0,
-    "fmax": null,
-    "fmax_for_loss": null,
-    
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_v2_22khz_80band_fmax8k_256x.json

@@ -1,61 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 4,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "use_tanh_at_final": false,
-    "use_bias_at_final": false,
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "use_cqtd_instead_of_mrd": true,
-    "cqtd_filters": 128,
-    "cqtd_max_filters": 1024,
-    "cqtd_filters_scale": 1,
-    "cqtd_dilations": [1, 2, 4],
-    "cqtd_hop_lengths": [512, 256, 256],
-    "cqtd_n_octaves": [9, 9, 9],
-    "cqtd_bins_per_octaves": [24, 36, 48],
-    
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "use_multiscale_melloss": true,
-    "lambda_melloss": 15,
-
-    "clip_grad_norm": 500,
-
-    "segment_size": 65536,
-    "num_mels": 80,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 22050,
-
-    "fmin": 0,
-    "fmax": 8000,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_v2_24khz_100band_256x.json

@@ -1,61 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 4,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "use_tanh_at_final": false,
-    "use_bias_at_final": false,
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "use_cqtd_instead_of_mrd": true,
-    "cqtd_filters": 128,
-    "cqtd_max_filters": 1024,
-    "cqtd_filters_scale": 1,
-    "cqtd_dilations": [1, 2, 4],
-    "cqtd_hop_lengths": [512, 256, 256],
-    "cqtd_n_octaves": [9, 9, 9],
-    "cqtd_bins_per_octaves": [24, 36, 48],
-
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "use_multiscale_melloss": true,
-    "lambda_melloss": 15,
-
-    "clip_grad_norm": 500,
-
-    "segment_size": 65536,
-    "num_mels": 100,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 24000,
-
-    "fmin": 0,
-    "fmax": null,
-    "fmax_for_loss": null,
-    
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_v2_44khz_128band_256x.json

@@ -1,61 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 4,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "use_tanh_at_final": false,
-    "use_bias_at_final": false,
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "use_cqtd_instead_of_mrd": true,
-    "cqtd_filters": 128,
-    "cqtd_max_filters": 1024,
-    "cqtd_filters_scale": 1,
-    "cqtd_dilations": [1, 2, 4],
-    "cqtd_hop_lengths": [512, 256, 256],
-    "cqtd_n_octaves": [9, 9, 9],
-    "cqtd_bins_per_octaves": [24, 36, 48],
-
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "use_multiscale_melloss": true,
-    "lambda_melloss": 15,
-
-    "clip_grad_norm": 500,
-
-    "segment_size": 65536,
-    "num_mels": 128,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 44100,
-
-    "fmin": 0,
-    "fmax": null,
-    "fmax_for_loss": null,
-    
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_v2_44khz_128band_512x.json

@@ -1,61 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 4,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [8,4,2,2,2,2],
-    "upsample_kernel_sizes": [16,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "use_tanh_at_final": false,
-    "use_bias_at_final": false,
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "use_cqtd_instead_of_mrd": true,
-    "cqtd_filters": 128,
-    "cqtd_max_filters": 1024,
-    "cqtd_filters_scale": 1,
-    "cqtd_dilations": [1, 2, 4],
-    "cqtd_hop_lengths": [512, 256, 256],
-    "cqtd_n_octaves": [9, 9, 9],
-    "cqtd_bins_per_octaves": [24, 36, 48],
-
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "use_multiscale_melloss": true,
-    "lambda_melloss": 15,
-
-    "clip_grad_norm": 500,
-
-    "segment_size": 65536,
-    "num_mels": 128,
-    "num_freq": 2049,
-    "n_fft": 2048,
-    "hop_size": 512,
-    "win_size": 2048,
-
-    "sampling_rate": 44100,
-
-    "fmin": 0,
-    "fmax": null,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/discriminators.py

@@ -1,625 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv2d
-from torch.nn.utils import weight_norm, spectral_norm
-from torchaudio.transforms import Spectrogram, Resample
-
-from env import AttrDict
-from utils import get_padding
-import typing
-from typing import List, Tuple
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(
-        self,
-        h: AttrDict,
-        period: List[int],
-        kernel_size: int = 5,
-        stride: int = 3,
-        use_spectral_norm: bool = False,
-    ):
-        super().__init__()
-        self.period = period
-        self.d_mult = h.discriminator_channel_mult
-        norm_f = weight_norm if not use_spectral_norm else spectral_norm
-
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        int(32 * self.d_mult),
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        int(32 * self.d_mult),
-                        int(128 * self.d_mult),
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        int(128 * self.d_mult),
-                        int(512 * self.d_mult),
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        int(512 * self.d_mult),
-                        int(1024 * self.d_mult),
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        int(1024 * self.d_mult),
-                        int(1024 * self.d_mult),
-                        (kernel_size, 1),
-                        1,
-                        padding=(2, 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(int(1024 * self.d_mult), 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, 0.1)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, h: AttrDict):
-        super().__init__()
-        self.mpd_reshapes = h.mpd_reshapes
-        print(f"mpd_reshapes: {self.mpd_reshapes}")
-        self.discriminators = nn.ModuleList(
-            [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes]
-        )
-
-    def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor
-    ) -> Tuple[
-        List[torch.Tensor],
-        List[torch.Tensor],
-        List[List[torch.Tensor]],
-        List[List[torch.Tensor]],
-    ]:
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorR(nn.Module):
-    def __init__(self, cfg: AttrDict, resolution: List[List[int]]):
-        super().__init__()
-
-        self.resolution = resolution
-        assert len(self.resolution) == 3, f"MRD layer requires list with len=3, got {self.resolution}"
-        self.lrelu_slope = 0.1
-
-        norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm
-        if hasattr(cfg, "mrd_use_spectral_norm"):
-            print(f"[INFO] overriding MRD use_spectral_norm as {cfg.mrd_use_spectral_norm}")
-            norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm
-        self.d_mult = cfg.discriminator_channel_mult
-        if hasattr(cfg, "mrd_channel_mult"):
-            print(f"[INFO] overriding mrd channel multiplier as {cfg.mrd_channel_mult}")
-            self.d_mult = cfg.mrd_channel_mult
-
-        self.convs = nn.ModuleList(
-            [
-                norm_f(nn.Conv2d(1, int(32 * self.d_mult), (3, 9), padding=(1, 4))),
-                norm_f(
-                    nn.Conv2d(
-                        int(32 * self.d_mult),
-                        int(32 * self.d_mult),
-                        (3, 9),
-                        stride=(1, 2),
-                        padding=(1, 4),
-                    )
-                ),
-                norm_f(
-                    nn.Conv2d(
-                        int(32 * self.d_mult),
-                        int(32 * self.d_mult),
-                        (3, 9),
-                        stride=(1, 2),
-                        padding=(1, 4),
-                    )
-                ),
-                norm_f(
-                    nn.Conv2d(
-                        int(32 * self.d_mult),
-                        int(32 * self.d_mult),
-                        (3, 9),
-                        stride=(1, 2),
-                        padding=(1, 4),
-                    )
-                ),
-                norm_f(
-                    nn.Conv2d(
-                        int(32 * self.d_mult),
-                        int(32 * self.d_mult),
-                        (3, 3),
-                        padding=(1, 1),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1)))
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        fmap = []
-
-        x = self.spectrogram(x)
-        x = x.unsqueeze(1)
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, self.lrelu_slope)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-    def spectrogram(self, x: torch.Tensor) -> torch.Tensor:
-        n_fft, hop_length, win_length = self.resolution
-        x = F.pad(
-            x,
-            (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)),
-            mode="reflect",
-        )
-        x = x.squeeze(1)
-        x = torch.stft(
-            x,
-            n_fft=n_fft,
-            hop_length=hop_length,
-            win_length=win_length,
-            center=False,
-            return_complex=True,
-        )
-        x = torch.view_as_real(x)  # [B, F, TT, 2]
-        mag = torch.norm(x, p=2, dim=-1)  # [B, F, TT]
-
-        return mag
-
-
-class MultiResolutionDiscriminator(nn.Module):
-    def __init__(self, cfg, debug=False):
-        super().__init__()
-        self.resolutions = cfg.resolutions
-        assert len(self.resolutions) == 3, (
-            f"MRD requires list of list with len=3, each element having a list with len=3. Got {self.resolutions}"
-        )
-        self.discriminators = nn.ModuleList([DiscriminatorR(cfg, resolution) for resolution in self.resolutions])
-
-    def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor
-    ) -> Tuple[
-        List[torch.Tensor],
-        List[torch.Tensor],
-        List[List[torch.Tensor]],
-        List[List[torch.Tensor]],
-    ]:
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(x=y)
-            y_d_g, fmap_g = d(x=y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-# Method based on descript-audio-codec: https://github.com/descriptinc/descript-audio-codec
-# Modified code adapted from https://github.com/gemelo-ai/vocos under the MIT license.
-#   LICENSE is in incl_licenses directory.
-class DiscriminatorB(nn.Module):
-    def __init__(
-        self,
-        window_length: int,
-        channels: int = 32,
-        hop_factor: float = 0.25,
-        bands: Tuple[Tuple[float, float], ...] = (
-            (0.0, 0.1),
-            (0.1, 0.25),
-            (0.25, 0.5),
-            (0.5, 0.75),
-            (0.75, 1.0),
-        ),
-    ):
-        super().__init__()
-        self.window_length = window_length
-        self.hop_factor = hop_factor
-        self.spec_fn = Spectrogram(
-            n_fft=window_length,
-            hop_length=int(window_length * hop_factor),
-            win_length=window_length,
-            power=None,
-        )
-        n_fft = window_length // 2 + 1
-        bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
-        self.bands = bands
-        convs = lambda: nn.ModuleList(
-            [
-                weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))),
-                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
-                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
-                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
-                weight_norm(nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))),
-            ]
-        )
-        self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
-
-        self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1)))
-
-    def spectrogram(self, x: torch.Tensor) -> List[torch.Tensor]:
-        # Remove DC offset
-        x = x - x.mean(dim=-1, keepdims=True)
-        # Peak normalize the volume of input audio
-        x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
-        x = self.spec_fn(x)
-        x = torch.view_as_real(x)
-        x = x.permute(0, 3, 2, 1)  # [B, F, T, C] -> [B, C, T, F]
-        # Split into bands
-        x_bands = [x[..., b[0] : b[1]] for b in self.bands]
-        return x_bands
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        x_bands = self.spectrogram(x.squeeze(1))
-        fmap = []
-        x = []
-
-        for band, stack in zip(x_bands, self.band_convs):
-            for i, layer in enumerate(stack):
-                band = layer(band)
-                band = torch.nn.functional.leaky_relu(band, 0.1)
-                if i > 0:
-                    fmap.append(band)
-            x.append(band)
-
-        x = torch.cat(x, dim=-1)
-        x = self.conv_post(x)
-        fmap.append(x)
-
-        return x, fmap
-
-
-# Method based on descript-audio-codec: https://github.com/descriptinc/descript-audio-codec
-# Modified code adapted from https://github.com/gemelo-ai/vocos under the MIT license.
-#   LICENSE is in incl_licenses directory.
-class MultiBandDiscriminator(nn.Module):
-    def __init__(
-        self,
-        h,
-    ):
-        """
-        Multi-band multi-scale STFT discriminator, with the architecture based on https://github.com/descriptinc/descript-audio-codec.
-        and the modified code adapted from https://github.com/gemelo-ai/vocos.
-        """
-        super().__init__()
-        # fft_sizes (list[int]): Tuple of window lengths for FFT. Defaults to [2048, 1024, 512] if not set in h.
-        self.fft_sizes = h.get("mbd_fft_sizes", [2048, 1024, 512])
-        self.discriminators = nn.ModuleList([DiscriminatorB(window_length=w) for w in self.fft_sizes])
-
-    def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor
-    ) -> Tuple[
-        List[torch.Tensor],
-        List[torch.Tensor],
-        List[List[torch.Tensor]],
-        List[List[torch.Tensor]],
-    ]:
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-
-        for d in self.discriminators:
-            y_d_r, fmap_r = d(x=y)
-            y_d_g, fmap_g = d(x=y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-# Adapted from https://github.com/open-mmlab/Amphion/blob/main/models/vocoders/gan/discriminator/mssbcqtd.py under the MIT license.
-#   LICENSE is in incl_licenses directory.
-class DiscriminatorCQT(nn.Module):
-    def __init__(self, cfg: AttrDict, hop_length: int, n_octaves: int, bins_per_octave: int):
-        super().__init__()
-        self.cfg = cfg
-
-        self.filters = cfg["cqtd_filters"]
-        self.max_filters = cfg["cqtd_max_filters"]
-        self.filters_scale = cfg["cqtd_filters_scale"]
-        self.kernel_size = (3, 9)
-        self.dilations = cfg["cqtd_dilations"]
-        self.stride = (1, 2)
-
-        self.in_channels = cfg["cqtd_in_channels"]
-        self.out_channels = cfg["cqtd_out_channels"]
-        self.fs = cfg["sampling_rate"]
-        self.hop_length = hop_length
-        self.n_octaves = n_octaves
-        self.bins_per_octave = bins_per_octave
-
-        # Lazy-load
-        from nnAudio import features
-
-        self.cqt_transform = features.cqt.CQT2010v2(
-            sr=self.fs * 2,
-            hop_length=self.hop_length,
-            n_bins=self.bins_per_octave * self.n_octaves,
-            bins_per_octave=self.bins_per_octave,
-            output_format="Complex",
-            pad_mode="constant",
-        )
-
-        self.conv_pres = nn.ModuleList()
-        for _ in range(self.n_octaves):
-            self.conv_pres.append(
-                nn.Conv2d(
-                    self.in_channels * 2,
-                    self.in_channels * 2,
-                    kernel_size=self.kernel_size,
-                    padding=self.get_2d_padding(self.kernel_size),
-                )
-            )
-
-        self.convs = nn.ModuleList()
-
-        self.convs.append(
-            nn.Conv2d(
-                self.in_channels * 2,
-                self.filters,
-                kernel_size=self.kernel_size,
-                padding=self.get_2d_padding(self.kernel_size),
-            )
-        )
-
-        in_chs = min(self.filters_scale * self.filters, self.max_filters)
-        for i, dilation in enumerate(self.dilations):
-            out_chs = min((self.filters_scale ** (i + 1)) * self.filters, self.max_filters)
-            self.convs.append(
-                weight_norm(
-                    nn.Conv2d(
-                        in_chs,
-                        out_chs,
-                        kernel_size=self.kernel_size,
-                        stride=self.stride,
-                        dilation=(dilation, 1),
-                        padding=self.get_2d_padding(self.kernel_size, (dilation, 1)),
-                    )
-                )
-            )
-            in_chs = out_chs
-        out_chs = min(
-            (self.filters_scale ** (len(self.dilations) + 1)) * self.filters,
-            self.max_filters,
-        )
-        self.convs.append(
-            weight_norm(
-                nn.Conv2d(
-                    in_chs,
-                    out_chs,
-                    kernel_size=(self.kernel_size[0], self.kernel_size[0]),
-                    padding=self.get_2d_padding((self.kernel_size[0], self.kernel_size[0])),
-                )
-            )
-        )
-
-        self.conv_post = weight_norm(
-            nn.Conv2d(
-                out_chs,
-                self.out_channels,
-                kernel_size=(self.kernel_size[0], self.kernel_size[0]),
-                padding=self.get_2d_padding((self.kernel_size[0], self.kernel_size[0])),
-            )
-        )
-
-        self.activation = torch.nn.LeakyReLU(negative_slope=0.1)
-        self.resample = Resample(orig_freq=self.fs, new_freq=self.fs * 2)
-
-        self.cqtd_normalize_volume = self.cfg.get("cqtd_normalize_volume", False)
-        if self.cqtd_normalize_volume:
-            print(
-                "[INFO] cqtd_normalize_volume set to True. Will apply DC offset removal & peak volume normalization in CQTD!"
-            )
-
-    def get_2d_padding(
-        self,
-        kernel_size: typing.Tuple[int, int],
-        dilation: typing.Tuple[int, int] = (1, 1),
-    ):
-        return (
-            ((kernel_size[0] - 1) * dilation[0]) // 2,
-            ((kernel_size[1] - 1) * dilation[1]) // 2,
-        )
-
-    def forward(self, x: torch.tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        fmap = []
-
-        if self.cqtd_normalize_volume:
-            # Remove DC offset
-            x = x - x.mean(dim=-1, keepdims=True)
-            # Peak normalize the volume of input audio
-            x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
-
-        x = self.resample(x)
-
-        z = self.cqt_transform(x)
-
-        z_amplitude = z[:, :, :, 0].unsqueeze(1)
-        z_phase = z[:, :, :, 1].unsqueeze(1)
-
-        z = torch.cat([z_amplitude, z_phase], dim=1)
-        z = torch.permute(z, (0, 1, 3, 2))  # [B, C, W, T] -> [B, C, T, W]
-
-        latent_z = []
-        for i in range(self.n_octaves):
-            latent_z.append(
-                self.conv_pres[i](
-                    z[
-                        :,
-                        :,
-                        :,
-                        i * self.bins_per_octave : (i + 1) * self.bins_per_octave,
-                    ]
-                )
-            )
-        latent_z = torch.cat(latent_z, dim=-1)
-
-        for i, l in enumerate(self.convs):
-            latent_z = l(latent_z)
-
-            latent_z = self.activation(latent_z)
-            fmap.append(latent_z)
-
-        latent_z = self.conv_post(latent_z)
-
-        return latent_z, fmap
-
-
-class MultiScaleSubbandCQTDiscriminator(nn.Module):
-    def __init__(self, cfg: AttrDict):
-        super().__init__()
-
-        self.cfg = cfg
-        # Using get with defaults
-        self.cfg["cqtd_filters"] = self.cfg.get("cqtd_filters", 32)
-        self.cfg["cqtd_max_filters"] = self.cfg.get("cqtd_max_filters", 1024)
-        self.cfg["cqtd_filters_scale"] = self.cfg.get("cqtd_filters_scale", 1)
-        self.cfg["cqtd_dilations"] = self.cfg.get("cqtd_dilations", [1, 2, 4])
-        self.cfg["cqtd_in_channels"] = self.cfg.get("cqtd_in_channels", 1)
-        self.cfg["cqtd_out_channels"] = self.cfg.get("cqtd_out_channels", 1)
-        # Multi-scale params to loop over
-        self.cfg["cqtd_hop_lengths"] = self.cfg.get("cqtd_hop_lengths", [512, 256, 256])
-        self.cfg["cqtd_n_octaves"] = self.cfg.get("cqtd_n_octaves", [9, 9, 9])
-        self.cfg["cqtd_bins_per_octaves"] = self.cfg.get("cqtd_bins_per_octaves", [24, 36, 48])
-
-        self.discriminators = nn.ModuleList(
-            [
-                DiscriminatorCQT(
-                    self.cfg,
-                    hop_length=self.cfg["cqtd_hop_lengths"][i],
-                    n_octaves=self.cfg["cqtd_n_octaves"][i],
-                    bins_per_octave=self.cfg["cqtd_bins_per_octaves"][i],
-                )
-                for i in range(len(self.cfg["cqtd_hop_lengths"]))
-            ]
-        )
-
-    def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor
-    ) -> Tuple[
-        List[torch.Tensor],
-        List[torch.Tensor],
-        List[List[torch.Tensor]],
-        List[List[torch.Tensor]],
-    ]:
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-
-        for disc in self.discriminators:
-            y_d_r, fmap_r = disc(y)
-            y_d_g, fmap_g = disc(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class CombinedDiscriminator(nn.Module):
-    """
-    Wrapper of chaining multiple discrimiantor architectures.
-    Example: combine mbd and cqtd as a single class
-    """
-
-    def __init__(self, list_discriminator: List[nn.Module]):
-        super().__init__()
-        self.discrimiantor = nn.ModuleList(list_discriminator)
-
-    def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor
-    ) -> Tuple[
-        List[torch.Tensor],
-        List[torch.Tensor],
-        List[List[torch.Tensor]],
-        List[List[torch.Tensor]],
-    ]:
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-
-        for disc in self.discrimiantor:
-            y_d_r, y_d_g, fmap_r, fmap_g = disc(y, y_hat)
-            y_d_rs.extend(y_d_r)
-            fmap_rs.extend(fmap_r)
-            y_d_gs.extend(y_d_g)
-            fmap_gs.extend(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs

GPT_SoVITS/BigVGAN/env.py

@@ -1,18 +0,0 @@
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-import os
-import shutil
-
-
-class AttrDict(dict):
-    def __init__(self, *args, **kwargs):
-        super(AttrDict, self).__init__(*args, **kwargs)
-        self.__dict__ = self
-
-
-def build_env(config, config_name, path):
-    t_path = os.path.join(path, config_name)
-    if config != t_path:
-        os.makedirs(path, exist_ok=True)
-        shutil.copyfile(config, os.path.join(path, config_name))

GPT_SoVITS/BigVGAN/incl_licenses/LICENSE_1

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2020 Jungil Kong
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

GPT_SoVITS/BigVGAN/incl_licenses/LICENSE_2

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2020 Edward Dixon
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

GPT_SoVITS/BigVGAN/incl_licenses/LICENSE_3

@@ -1,201 +0,0 @@
-                                Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
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-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
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-      to that Work or Derivative Works thereof, that is intentionally
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GPT_SoVITS/BigVGAN/incl_licenses/LICENSE_4

@@ -1,29 +0,0 @@
-BSD 3-Clause License
-
-Copyright (c) 2019, Seungwon Park 박승원
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
-
-3. Neither the name of the copyright holder nor the names of its
-   contributors may be used to endorse or promote products derived from
-   this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

GPT_SoVITS/BigVGAN/incl_licenses/LICENSE_5

@@ -1,16 +0,0 @@
-Copyright 2020 Alexandre Défossez
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-associated documentation files (the "Software"), to deal in the Software without restriction,
-including without limitation the rights to use, copy, modify, merge, publish, distribute,
-sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all copies or
-substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

GPT_SoVITS/BigVGAN/incl_licenses/LICENSE_6

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2023-present, Descript
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

GPT_SoVITS/BigVGAN/incl_licenses/LICENSE_7

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2023 Charactr Inc.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

GPT_SoVITS/BigVGAN/incl_licenses/LICENSE_8

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2023 Amphion
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

GPT_SoVITS/BigVGAN/inference.py

@@ -1,85 +0,0 @@
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import os
-import argparse
-import json
-import torch
-import librosa
-from utils import load_checkpoint
-from meldataset import get_mel_spectrogram
-from scipy.io.wavfile import write
-from env import AttrDict
-from meldataset import MAX_WAV_VALUE
-from bigvgan import BigVGAN as Generator
-
-h = None
-device = None
-torch.backends.cudnn.benchmark = False
-
-
-def inference(a, h):
-    generator = Generator(h, use_cuda_kernel=a.use_cuda_kernel).to(device)
-
-    state_dict_g = load_checkpoint(a.checkpoint_file, device)
-    generator.load_state_dict(state_dict_g["generator"])
-
-    filelist = os.listdir(a.input_wavs_dir)
-
-    os.makedirs(a.output_dir, exist_ok=True)
-
-    generator.eval()
-    generator.remove_weight_norm()
-    with torch.no_grad():
-        for i, filname in enumerate(filelist):
-            # Load the ground truth audio and resample if necessary
-            wav, sr = librosa.load(os.path.join(a.input_wavs_dir, filname), sr=h.sampling_rate, mono=True)
-            wav = torch.FloatTensor(wav).to(device)
-            # Compute mel spectrogram from the ground truth audio
-            x = get_mel_spectrogram(wav.unsqueeze(0), generator.h)
-
-            y_g_hat = generator(x)
-
-            audio = y_g_hat.squeeze()
-            audio = audio * MAX_WAV_VALUE
-            audio = audio.cpu().numpy().astype("int16")
-
-            output_file = os.path.join(a.output_dir, os.path.splitext(filname)[0] + "_generated.wav")
-            write(output_file, h.sampling_rate, audio)
-            print(output_file)
-
-
-def main():
-    print("Initializing Inference Process..")
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--input_wavs_dir", default="test_files")
-    parser.add_argument("--output_dir", default="generated_files")
-    parser.add_argument("--checkpoint_file", required=True)
-    parser.add_argument("--use_cuda_kernel", action="store_true", default=False)
-
-    a = parser.parse_args()
-
-    config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json")
-    with open(config_file) as f:
-        data = f.read()
-
-    global h
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-
-    torch.manual_seed(h.seed)
-    global device
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(h.seed)
-        device = torch.device("cuda")
-    else:
-        device = torch.device("cpu")
-
-    inference(a, h)
-
-
-if __name__ == "__main__":
-    main()

GPT_SoVITS/BigVGAN/inference_e2e.py

@@ -1,100 +0,0 @@
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import glob
-import os
-import numpy as np
-import argparse
-import json
-import torch
-from scipy.io.wavfile import write
-from env import AttrDict
-from meldataset import MAX_WAV_VALUE
-from bigvgan import BigVGAN as Generator
-
-h = None
-device = None
-torch.backends.cudnn.benchmark = False
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print(f"Loading '{filepath}'")
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + "*")
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return ""
-    return sorted(cp_list)[-1]
-
-
-def inference(a, h):
-    generator = Generator(h, use_cuda_kernel=a.use_cuda_kernel).to(device)
-
-    state_dict_g = load_checkpoint(a.checkpoint_file, device)
-    generator.load_state_dict(state_dict_g["generator"])
-
-    filelist = os.listdir(a.input_mels_dir)
-
-    os.makedirs(a.output_dir, exist_ok=True)
-
-    generator.eval()
-    generator.remove_weight_norm()
-    with torch.no_grad():
-        for i, filname in enumerate(filelist):
-            # Load the mel spectrogram in .npy format
-            x = np.load(os.path.join(a.input_mels_dir, filname))
-            x = torch.FloatTensor(x).to(device)
-            if len(x.shape) == 2:
-                x = x.unsqueeze(0)
-
-            y_g_hat = generator(x)
-
-            audio = y_g_hat.squeeze()
-            audio = audio * MAX_WAV_VALUE
-            audio = audio.cpu().numpy().astype("int16")
-
-            output_file = os.path.join(a.output_dir, os.path.splitext(filname)[0] + "_generated_e2e.wav")
-            write(output_file, h.sampling_rate, audio)
-            print(output_file)
-
-
-def main():
-    print("Initializing Inference Process..")
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--input_mels_dir", default="test_mel_files")
-    parser.add_argument("--output_dir", default="generated_files_from_mel")
-    parser.add_argument("--checkpoint_file", required=True)
-    parser.add_argument("--use_cuda_kernel", action="store_true", default=False)
-
-    a = parser.parse_args()
-
-    config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json")
-    with open(config_file) as f:
-        data = f.read()
-
-    global h
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-
-    torch.manual_seed(h.seed)
-    global device
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(h.seed)
-        device = torch.device("cuda")
-    else:
-        device = torch.device("cpu")
-
-    inference(a, h)
-
-
-if __name__ == "__main__":
-    main()

GPT_SoVITS/BigVGAN/loss.py

@@ -1,238 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-
-import torch
-import torch.nn as nn
-from librosa.filters import mel as librosa_mel_fn
-from scipy import signal
-
-import typing
-from typing import List, Tuple
-from collections import namedtuple
-import math
-import functools
-
-
-# Adapted from https://github.com/descriptinc/descript-audio-codec/blob/main/dac/nn/loss.py under the MIT license.
-#   LICENSE is in incl_licenses directory.
-class MultiScaleMelSpectrogramLoss(nn.Module):
-    """Compute distance between mel spectrograms. Can be used
-    in a multi-scale way.
-
-    Parameters
-    ----------
-    n_mels : List[int]
-        Number of mels per STFT, by default [5, 10, 20, 40, 80, 160, 320],
-    window_lengths : List[int], optional
-        Length of each window of each STFT, by default [32, 64, 128, 256, 512, 1024, 2048]
-    loss_fn : typing.Callable, optional
-        How to compare each loss, by default nn.L1Loss()
-    clamp_eps : float, optional
-        Clamp on the log magnitude, below, by default 1e-5
-    mag_weight : float, optional
-        Weight of raw magnitude portion of loss, by default 0.0 (no ampliciation on mag part)
-    log_weight : float, optional
-        Weight of log magnitude portion of loss, by default 1.0
-    pow : float, optional
-        Power to raise magnitude to before taking log, by default 1.0
-    weight : float, optional
-        Weight of this loss, by default 1.0
-    match_stride : bool, optional
-        Whether to match the stride of convolutional layers, by default False
-
-    Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py
-    Additional code copied and modified from https://github.com/descriptinc/audiotools/blob/master/audiotools/core/audio_signal.py
-    """
-
-    def __init__(
-        self,
-        sampling_rate: int,
-        n_mels: List[int] = [5, 10, 20, 40, 80, 160, 320],
-        window_lengths: List[int] = [32, 64, 128, 256, 512, 1024, 2048],
-        loss_fn: typing.Callable = nn.L1Loss(),
-        clamp_eps: float = 1e-5,
-        mag_weight: float = 0.0,
-        log_weight: float = 1.0,
-        pow: float = 1.0,
-        weight: float = 1.0,
-        match_stride: bool = False,
-        mel_fmin: List[float] = [0, 0, 0, 0, 0, 0, 0],
-        mel_fmax: List[float] = [None, None, None, None, None, None, None],
-        window_type: str = "hann",
-    ):
-        super().__init__()
-        self.sampling_rate = sampling_rate
-
-        STFTParams = namedtuple(
-            "STFTParams",
-            ["window_length", "hop_length", "window_type", "match_stride"],
-        )
-
-        self.stft_params = [
-            STFTParams(
-                window_length=w,
-                hop_length=w // 4,
-                match_stride=match_stride,
-                window_type=window_type,
-            )
-            for w in window_lengths
-        ]
-        self.n_mels = n_mels
-        self.loss_fn = loss_fn
-        self.clamp_eps = clamp_eps
-        self.log_weight = log_weight
-        self.mag_weight = mag_weight
-        self.weight = weight
-        self.mel_fmin = mel_fmin
-        self.mel_fmax = mel_fmax
-        self.pow = pow
-
-    @staticmethod
-    @functools.lru_cache(None)
-    def get_window(
-        window_type,
-        window_length,
-    ):
-        return signal.get_window(window_type, window_length)
-
-    @staticmethod
-    @functools.lru_cache(None)
-    def get_mel_filters(sr, n_fft, n_mels, fmin, fmax):
-        return librosa_mel_fn(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
-
-    def mel_spectrogram(
-        self,
-        wav,
-        n_mels,
-        fmin,
-        fmax,
-        window_length,
-        hop_length,
-        match_stride,
-        window_type,
-    ):
-        """
-        Mirrors AudioSignal.mel_spectrogram used by BigVGAN-v2 training from:
-        https://github.com/descriptinc/audiotools/blob/master/audiotools/core/audio_signal.py
-        """
-        B, C, T = wav.shape
-
-        if match_stride:
-            assert hop_length == window_length // 4, "For match_stride, hop must equal n_fft // 4"
-            right_pad = math.ceil(T / hop_length) * hop_length - T
-            pad = (window_length - hop_length) // 2
-        else:
-            right_pad = 0
-            pad = 0
-
-        wav = torch.nn.functional.pad(wav, (pad, pad + right_pad), mode="reflect")
-
-        window = self.get_window(window_type, window_length)
-        window = torch.from_numpy(window).to(wav.device).float()
-
-        stft = torch.stft(
-            wav.reshape(-1, T),
-            n_fft=window_length,
-            hop_length=hop_length,
-            window=window,
-            return_complex=True,
-            center=True,
-        )
-        _, nf, nt = stft.shape
-        stft = stft.reshape(B, C, nf, nt)
-        if match_stride:
-            """
-            Drop first two and last two frames, which are added, because of padding. Now num_frames * hop_length = num_samples.
-            """
-            stft = stft[..., 2:-2]
-        magnitude = torch.abs(stft)
-
-        nf = magnitude.shape[2]
-        mel_basis = self.get_mel_filters(self.sampling_rate, 2 * (nf - 1), n_mels, fmin, fmax)
-        mel_basis = torch.from_numpy(mel_basis).to(wav.device)
-        mel_spectrogram = magnitude.transpose(2, -1) @ mel_basis.T
-        mel_spectrogram = mel_spectrogram.transpose(-1, 2)
-
-        return mel_spectrogram
-
-    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-        """Computes mel loss between an estimate and a reference
-        signal.
-
-        Parameters
-        ----------
-        x : torch.Tensor
-            Estimate signal
-        y : torch.Tensor
-            Reference signal
-
-        Returns
-        -------
-        torch.Tensor
-            Mel loss.
-        """
-
-        loss = 0.0
-        for n_mels, fmin, fmax, s in zip(self.n_mels, self.mel_fmin, self.mel_fmax, self.stft_params):
-            kwargs = {
-                "n_mels": n_mels,
-                "fmin": fmin,
-                "fmax": fmax,
-                "window_length": s.window_length,
-                "hop_length": s.hop_length,
-                "match_stride": s.match_stride,
-                "window_type": s.window_type,
-            }
-
-            x_mels = self.mel_spectrogram(x, **kwargs)
-            y_mels = self.mel_spectrogram(y, **kwargs)
-            x_logmels = torch.log(x_mels.clamp(min=self.clamp_eps).pow(self.pow)) / torch.log(torch.tensor(10.0))
-            y_logmels = torch.log(y_mels.clamp(min=self.clamp_eps).pow(self.pow)) / torch.log(torch.tensor(10.0))
-
-            loss += self.log_weight * self.loss_fn(x_logmels, y_logmels)
-            loss += self.mag_weight * self.loss_fn(x_logmels, y_logmels)
-
-        return loss
-
-
-# Loss functions
-def feature_loss(fmap_r: List[List[torch.Tensor]], fmap_g: List[List[torch.Tensor]]) -> torch.Tensor:
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2  # This equates to lambda=2.0 for the feature matching loss
-
-
-def discriminator_loss(
-    disc_real_outputs: List[torch.Tensor], disc_generated_outputs: List[torch.Tensor]
-) -> Tuple[torch.Tensor, List[torch.Tensor], List[torch.Tensor]]:
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg**2)
-        loss += r_loss + g_loss
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(
-    disc_outputs: List[torch.Tensor],
-) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses

GPT_SoVITS/BigVGAN/meldataset.py

@@ -1,370 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-import math
-import os
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.filters import mel as librosa_mel_fn
-import pathlib
-from tqdm import tqdm
-from typing import List, Tuple, Optional
-from .env import AttrDict
-
-MAX_WAV_VALUE = 32767.0  # NOTE: 32768.0 -1 to prevent int16 overflow (results in popping sound in corner cases)
-
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-
-def dynamic_range_decompression(x, C=1):
-    return np.exp(x) / C
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    return dynamic_range_compression_torch(magnitudes)
-
-
-def spectral_de_normalize_torch(magnitudes):
-    return dynamic_range_decompression_torch(magnitudes)
-
-
-mel_basis_cache = {}
-hann_window_cache = {}
-
-
-def mel_spectrogram(
-    y: torch.Tensor,
-    n_fft: int,
-    num_mels: int,
-    sampling_rate: int,
-    hop_size: int,
-    win_size: int,
-    fmin: int,
-    fmax: int = None,
-    center: bool = False,
-) -> torch.Tensor:
-    """
-    Calculate the mel spectrogram of an input signal.
-    This function uses slaney norm for the librosa mel filterbank (using librosa.filters.mel) and uses Hann window for STFT (using torch.stft).
-
-    Args:
-        y (torch.Tensor): Input signal.
-        n_fft (int): FFT size.
-        num_mels (int): Number of mel bins.
-        sampling_rate (int): Sampling rate of the input signal.
-        hop_size (int): Hop size for STFT.
-        win_size (int): Window size for STFT.
-        fmin (int): Minimum frequency for mel filterbank.
-        fmax (int): Maximum frequency for mel filterbank. If None, defaults to half the sampling rate (fmax = sr / 2.0) inside librosa_mel_fn
-        center (bool): Whether to pad the input to center the frames. Default is False.
-
-    Returns:
-        torch.Tensor: Mel spectrogram.
-    """
-    if torch.min(y) < -1.0:
-        print(f"[WARNING] Min value of input waveform signal is {torch.min(y)}")
-    if torch.max(y) > 1.0:
-        print(f"[WARNING] Max value of input waveform signal is {torch.max(y)}")
-
-    device = y.device
-    key = f"{n_fft}_{num_mels}_{sampling_rate}_{hop_size}_{win_size}_{fmin}_{fmax}_{device}"
-
-    if key not in mel_basis_cache:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis_cache[key] = torch.from_numpy(mel).float().to(device)
-        hann_window_cache[key] = torch.hann_window(win_size).to(device)
-
-    mel_basis = mel_basis_cache[key]
-    hann_window = hann_window_cache[key]
-
-    padding = (n_fft - hop_size) // 2
-    y = torch.nn.functional.pad(y.unsqueeze(1), (padding, padding), mode="reflect").squeeze(1)
-
-    spec = torch.stft(
-        y,
-        n_fft,
-        hop_length=hop_size,
-        win_length=win_size,
-        window=hann_window,
-        center=center,
-        pad_mode="reflect",
-        normalized=False,
-        onesided=True,
-        return_complex=True,
-    )
-    spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)
-
-    mel_spec = torch.matmul(mel_basis, spec)
-    mel_spec = spectral_normalize_torch(mel_spec)
-
-    return mel_spec
-
-
-def get_mel_spectrogram(wav, h):
-    """
-    Generate mel spectrogram from a waveform using given hyperparameters.
-
-    Args:
-        wav (torch.Tensor): Input waveform.
-        h: Hyperparameters object with attributes n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax.
-
-    Returns:
-        torch.Tensor: Mel spectrogram.
-    """
-    return mel_spectrogram(
-        wav,
-        h.n_fft,
-        h.num_mels,
-        h.sampling_rate,
-        h.hop_size,
-        h.win_size,
-        h.fmin,
-        h.fmax,
-    )
-
-
-def get_dataset_filelist(a):
-    training_files = []
-    validation_files = []
-    list_unseen_validation_files = []
-
-    with open(a.input_training_file, "r", encoding="utf-8") as fi:
-        training_files = [
-            os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0
-        ]
-        print(f"first training file: {training_files[0]}")
-
-    with open(a.input_validation_file, "r", encoding="utf-8") as fi:
-        validation_files = [
-            os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0
-        ]
-        print(f"first validation file: {validation_files[0]}")
-
-    for i in range(len(a.list_input_unseen_validation_file)):
-        with open(a.list_input_unseen_validation_file[i], "r", encoding="utf-8") as fi:
-            unseen_validation_files = [
-                os.path.join(a.list_input_unseen_wavs_dir[i], x.split("|")[0] + ".wav")
-                for x in fi.read().split("\n")
-                if len(x) > 0
-            ]
-            print(f"first unseen {i}th validation fileset: {unseen_validation_files[0]}")
-            list_unseen_validation_files.append(unseen_validation_files)
-
-    return training_files, validation_files, list_unseen_validation_files
-
-
-class MelDataset(torch.utils.data.Dataset):
-    def __init__(
-        self,
-        training_files: List[str],
-        hparams: AttrDict,
-        segment_size: int,
-        n_fft: int,
-        num_mels: int,
-        hop_size: int,
-        win_size: int,
-        sampling_rate: int,
-        fmin: int,
-        fmax: Optional[int],
-        split: bool = True,
-        shuffle: bool = True,
-        device: str = None,
-        fmax_loss: Optional[int] = None,
-        fine_tuning: bool = False,
-        base_mels_path: str = None,
-        is_seen: bool = True,
-    ):
-        self.audio_files = training_files
-        random.seed(1234)
-        if shuffle:
-            random.shuffle(self.audio_files)
-        self.hparams = hparams
-        self.is_seen = is_seen
-        if self.is_seen:
-            self.name = pathlib.Path(self.audio_files[0]).parts[0]
-        else:
-            self.name = "-".join(pathlib.Path(self.audio_files[0]).parts[:2]).strip("/")
-
-        self.segment_size = segment_size
-        self.sampling_rate = sampling_rate
-        self.split = split
-        self.n_fft = n_fft
-        self.num_mels = num_mels
-        self.hop_size = hop_size
-        self.win_size = win_size
-        self.fmin = fmin
-        self.fmax = fmax
-        self.fmax_loss = fmax_loss
-        self.device = device
-        self.fine_tuning = fine_tuning
-        self.base_mels_path = base_mels_path
-
-        print("[INFO] checking dataset integrity...")
-        for i in tqdm(range(len(self.audio_files))):
-            assert os.path.exists(self.audio_files[i]), f"{self.audio_files[i]} not found"
-
-    def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor, str, torch.Tensor]:
-        try:
-            filename = self.audio_files[index]
-
-            # Use librosa.load that ensures loading waveform into mono with [-1, 1] float values
-            # Audio is ndarray with shape [T_time]. Disable auto-resampling here to minimize overhead
-            # The on-the-fly resampling during training will be done only for the obtained random chunk
-            audio, source_sampling_rate = librosa.load(filename, sr=None, mono=True)
-
-            # Main logic that uses <mel, audio> pair for training BigVGAN
-            if not self.fine_tuning:
-                if self.split:  # Training step
-                    # Obtain randomized audio chunk
-                    if source_sampling_rate != self.sampling_rate:
-                        # Adjust segment size to crop if the source sr is different
-                        target_segment_size = math.ceil(self.segment_size * (source_sampling_rate / self.sampling_rate))
-                    else:
-                        target_segment_size = self.segment_size
-
-                    # Compute upper bound index for the random chunk
-                    random_chunk_upper_bound = max(0, audio.shape[0] - target_segment_size)
-
-                    # Crop or pad audio to obtain random chunk with target_segment_size
-                    if audio.shape[0] >= target_segment_size:
-                        audio_start = random.randint(0, random_chunk_upper_bound)
-                        audio = audio[audio_start : audio_start + target_segment_size]
-                    else:
-                        audio = np.pad(
-                            audio,
-                            (0, target_segment_size - audio.shape[0]),
-                            mode="constant",
-                        )
-
-                    # Resample audio chunk to self.sampling rate
-                    if source_sampling_rate != self.sampling_rate:
-                        audio = librosa.resample(
-                            audio,
-                            orig_sr=source_sampling_rate,
-                            target_sr=self.sampling_rate,
-                        )
-                        if audio.shape[0] > self.segment_size:
-                            # trim last elements to match self.segment_size (e.g., 16385 for 44khz downsampled to 24khz -> 16384)
-                            audio = audio[: self.segment_size]
-
-                else:  # Validation step
-                    # Resample full audio clip to target sampling rate
-                    if source_sampling_rate != self.sampling_rate:
-                        audio = librosa.resample(
-                            audio,
-                            orig_sr=source_sampling_rate,
-                            target_sr=self.sampling_rate,
-                        )
-                    # Trim last elements to match audio length to self.hop_size * n for evaluation
-                    if (audio.shape[0] % self.hop_size) != 0:
-                        audio = audio[: -(audio.shape[0] % self.hop_size)]
-
-                # BigVGAN is trained using volume-normalized waveform
-                audio = librosa.util.normalize(audio) * 0.95
-
-                # Cast ndarray to torch tensor
-                audio = torch.FloatTensor(audio)
-                audio = audio.unsqueeze(0)  # [B(1), self.segment_size]
-
-                # Compute mel spectrogram corresponding to audio
-                mel = mel_spectrogram(
-                    audio,
-                    self.n_fft,
-                    self.num_mels,
-                    self.sampling_rate,
-                    self.hop_size,
-                    self.win_size,
-                    self.fmin,
-                    self.fmax,
-                    center=False,
-                )  # [B(1), self.num_mels, self.segment_size // self.hop_size]
-
-            # Fine-tuning logic that uses pre-computed mel. Example: Using TTS model-generated mel as input
-            else:
-                # For fine-tuning, assert that the waveform is in the defined sampling_rate
-                # Fine-tuning won't support on-the-fly resampling to be fool-proof (the dataset should have been prepared properly)
-                assert source_sampling_rate == self.sampling_rate, (
-                    f"For fine_tuning, waveform must be in the spcified sampling rate {self.sampling_rate}, got {source_sampling_rate}"
-                )
-
-                # Cast ndarray to torch tensor
-                audio = torch.FloatTensor(audio)
-                audio = audio.unsqueeze(0)  # [B(1), T_time]
-
-                # Load pre-computed mel from disk
-                mel = np.load(
-                    os.path.join(
-                        self.base_mels_path,
-                        os.path.splitext(os.path.split(filename)[-1])[0] + ".npy",
-                    )
-                )
-                mel = torch.from_numpy(mel)
-
-                if len(mel.shape) < 3:
-                    mel = mel.unsqueeze(0)  # ensure [B, C, T]
-
-                if self.split:
-                    frames_per_seg = math.ceil(self.segment_size / self.hop_size)
-
-                    if audio.size(1) >= self.segment_size:
-                        mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1)
-                        mel = mel[:, :, mel_start : mel_start + frames_per_seg]
-                        audio = audio[
-                            :,
-                            mel_start * self.hop_size : (mel_start + frames_per_seg) * self.hop_size,
-                        ]
-
-                    # Pad pre-computed mel and audio to match length to ensuring fine-tuning without error.
-                    # NOTE: this may introduce a single-frame misalignment of the <pre-computed mel, audio>
-                    # To remove possible misalignment, it is recommended to prepare the <pre-computed mel, audio> pair where the audio length is the integer multiple of self.hop_size
-                    mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), "constant")
-                    audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant")
-
-            # Compute mel_loss used by spectral regression objective. Uses self.fmax_loss instead (usually None)
-            mel_loss = mel_spectrogram(
-                audio,
-                self.n_fft,
-                self.num_mels,
-                self.sampling_rate,
-                self.hop_size,
-                self.win_size,
-                self.fmin,
-                self.fmax_loss,
-                center=False,
-            )  # [B(1), self.num_mels, self.segment_size // self.hop_size]
-
-            # Shape sanity checks
-            assert (
-                audio.shape[1] == mel.shape[2] * self.hop_size and audio.shape[1] == mel_loss.shape[2] * self.hop_size
-            ), (
-                f"Audio length must be mel frame length * hop_size. Got audio shape {audio.shape} mel shape {mel.shape} mel_loss shape {mel_loss.shape}"
-            )
-
-            return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze())
-
-        # If it encounters error during loading the data, skip this sample and load random other sample to the batch
-        except Exception as e:
-            if self.fine_tuning:
-                raise e  # Terminate training if it is fine-tuning. The dataset should have been prepared properly.
-            else:
-                print(f"[WARNING] Failed to load waveform, skipping! filename: {filename} Error: {e}")
-                return self[random.randrange(len(self))]
-
-    def __len__(self):
-        return len(self.audio_files)

GPT_SoVITS/BigVGAN/nv-modelcard++/.gitkeep

@@ -1 +0,0 @@
-

GPT_SoVITS/BigVGAN/nv-modelcard++/bias.md

@@ -1,4 +0,0 @@
-| Field                                                                                                      | Response                                             |
-| :--------------------------------------------------------------------------------------------------------- | :--------------------------------------------------- |
-| Participation considerations from adversely impacted groups protected classes in model design and testing: | None                                                 |
-| Measures taken to mitigate against unwanted bias:                                                          | No measures taken to mitigate against unwanted bias. |

GPT_SoVITS/BigVGAN/nv-modelcard++/explainability.md

@@ -1,13 +0,0 @@
-| Field                                                                                                 | Response                                                                                                                                                                                                               |
-| :---------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Intended Application & Domain:                                                                        | Generating waveform from mel spectrogram.                                                                                                                                                                              |
-| Model Type:                                                                                           | Convolutional Neural Network (CNN)                                                                                                                                                                                     |
-| Intended Users:                                                                                       | This model is intended for developers to synthesize and generate waveforms from the AI-generated mel spectrograms.                                                                                                     |
-| Output:                                                                                               | Audio Waveform                                                                                                                                                                                                         |
-| Describe how the model works:                                                                         | Model generates audio waveform corresponding to the input mel spectrogram.                                                                                                                                             |
-| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable                                                                                                                                                                                                         |
-| Technical Limitations:                                                                                | This may not perform well on synthetically-generated mel spectrograms that deviate significantly from the profile of mel spectrograms on which this was trained.                                                       |
-| Verified to have met prescribed NVIDIA quality standards:                                             | Yes                                                                                                                                                                                                                    |
-| Performance Metrics:                                                                                  | Perceptual Evaluation of Speech Quality (PESQ), Virtual Speech Quality Objective Listener (VISQOL), Multi-resolution STFT (MRSTFT), Mel cepstral distortion (MCD), Periodicity RMSE, Voice/Unvoiced F1 Score (V/UV F1) |
-| Potential Known Risks:                                                                                | This model may generate low-quality or distorted soundwaves.                                                                                                                                                           |
-| Licensing:                                                                                            | https://github.com/NVIDIA/BigVGAN/blob/main/LICENSE                                                                                                                                                                    |

GPT_SoVITS/BigVGAN/nv-modelcard++/overview.md

@@ -1,126 +0,0 @@
-# Model Overview
-
-## Description:
-
-BigVGAN is a generative AI model specialized in synthesizing audio waveforms using Mel spectrogram as inputs.
-
-<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
-
-BigVGAN is a fully convolutional architecture with several upsampling blocks using transposed convolution followed by multiple residual dilated convolution layers.
-
-BigVGAN consists of a novel module, called anti-aliased multi-periodicity composition (AMP), which is specifically designed for generating waveforms. AMP is specialized in synthesizing high-frequency and periodic soundwaves drawing inspiration from audio signal processing principles.
-
-It applies a periodic activation function, called Snake, which provides an inductive bias to the architecture in generating periodic soundwaves. It also applies anti-aliasing filters to reduce undesired artifacts in the generated waveforms. <br>
-
-This model is ready for commercial use.<br>
-
-## References(s):
-
-- [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://arxiv.org/abs/2206.04658) <br>
-- [Project Page](https://research.nvidia.com/labs/adlr/projects/bigvgan/) <br>
-- [Audio Demo](https://bigvgan-demo.github.io/) <br>
-
-## Model Architecture:
-
-**Architecture Type:** Convolution Neural Network (CNN) <br>
-**Network Architecture:** You can see the details of this model on this link: https://github.com/NVIDIA/BigVGAN and the related paper can be found here: https://arxiv.org/abs/2206.04658<br>
-**Model Version:** 2.0 <br>
-
-## Input:
-
-**Input Type:** Audio <br>
-**Input Format:** Mel Spectrogram <br>
-**Input Parameters:** None <br>
-**Other Properties Related to Input:** The input mel spectrogram has shape `[batch, channels, frames]`, where `channels` refers to the number of mel bands defined by the model and `frames` refers to the temporal length. The model supports arbitrary long `frames` that fits into the GPU memory.
-
-## Output:
-
-**Input Type:** Audio <br>
-**Output Format:** Audio Waveform <br>
-**Output Parameters:** None <br>
-**Other Properties Related to Output:** The output audio waveform has shape `[batch, 1, time]`, where `1` refers to the mono audio channels and `time` refers to the temporal length. `time` is defined as a fixed integer multiple of input `frames`, which is an upsampling ratio of the model (`time = upsampling ratio * frames`). The output audio waveform consitutes float values with a range of `[-1, 1]`.
-
-## Software Integration:
-
-**Runtime Engine(s):** PyTorch
-
-**Supported Hardware Microarchitecture Compatibility:** NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace, NVIDIA Turing, NVIDIA Volta <br>
-
-## Preferred/Supported Operating System(s):
-
-Linux
-
-## Model Version(s):
-
-v2.0
-
-## Training, Testing, and Evaluation Datasets:
-
-### Training Dataset:
-
-The dataset contains diverse audio types, including speech in multiple languages, environmental sounds, and instruments.
-
-**Links:**
-
-- [AAM: Artificial Audio Multitracks Dataset](https://zenodo.org/records/5794629)
-- [AudioCaps](https://audiocaps.github.io/)
-- [AudioSet](https://research.google.com/audioset/index.html)
-- [common-accent](https://huggingface.co/datasets/DTU54DL/common-accent)
-- [Crowd Sourced Emotional Multimodal Actors Dataset (CREMA-D)](https://ieeexplore.ieee.org/document/6849440)
-- [DCASE2017 Challenge, Task 4: Large-scale weakly supervised sound event detection for smart cars](https://dcase.community/challenge2017/task-large-scale-sound-event-detection)
-- [FSDnoisy18k](https://zenodo.org/records/2529934)
-- [Free Universal Sound Separation Dataset](https://zenodo.org/records/3694384)
-- [Greatest Hits dataset](https://andrewowens.com/vis/)
-- [GTZAN](https://ieeexplore.ieee.org/document/1021072)
-- [JL corpus](https://www.kaggle.com/datasets/tli725/jl-corpus)
-- [Medley-solos-DB: a cross-collection dataset for musical instrument recognition](https://zenodo.org/records/3464194)
-- [MUSAN: A Music, Speech, and Noise Corpus](https://www.openslr.org/17/)
-- [MusicBench](https://huggingface.co/datasets/amaai-lab/MusicBench)
-- [MusicCaps](https://www.kaggle.com/datasets/googleai/musiccaps)
-- [MusicNet](https://www.kaggle.com/datasets/imsparsh/musicnet-dataset)
-- [NSynth](https://magenta.tensorflow.org/datasets/nsynth)
-- [OnAir-Music-Dataset](https://github.com/sevagh/OnAir-Music-Dataset)
-- [Audio Piano Triads Dataset](https://zenodo.org/records/4740877)
-- [Pitch Audio Dataset (Surge synthesizer)](https://zenodo.org/records/4677097)
-- [SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network](https://zenodo.org/records/3966543)
-- [VocalSound: A Dataset for Improving Human Vocal Sounds Recognition](https://arxiv.org/abs/2205.03433)
-- [WavText5K](https://github.com/microsoft/WavText5K)
-- [CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages](https://github.com/Kyubyong/css10)
-- [Hi-Fi Multi-Speaker English TTS Dataset (Hi-Fi TTS)](https://www.openslr.org/109/)
-- [IIIT-H Indic Speech Databases](http://festvox.org/databases/iiit_voices/)
-- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://arxiv.org/abs/1912.07875)
-- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://www.openslr.org/60)
-- [LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus](https://www.openslr.org/141/)
-- [The SIWIS French Speech Synthesis Database](https://datashare.ed.ac.uk/handle/10283/2353)
-- [Crowdsourced high-quality Colombian Spanish speech data set](https://openslr.org/72/)
-- [TTS-Portuguese Corpus](https://github.com/Edresson/TTS-Portuguese-Corpus)
-- [CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit](https://datashare.ed.ac.uk/handle/10283/3443)
-
-\*\* Data Collection Method by dataset <br>
-
-- Human <br>
-
-\*\* Labeling Method by dataset (for those with labels) <br>
-
-- Hybrid: Automated, Human, Unknown <br>
-
-### Evaluating Dataset:
-
-Properties: The audio generation quality of BigVGAN is evaluated using `dev` splits of the [LibriTTS dataset](https://www.openslr.org/60/) and [Hi-Fi TTS dataset](https://www.openslr.org/109/). The datasets include speech in English language with equal balance of genders.
-
-\*\* Data Collection Method by dataset <br>
-
-- Human <br>
-
-\*\* Labeling Method by dataset <br>
-
-- Automated <br>
-
-## Inference:
-
-**Engine:** PyTorch <br>
-**Test Hardware:** NVIDIA A100 GPU <br>
-
-## Ethical Considerations:
-
-NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

GPT_SoVITS/BigVGAN/nv-modelcard++/privacy.md

@@ -1,14 +0,0 @@
-| Field                                                                                                                                  | Response                                       |
-| :------------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------- |
-| Generatable or reverse engineerable personal information?                                                                              | None                                           |
-| Protected class data used to create this model?                                                                                        | None                                           |
-| Was consent obtained for any personal data used?                                                                                       | Not Applicable (No Personal Data)              |
-| How often is dataset reviewed?                                                                                                         | Before Release                                 |
-| Is a mechanism in place to honor data subject right of access or deletion of personal data?                                            | Not Applicable                                 |
-| If personal collected for the development of the model, was it collected directly by NVIDIA?                                           | Not Applicable                                 |
-| If personal collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable                                 |
-| If personal collected for the development of this AI model, was it minimized to only what was required?                                | Not Applicable                                 |
-| Is data in dataset traceable?                                                                                                          | Yes                                            |
-| Is there provenance for all datasets used in training?                                                                                 | Yes                                            |
-| Does data labeling (annotation, metadata) comply with privacy laws?                                                                    | Yes                                            |
-| Is data compliant with data subject requests for data correction or removal, if such a request was made?                               | No, not possible with externally-sourced data. |

GPT_SoVITS/BigVGAN/nv-modelcard++/safety.md

@@ -1,6 +0,0 @@
-| Field                                           | Response                                                                                                                                                                                                          |
-| :---------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Model Application(s):                           | Synethic Audio Generation                                                                                                                                                                                         |
-| Describe the life critical impact (if present). | Not Applicable                                                                                                                                                                                                    |
-| Use Case Restrictions:                          | None                                                                                                                                                                                                              |
-| Model and dataset restrictions:                 | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |

GPT_SoVITS/BigVGAN/requirements.txt

@@ -1,13 +0,0 @@
-torch
-numpy
-librosa>=0.8.1
-scipy
-tensorboard
-soundfile
-matplotlib
-pesq
-auraloss
-tqdm
-nnAudio
-ninja
-huggingface_hub>=0.23.4

GPT_SoVITS/BigVGAN/tests/test_activation.py

@@ -1,62 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-import os
-import sys
-
-# to import modules from parent_dir
-parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(parent_dir)
-
-import torch
-from alias_free_activation.cuda import activation1d
-from activations import Snake
-
-
-def test_load_fused_kernels():
-    try:
-        print("[Success] load_fused_kernels")
-    except ImportError as e:
-        print("[Fail] load_fused_kernels")
-        raise e
-
-
-def test_anti_alias_activation():
-    data = torch.rand((10, 10, 200), device="cuda")
-
-    # Check activations.Snake cuda vs. torch
-    fused_anti_alias_activation = activation1d.Activation1d(activation=Snake(10), fused=True).cuda()
-    fused_activation_output = fused_anti_alias_activation(data)
-
-    torch_anti_alias_activation = activation1d.Activation1d(activation=Snake(10), fused=False).cuda()
-    torch_activation_output = torch_anti_alias_activation(data)
-
-    test_result = (fused_activation_output - torch_activation_output).abs()
-
-    while test_result.dim() != 1:
-        test_result = test_result.mean(dim=-1)
-
-    diff = test_result.mean(dim=-1)
-
-    if diff <= 1e-3:
-        print(
-            f"\n[Success] test_fused_anti_alias_activation"
-            f"\n > mean_difference={diff}"
-            f"\n > fused_values={fused_activation_output[-1][-1][:].tolist()}"
-            f"\n > torch_values={torch_activation_output[-1][-1][:].tolist()}"
-        )
-    else:
-        print(
-            f"\n[Fail] test_fused_anti_alias_activation"
-            f"\n > mean_difference={diff}, "
-            f"\n > fused_values={fused_activation_output[-1][-1][:].tolist()}, "
-            f"\n > torch_values={torch_activation_output[-1][-1][:].tolist()}"
-        )
-
-
-if __name__ == "__main__":
-    from alias_free_activation.cuda import load
-
-    load.load()
-    test_load_fused_kernels()
-    test_anti_alias_activation()

GPT_SoVITS/BigVGAN/tests/test_activation_snake_beta.py

@@ -1,62 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-import os
-import sys
-
-# to import modules from parent_dir
-parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(parent_dir)
-
-import torch
-from alias_free_activation.cuda import activation1d
-from activations import SnakeBeta
-
-
-def test_load_fused_kernels():
-    try:
-        print("[Success] load_fused_kernels")
-    except ImportError as e:
-        print("[Fail] load_fused_kernels")
-        raise e
-
-
-def test_anti_alias_activation():
-    data = torch.rand((10, 10, 200), device="cuda")
-
-    # Check activations, Snake CUDA vs. Torch
-    fused_anti_alias_activation = activation1d.Activation1d(activation=SnakeBeta(10), fused=True).cuda()
-    fused_activation_output = fused_anti_alias_activation(data)
-
-    torch_anti_alias_activation = activation1d.Activation1d(activation=SnakeBeta(10), fused=False).cuda()
-    torch_activation_output = torch_anti_alias_activation(data)
-
-    test_result = (fused_activation_output - torch_activation_output).abs()
-
-    while test_result.dim() != 1:
-        test_result = test_result.mean(dim=-1)
-
-    diff = test_result.mean(dim=-1)
-
-    if diff <= 1e-3:
-        print(
-            f"\n[Success] test_fused_anti_alias_activation"
-            f"\n > mean_difference={diff}"
-            f"\n > fused_values={fused_activation_output[-1][-1][:].tolist()}"
-            f"\n > torch_values={torch_activation_output[-1][-1][:].tolist()}"
-        )
-    else:
-        print(
-            f"\n[Fail] test_fused_anti_alias_activation"
-            f"\n > mean_difference={diff}, "
-            f"\n > fused_values={fused_activation_output[-1][-1][:].tolist()}, "
-            f"\n > torch_values={torch_activation_output[-1][-1][:].tolist()}"
-        )
-
-
-if __name__ == "__main__":
-    from alias_free_activation.cuda import load
-
-    load.load()
-    test_load_fused_kernels()
-    test_anti_alias_activation()

GPT_SoVITS/BigVGAN/tests/test_cuda_vs_torch_model.py

@@ -1,215 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-import os
-import sys
-
-# to import modules from parent_dir
-parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(parent_dir)
-
-import torch
-import json
-from env import AttrDict
-from bigvgan import BigVGAN
-from time import time
-from tqdm import tqdm
-from meldataset import mel_spectrogram, MAX_WAV_VALUE
-from scipy.io.wavfile import write
-import numpy as np
-
-import argparse
-
-torch.backends.cudnn.benchmark = True
-
-# For easier debugging
-torch.set_printoptions(linewidth=200, threshold=10_000)
-
-
-def generate_soundwave(duration=5.0, sr=24000):
-    t = np.linspace(0, duration, int(sr * duration), False, dtype=np.float32)
-
-    modulation = np.sin(2 * np.pi * t / duration)
-
-    min_freq = 220
-    max_freq = 1760
-    frequencies = min_freq + (max_freq - min_freq) * (modulation + 1) / 2
-    soundwave = np.sin(2 * np.pi * frequencies * t)
-
-    soundwave = soundwave / np.max(np.abs(soundwave)) * 0.95
-
-    return soundwave, sr
-
-
-def get_mel(x, h):
-    return mel_spectrogram(x, h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print(f"Loading '{filepath}'")
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Test script to check CUDA kernel correctness.")
-    parser.add_argument(
-        "--checkpoint_file",
-        type=str,
-        required=True,
-        help="Path to the checkpoint file. Assumes config.json exists in the directory.",
-    )
-
-    args = parser.parse_args()
-
-    config_file = os.path.join(os.path.split(args.checkpoint_file)[0], "config.json")
-    with open(config_file) as f:
-        config = f.read()
-    json_config = json.loads(config)
-    h = AttrDict({**json_config})
-
-    print("loading plain Pytorch BigVGAN")
-    generator_original = BigVGAN(h).to("cuda")
-    print("loading CUDA kernel BigVGAN with auto-build")
-    generator_cuda_kernel = BigVGAN(h, use_cuda_kernel=True).to("cuda")
-
-    state_dict_g = load_checkpoint(args.checkpoint_file, "cuda")
-    generator_original.load_state_dict(state_dict_g["generator"])
-    generator_cuda_kernel.load_state_dict(state_dict_g["generator"])
-
-    generator_original.remove_weight_norm()
-    generator_original.eval()
-    generator_cuda_kernel.remove_weight_norm()
-    generator_cuda_kernel.eval()
-
-    # define number of samples and length of mel frame to benchmark
-    num_sample = 10
-    num_mel_frame = 16384
-
-    # CUDA kernel correctness check
-    diff = 0.0
-    for i in tqdm(range(num_sample)):
-        # Random mel
-        data = torch.rand((1, h.num_mels, num_mel_frame), device="cuda")
-
-        with torch.inference_mode():
-            audio_original = generator_original(data)
-
-        with torch.inference_mode():
-            audio_cuda_kernel = generator_cuda_kernel(data)
-
-        # Both outputs should be (almost) the same
-        test_result = (audio_original - audio_cuda_kernel).abs()
-        diff += test_result.mean(dim=-1).item()
-
-    diff /= num_sample
-    if diff <= 2e-3:  # We can expect a small difference (~1e-3) which does not affect perceptual quality
-        print(
-            f"\n[Success] test CUDA fused vs. plain torch BigVGAN inference"
-            f"\n > mean_difference={diff}"
-            f"\n > fused_values={audio_cuda_kernel[-1][-1][-30:].tolist()}"
-            f"\n > torch_values={audio_original[-1][-1][-30:].tolist()}"
-        )
-    else:
-        print(
-            f"\n[Fail] test CUDA fused vs. plain torch BigVGAN inference"
-            f"\n > mean_difference={diff}"
-            f"\n > fused_values={audio_cuda_kernel[-1][-1][-30:].tolist()}, "
-            f"\n > torch_values={audio_original[-1][-1][-30:].tolist()}"
-        )
-
-    del data, audio_original, audio_cuda_kernel
-
-    # Variables for tracking total time and VRAM usage
-    toc_total_original = 0
-    toc_total_cuda_kernel = 0
-    vram_used_original_total = 0
-    vram_used_cuda_kernel_total = 0
-    audio_length_total = 0
-
-    # Measure Original inference in isolation
-    for i in tqdm(range(num_sample)):
-        torch.cuda.reset_peak_memory_stats(device="cuda")
-        data = torch.rand((1, h.num_mels, num_mel_frame), device="cuda")
-        torch.cuda.synchronize()
-        tic = time()
-        with torch.inference_mode():
-            audio_original = generator_original(data)
-        torch.cuda.synchronize()
-        toc = time() - tic
-        toc_total_original += toc
-
-        vram_used_original_total += torch.cuda.max_memory_allocated(device="cuda")
-
-        del data, audio_original
-        torch.cuda.empty_cache()
-
-    # Measure CUDA kernel inference in isolation
-    for i in tqdm(range(num_sample)):
-        torch.cuda.reset_peak_memory_stats(device="cuda")
-        data = torch.rand((1, h.num_mels, num_mel_frame), device="cuda")
-        torch.cuda.synchronize()
-        tic = time()
-        with torch.inference_mode():
-            audio_cuda_kernel = generator_cuda_kernel(data)
-        torch.cuda.synchronize()
-        toc = time() - tic
-        toc_total_cuda_kernel += toc
-
-        audio_length_total += audio_cuda_kernel.shape[-1]
-
-        vram_used_cuda_kernel_total += torch.cuda.max_memory_allocated(device="cuda")
-
-        del data, audio_cuda_kernel
-        torch.cuda.empty_cache()
-
-    # Calculate metrics
-    audio_second = audio_length_total / h.sampling_rate
-    khz_original = audio_length_total / toc_total_original / 1000
-    khz_cuda_kernel = audio_length_total / toc_total_cuda_kernel / 1000
-    vram_used_original_gb = vram_used_original_total / num_sample / (1024**3)
-    vram_used_cuda_kernel_gb = vram_used_cuda_kernel_total / num_sample / (1024**3)
-
-    # Print results
-    print(
-        f"Original BigVGAN: took {toc_total_original:.2f} seconds to generate {audio_second:.2f} seconds of audio, {khz_original:.1f}kHz, {audio_second / toc_total_original:.1f} faster than realtime, VRAM used {vram_used_original_gb:.1f} GB"
-    )
-    print(
-        f"CUDA kernel BigVGAN: took {toc_total_cuda_kernel:.2f} seconds to generate {audio_second:.2f} seconds of audio, {khz_cuda_kernel:.1f}kHz, {audio_second / toc_total_cuda_kernel:.1f} faster than realtime, VRAM used {vram_used_cuda_kernel_gb:.1f} GB"
-    )
-    print(f"speedup of CUDA kernel: {khz_cuda_kernel / khz_original}")
-    print(f"VRAM saving of CUDA kernel: {vram_used_original_gb / vram_used_cuda_kernel_gb}")
-
-    # Use artificial sine waves for inference test
-    audio_real, sr = generate_soundwave(duration=5.0, sr=h.sampling_rate)
-    audio_real = torch.tensor(audio_real).to("cuda")
-    # Compute mel spectrogram from the ground truth audio
-    x = get_mel(audio_real.unsqueeze(0), h)
-
-    with torch.inference_mode():
-        y_g_hat_original = generator_original(x)
-        y_g_hat_cuda_kernel = generator_cuda_kernel(x)
-
-    audio_real = audio_real.squeeze()
-    audio_real = audio_real * MAX_WAV_VALUE
-    audio_real = audio_real.cpu().numpy().astype("int16")
-
-    audio_original = y_g_hat_original.squeeze()
-    audio_original = audio_original * MAX_WAV_VALUE
-    audio_original = audio_original.cpu().numpy().astype("int16")
-
-    audio_cuda_kernel = y_g_hat_cuda_kernel.squeeze()
-    audio_cuda_kernel = audio_cuda_kernel * MAX_WAV_VALUE
-    audio_cuda_kernel = audio_cuda_kernel.cpu().numpy().astype("int16")
-
-    os.makedirs("tmp", exist_ok=True)
-    output_file_real = os.path.join("tmp", "audio_real.wav")
-    output_file_original = os.path.join("tmp", "audio_generated_original.wav")
-    output_file_cuda_kernel = os.path.join("tmp", "audio_generated_cuda_kernel.wav")
-    write(output_file_real, h.sampling_rate, audio_real)
-    write(output_file_original, h.sampling_rate, audio_original)
-    write(output_file_cuda_kernel, h.sampling_rate, audio_cuda_kernel)
-    print("Example generated audios of original vs. fused CUDA kernel written to tmp!")
-    print("Done")

GPT_SoVITS/BigVGAN/train.py

@@ -1,716 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-
-import warnings
-
-warnings.simplefilter(action="ignore", category=FutureWarning)
-import itertools
-import os
-import time
-import argparse
-import json
-import torch
-import torch.nn.functional as F
-from torch.utils.tensorboard import SummaryWriter
-from torch.utils.data import DistributedSampler, DataLoader
-import torch.multiprocessing as mp
-from torch.distributed import init_process_group
-from torch.nn.parallel import DistributedDataParallel
-from env import AttrDict, build_env
-from meldataset import MelDataset, mel_spectrogram, get_dataset_filelist, MAX_WAV_VALUE
-
-from bigvgan import BigVGAN
-from discriminators import (
-    MultiPeriodDiscriminator,
-    MultiResolutionDiscriminator,
-    MultiBandDiscriminator,
-    MultiScaleSubbandCQTDiscriminator,
-)
-from loss import (
-    feature_loss,
-    generator_loss,
-    discriminator_loss,
-    MultiScaleMelSpectrogramLoss,
-)
-
-from utils import (
-    plot_spectrogram,
-    plot_spectrogram_clipped,
-    scan_checkpoint,
-    load_checkpoint,
-    save_checkpoint,
-    save_audio,
-)
-import torchaudio as ta
-from pesq import pesq
-from tqdm import tqdm
-import auraloss
-
-torch.backends.cudnn.benchmark = False
-
-
-def train(rank, a, h):
-    if h.num_gpus > 1:
-        # initialize distributed
-        init_process_group(
-            backend=h.dist_config["dist_backend"],
-            init_method=h.dist_config["dist_url"],
-            world_size=h.dist_config["world_size"] * h.num_gpus,
-            rank=rank,
-        )
-
-    # Set seed and device
-    torch.cuda.manual_seed(h.seed)
-    torch.cuda.set_device(rank)
-    device = torch.device(f"cuda:{rank:d}")
-
-    # Define BigVGAN generator
-    generator = BigVGAN(h).to(device)
-
-    # Define discriminators. MPD is used by default
-    mpd = MultiPeriodDiscriminator(h).to(device)
-
-    # Define additional discriminators. BigVGAN-v1 uses UnivNet's MRD as default
-    # New in BigVGAN-v2: option to switch to new discriminators: MultiBandDiscriminator / MultiScaleSubbandCQTDiscriminator
-    if h.get("use_mbd_instead_of_mrd", False):  # Switch to MBD
-        print("[INFO] using MultiBandDiscriminator of BigVGAN-v2 instead of MultiResolutionDiscriminator")
-        # Variable name is kept as "mrd" for backward compatibility & minimal code change
-        mrd = MultiBandDiscriminator(h).to(device)
-    elif h.get("use_cqtd_instead_of_mrd", False):  # Switch to CQTD
-        print("[INFO] using MultiScaleSubbandCQTDiscriminator of BigVGAN-v2 instead of MultiResolutionDiscriminator")
-        mrd = MultiScaleSubbandCQTDiscriminator(h).to(device)
-    else:  # Fallback to original MRD in BigVGAN-v1
-        mrd = MultiResolutionDiscriminator(h).to(device)
-
-    # New in BigVGAN-v2: option to switch to multi-scale L1 mel loss
-    if h.get("use_multiscale_melloss", False):
-        print("[INFO] using multi-scale Mel l1 loss of BigVGAN-v2 instead of the original single-scale loss")
-        fn_mel_loss_multiscale = MultiScaleMelSpectrogramLoss(
-            sampling_rate=h.sampling_rate
-        )  # NOTE: accepts waveform as input
-    else:
-        fn_mel_loss_singlescale = F.l1_loss
-
-    # Print the model & number of parameters, and create or scan the latest checkpoint from checkpoints directory
-    if rank == 0:
-        print(generator)
-        print(mpd)
-        print(mrd)
-        print(f"Generator params: {sum(p.numel() for p in generator.parameters())}")
-        print(f"Discriminator mpd params: {sum(p.numel() for p in mpd.parameters())}")
-        print(f"Discriminator mrd params: {sum(p.numel() for p in mrd.parameters())}")
-        os.makedirs(a.checkpoint_path, exist_ok=True)
-        print(f"Checkpoints directory: {a.checkpoint_path}")
-
-    if os.path.isdir(a.checkpoint_path):
-        # New in v2.1: If the step prefix pattern-based checkpoints are not found, also check for renamed files in Hugging Face Hub to resume training
-        cp_g = scan_checkpoint(a.checkpoint_path, prefix="g_", renamed_file="bigvgan_generator.pt")
-        cp_do = scan_checkpoint(
-            a.checkpoint_path,
-            prefix="do_",
-            renamed_file="bigvgan_discriminator_optimizer.pt",
-        )
-
-    # Load the latest checkpoint if exists
-    steps = 0
-    if cp_g is None or cp_do is None:
-        state_dict_do = None
-        last_epoch = -1
-    else:
-        state_dict_g = load_checkpoint(cp_g, device)
-        state_dict_do = load_checkpoint(cp_do, device)
-        generator.load_state_dict(state_dict_g["generator"])
-        mpd.load_state_dict(state_dict_do["mpd"])
-        mrd.load_state_dict(state_dict_do["mrd"])
-        steps = state_dict_do["steps"] + 1
-        last_epoch = state_dict_do["epoch"]
-
-    # Initialize DDP, optimizers, and schedulers
-    if h.num_gpus > 1:
-        generator = DistributedDataParallel(generator, device_ids=[rank]).to(device)
-        mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device)
-        mrd = DistributedDataParallel(mrd, device_ids=[rank]).to(device)
-
-    optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2])
-    optim_d = torch.optim.AdamW(
-        itertools.chain(mrd.parameters(), mpd.parameters()),
-        h.learning_rate,
-        betas=[h.adam_b1, h.adam_b2],
-    )
-
-    if state_dict_do is not None:
-        optim_g.load_state_dict(state_dict_do["optim_g"])
-        optim_d.load_state_dict(state_dict_do["optim_d"])
-
-    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch)
-    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch)
-
-    # Define training and validation datasets
-
-    """
-    unseen_validation_filelist will contain sample filepaths outside the seen training & validation dataset
-    Example: trained on LibriTTS, validate on VCTK
-    """
-    training_filelist, validation_filelist, list_unseen_validation_filelist = get_dataset_filelist(a)
-
-    trainset = MelDataset(
-        training_filelist,
-        h,
-        h.segment_size,
-        h.n_fft,
-        h.num_mels,
-        h.hop_size,
-        h.win_size,
-        h.sampling_rate,
-        h.fmin,
-        h.fmax,
-        shuffle=False if h.num_gpus > 1 else True,
-        fmax_loss=h.fmax_for_loss,
-        device=device,
-        fine_tuning=a.fine_tuning,
-        base_mels_path=a.input_mels_dir,
-        is_seen=True,
-    )
-
-    train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None
-
-    train_loader = DataLoader(
-        trainset,
-        num_workers=h.num_workers,
-        shuffle=False,
-        sampler=train_sampler,
-        batch_size=h.batch_size,
-        pin_memory=True,
-        drop_last=True,
-    )
-
-    if rank == 0:
-        validset = MelDataset(
-            validation_filelist,
-            h,
-            h.segment_size,
-            h.n_fft,
-            h.num_mels,
-            h.hop_size,
-            h.win_size,
-            h.sampling_rate,
-            h.fmin,
-            h.fmax,
-            False,
-            False,
-            fmax_loss=h.fmax_for_loss,
-            device=device,
-            fine_tuning=a.fine_tuning,
-            base_mels_path=a.input_mels_dir,
-            is_seen=True,
-        )
-        validation_loader = DataLoader(
-            validset,
-            num_workers=1,
-            shuffle=False,
-            sampler=None,
-            batch_size=1,
-            pin_memory=True,
-            drop_last=True,
-        )
-
-        list_unseen_validset = []
-        list_unseen_validation_loader = []
-        for i in range(len(list_unseen_validation_filelist)):
-            unseen_validset = MelDataset(
-                list_unseen_validation_filelist[i],
-                h,
-                h.segment_size,
-                h.n_fft,
-                h.num_mels,
-                h.hop_size,
-                h.win_size,
-                h.sampling_rate,
-                h.fmin,
-                h.fmax,
-                False,
-                False,
-                fmax_loss=h.fmax_for_loss,
-                device=device,
-                fine_tuning=a.fine_tuning,
-                base_mels_path=a.input_mels_dir,
-                is_seen=False,
-            )
-            unseen_validation_loader = DataLoader(
-                unseen_validset,
-                num_workers=1,
-                shuffle=False,
-                sampler=None,
-                batch_size=1,
-                pin_memory=True,
-                drop_last=True,
-            )
-            list_unseen_validset.append(unseen_validset)
-            list_unseen_validation_loader.append(unseen_validation_loader)
-
-        # Tensorboard logger
-        sw = SummaryWriter(os.path.join(a.checkpoint_path, "logs"))
-        if a.save_audio:  # Also save audio to disk if --save_audio is set to True
-            os.makedirs(os.path.join(a.checkpoint_path, "samples"), exist_ok=True)
-
-    """
-    Validation loop, "mode" parameter is automatically defined as (seen or unseen)_(name of the dataset).
-    If the name of the dataset contains "nonspeech", it skips PESQ calculation to prevent errors 
-    """
-
-    def validate(rank, a, h, loader, mode="seen"):
-        assert rank == 0, "validate should only run on rank=0"
-        generator.eval()
-        torch.cuda.empty_cache()
-
-        val_err_tot = 0
-        val_pesq_tot = 0
-        val_mrstft_tot = 0
-
-        # Modules for evaluation metrics
-        pesq_resampler = ta.transforms.Resample(h.sampling_rate, 16000).cuda()
-        loss_mrstft = auraloss.freq.MultiResolutionSTFTLoss(device="cuda")
-
-        if a.save_audio:  # Also save audio to disk if --save_audio is set to True
-            os.makedirs(
-                os.path.join(a.checkpoint_path, "samples", f"gt_{mode}"),
-                exist_ok=True,
-            )
-            os.makedirs(
-                os.path.join(a.checkpoint_path, "samples", f"{mode}_{steps:08d}"),
-                exist_ok=True,
-            )
-
-        with torch.no_grad():
-            print(f"step {steps} {mode} speaker validation...")
-
-            # Loop over validation set and compute metrics
-            for j, batch in enumerate(tqdm(loader)):
-                x, y, _, y_mel = batch
-                y = y.to(device)
-                if hasattr(generator, "module"):
-                    y_g_hat = generator.module(x.to(device))
-                else:
-                    y_g_hat = generator(x.to(device))
-                y_mel = y_mel.to(device, non_blocking=True)
-                y_g_hat_mel = mel_spectrogram(
-                    y_g_hat.squeeze(1),
-                    h.n_fft,
-                    h.num_mels,
-                    h.sampling_rate,
-                    h.hop_size,
-                    h.win_size,
-                    h.fmin,
-                    h.fmax_for_loss,
-                )
-                min_t = min(y_mel.size(-1), y_g_hat_mel.size(-1))
-                val_err_tot += F.l1_loss(y_mel[..., :min_t], y_g_hat_mel[..., :min_t]).item()
-
-                # PESQ calculation. only evaluate PESQ if it's speech signal (nonspeech PESQ will error out)
-                if "nonspeech" not in mode:  # Skips if the name of dataset (in mode string) contains "nonspeech"
-                    # Resample to 16000 for pesq
-                    y_16k = pesq_resampler(y)
-                    y_g_hat_16k = pesq_resampler(y_g_hat.squeeze(1))
-                    y_int_16k = (y_16k[0] * MAX_WAV_VALUE).short().cpu().numpy()
-                    y_g_hat_int_16k = (y_g_hat_16k[0] * MAX_WAV_VALUE).short().cpu().numpy()
-                    val_pesq_tot += pesq(16000, y_int_16k, y_g_hat_int_16k, "wb")
-
-                # MRSTFT calculation
-                min_t = min(y.size(-1), y_g_hat.size(-1))
-                val_mrstft_tot += loss_mrstft(y_g_hat[..., :min_t], y[..., :min_t]).item()
-
-                # Log audio and figures to Tensorboard
-                if j % a.eval_subsample == 0:  # Subsample every nth from validation set
-                    if steps >= 0:
-                        sw.add_audio(f"gt_{mode}/y_{j}", y[0], steps, h.sampling_rate)
-                        if a.save_audio:  # Also save audio to disk if --save_audio is set to True
-                            save_audio(
-                                y[0],
-                                os.path.join(
-                                    a.checkpoint_path,
-                                    "samples",
-                                    f"gt_{mode}",
-                                    f"{j:04d}.wav",
-                                ),
-                                h.sampling_rate,
-                            )
-                        sw.add_figure(
-                            f"gt_{mode}/y_spec_{j}",
-                            plot_spectrogram(x[0]),
-                            steps,
-                        )
-
-                    sw.add_audio(
-                        f"generated_{mode}/y_hat_{j}",
-                        y_g_hat[0],
-                        steps,
-                        h.sampling_rate,
-                    )
-                    if a.save_audio:  # Also save audio to disk if --save_audio is set to True
-                        save_audio(
-                            y_g_hat[0, 0],
-                            os.path.join(
-                                a.checkpoint_path,
-                                "samples",
-                                f"{mode}_{steps:08d}",
-                                f"{j:04d}.wav",
-                            ),
-                            h.sampling_rate,
-                        )
-                    # Spectrogram of synthesized audio
-                    y_hat_spec = mel_spectrogram(
-                        y_g_hat.squeeze(1),
-                        h.n_fft,
-                        h.num_mels,
-                        h.sampling_rate,
-                        h.hop_size,
-                        h.win_size,
-                        h.fmin,
-                        h.fmax,
-                    )
-                    sw.add_figure(
-                        f"generated_{mode}/y_hat_spec_{j}",
-                        plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()),
-                        steps,
-                    )
-
-                    """
-                    Visualization of spectrogram difference between GT and synthesized audio, difference higher than 1 is clipped for better visualization.
-                    """
-                    spec_delta = torch.clamp(
-                        torch.abs(x[0] - y_hat_spec.squeeze(0).cpu()),
-                        min=1e-6,
-                        max=1.0,
-                    )
-                    sw.add_figure(
-                        f"delta_dclip1_{mode}/spec_{j}",
-                        plot_spectrogram_clipped(spec_delta.numpy(), clip_max=1.0),
-                        steps,
-                    )
-
-            val_err = val_err_tot / (j + 1)
-            val_pesq = val_pesq_tot / (j + 1)
-            val_mrstft = val_mrstft_tot / (j + 1)
-            # Log evaluation metrics to Tensorboard
-            sw.add_scalar(f"validation_{mode}/mel_spec_error", val_err, steps)
-            sw.add_scalar(f"validation_{mode}/pesq", val_pesq, steps)
-            sw.add_scalar(f"validation_{mode}/mrstft", val_mrstft, steps)
-
-        generator.train()
-
-    # If the checkpoint is loaded, start with validation loop
-    if steps != 0 and rank == 0 and not a.debug:
-        if not a.skip_seen:
-            validate(
-                rank,
-                a,
-                h,
-                validation_loader,
-                mode=f"seen_{train_loader.dataset.name}",
-            )
-        for i in range(len(list_unseen_validation_loader)):
-            validate(
-                rank,
-                a,
-                h,
-                list_unseen_validation_loader[i],
-                mode=f"unseen_{list_unseen_validation_loader[i].dataset.name}",
-            )
-    # Exit the script if --evaluate is set to True
-    if a.evaluate:
-        exit()
-
-    # Main training loop
-    generator.train()
-    mpd.train()
-    mrd.train()
-    for epoch in range(max(0, last_epoch), a.training_epochs):
-        if rank == 0:
-            start = time.time()
-            print(f"Epoch: {epoch + 1}")
-
-        if h.num_gpus > 1:
-            train_sampler.set_epoch(epoch)
-
-        for i, batch in enumerate(train_loader):
-            if rank == 0:
-                start_b = time.time()
-            x, y, _, y_mel = batch
-
-            x = x.to(device, non_blocking=True)
-            y = y.to(device, non_blocking=True)
-            y_mel = y_mel.to(device, non_blocking=True)
-            y = y.unsqueeze(1)
-
-            y_g_hat = generator(x)
-            y_g_hat_mel = mel_spectrogram(
-                y_g_hat.squeeze(1),
-                h.n_fft,
-                h.num_mels,
-                h.sampling_rate,
-                h.hop_size,
-                h.win_size,
-                h.fmin,
-                h.fmax_for_loss,
-            )
-
-            optim_d.zero_grad()
-
-            # MPD
-            y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach())
-            loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g)
-
-            # MRD
-            y_ds_hat_r, y_ds_hat_g, _, _ = mrd(y, y_g_hat.detach())
-            loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g)
-
-            loss_disc_all = loss_disc_s + loss_disc_f
-
-            # Set clip_grad_norm value
-            clip_grad_norm = h.get("clip_grad_norm", 1000.0)  # Default to 1000
-
-            # Whether to freeze D for initial training steps
-            if steps >= a.freeze_step:
-                loss_disc_all.backward()
-                grad_norm_mpd = torch.nn.utils.clip_grad_norm_(mpd.parameters(), clip_grad_norm)
-                grad_norm_mrd = torch.nn.utils.clip_grad_norm_(mrd.parameters(), clip_grad_norm)
-                optim_d.step()
-            else:
-                print(f"[WARNING] skipping D training for the first {a.freeze_step} steps")
-                grad_norm_mpd = 0.0
-                grad_norm_mrd = 0.0
-
-            # Generator
-            optim_g.zero_grad()
-
-            # L1 Mel-Spectrogram Loss
-            lambda_melloss = h.get("lambda_melloss", 45.0)  # Defaults to 45 in BigVGAN-v1 if not set
-            if h.get("use_multiscale_melloss", False):  # uses wav <y, y_g_hat> for loss
-                loss_mel = fn_mel_loss_multiscale(y, y_g_hat) * lambda_melloss
-            else:  # Uses mel <y_mel, y_g_hat_mel> for loss
-                loss_mel = fn_mel_loss_singlescale(y_mel, y_g_hat_mel) * lambda_melloss
-
-            # MPD loss
-            y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat)
-            loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
-            loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
-
-            # MRD loss
-            y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = mrd(y, y_g_hat)
-            loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
-            loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
-
-            if steps >= a.freeze_step:
-                loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel
-            else:
-                print(f"[WARNING] using regression loss only for G for the first {a.freeze_step} steps")
-                loss_gen_all = loss_mel
-
-            loss_gen_all.backward()
-            grad_norm_g = torch.nn.utils.clip_grad_norm_(generator.parameters(), clip_grad_norm)
-            optim_g.step()
-
-            if rank == 0:
-                # STDOUT logging
-                if steps % a.stdout_interval == 0:
-                    mel_error = loss_mel.item() / lambda_melloss  # Log training mel regression loss to stdout
-                    print(
-                        f"Steps: {steps:d}, "
-                        f"Gen Loss Total: {loss_gen_all:4.3f}, "
-                        f"Mel Error: {mel_error:4.3f}, "
-                        f"s/b: {time.time() - start_b:4.3f} "
-                        f"lr: {optim_g.param_groups[0]['lr']:4.7f} "
-                        f"grad_norm_g: {grad_norm_g:4.3f}"
-                    )
-
-                # Checkpointing
-                if steps % a.checkpoint_interval == 0 and steps != 0:
-                    checkpoint_path = f"{a.checkpoint_path}/g_{steps:08d}"
-                    save_checkpoint(
-                        checkpoint_path,
-                        {"generator": (generator.module if h.num_gpus > 1 else generator).state_dict()},
-                    )
-                    checkpoint_path = f"{a.checkpoint_path}/do_{steps:08d}"
-                    save_checkpoint(
-                        checkpoint_path,
-                        {
-                            "mpd": (mpd.module if h.num_gpus > 1 else mpd).state_dict(),
-                            "mrd": (mrd.module if h.num_gpus > 1 else mrd).state_dict(),
-                            "optim_g": optim_g.state_dict(),
-                            "optim_d": optim_d.state_dict(),
-                            "steps": steps,
-                            "epoch": epoch,
-                        },
-                    )
-
-                # Tensorboard summary logging
-                if steps % a.summary_interval == 0:
-                    mel_error = loss_mel.item() / lambda_melloss  # Log training mel regression loss to tensorboard
-                    sw.add_scalar("training/gen_loss_total", loss_gen_all.item(), steps)
-                    sw.add_scalar("training/mel_spec_error", mel_error, steps)
-                    sw.add_scalar("training/fm_loss_mpd", loss_fm_f.item(), steps)
-                    sw.add_scalar("training/gen_loss_mpd", loss_gen_f.item(), steps)
-                    sw.add_scalar("training/disc_loss_mpd", loss_disc_f.item(), steps)
-                    sw.add_scalar("training/grad_norm_mpd", grad_norm_mpd, steps)
-                    sw.add_scalar("training/fm_loss_mrd", loss_fm_s.item(), steps)
-                    sw.add_scalar("training/gen_loss_mrd", loss_gen_s.item(), steps)
-                    sw.add_scalar("training/disc_loss_mrd", loss_disc_s.item(), steps)
-                    sw.add_scalar("training/grad_norm_mrd", grad_norm_mrd, steps)
-                    sw.add_scalar("training/grad_norm_g", grad_norm_g, steps)
-                    sw.add_scalar("training/learning_rate_d", scheduler_d.get_last_lr()[0], steps)
-                    sw.add_scalar("training/learning_rate_g", scheduler_g.get_last_lr()[0], steps)
-                    sw.add_scalar("training/epoch", epoch + 1, steps)
-
-                # Validation
-                if steps % a.validation_interval == 0:
-                    # Plot training input x so far used
-                    for i_x in range(x.shape[0]):
-                        sw.add_figure(
-                            f"training_input/x_{i_x}",
-                            plot_spectrogram(x[i_x].cpu()),
-                            steps,
-                        )
-                        sw.add_audio(
-                            f"training_input/y_{i_x}",
-                            y[i_x][0],
-                            steps,
-                            h.sampling_rate,
-                        )
-
-                    # Seen and unseen speakers validation loops
-                    if not a.debug and steps != 0:
-                        validate(
-                            rank,
-                            a,
-                            h,
-                            validation_loader,
-                            mode=f"seen_{train_loader.dataset.name}",
-                        )
-                        for i in range(len(list_unseen_validation_loader)):
-                            validate(
-                                rank,
-                                a,
-                                h,
-                                list_unseen_validation_loader[i],
-                                mode=f"unseen_{list_unseen_validation_loader[i].dataset.name}",
-                            )
-            steps += 1
-
-            # BigVGAN-v2 learning rate scheduler is changed from epoch-level to step-level
-            scheduler_g.step()
-            scheduler_d.step()
-
-        if rank == 0:
-            print(f"Time taken for epoch {epoch + 1} is {int(time.time() - start)} sec\n")
-
-
-def main():
-    print("Initializing Training Process..")
-
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--group_name", default=None)
-
-    parser.add_argument("--input_wavs_dir", default="LibriTTS")
-    parser.add_argument("--input_mels_dir", default="ft_dataset")
-    parser.add_argument("--input_training_file", default="tests/LibriTTS/train-full.txt")
-    parser.add_argument("--input_validation_file", default="tests/LibriTTS/val-full.txt")
-
-    parser.add_argument(
-        "--list_input_unseen_wavs_dir",
-        nargs="+",
-        default=["tests/LibriTTS", "tests/LibriTTS"],
-    )
-    parser.add_argument(
-        "--list_input_unseen_validation_file",
-        nargs="+",
-        default=["tests/LibriTTS/dev-clean.txt", "tests/LibriTTS/dev-other.txt"],
-    )
-
-    parser.add_argument("--checkpoint_path", default="exp/bigvgan")
-    parser.add_argument("--config", default="")
-
-    parser.add_argument("--training_epochs", default=100000, type=int)
-    parser.add_argument("--stdout_interval", default=5, type=int)
-    parser.add_argument("--checkpoint_interval", default=50000, type=int)
-    parser.add_argument("--summary_interval", default=100, type=int)
-    parser.add_argument("--validation_interval", default=50000, type=int)
-
-    parser.add_argument(
-        "--freeze_step",
-        default=0,
-        type=int,
-        help="freeze D for the first specified steps. G only uses regression loss for these steps.",
-    )
-
-    parser.add_argument("--fine_tuning", default=False, type=bool)
-
-    parser.add_argument(
-        "--debug",
-        default=False,
-        type=bool,
-        help="debug mode. skips validation loop throughout training",
-    )
-    parser.add_argument(
-        "--evaluate",
-        default=False,
-        type=bool,
-        help="only run evaluation from checkpoint and exit",
-    )
-    parser.add_argument(
-        "--eval_subsample",
-        default=5,
-        type=int,
-        help="subsampling during evaluation loop",
-    )
-    parser.add_argument(
-        "--skip_seen",
-        default=False,
-        type=bool,
-        help="skip seen dataset. useful for test set inference",
-    )
-    parser.add_argument(
-        "--save_audio",
-        default=False,
-        type=bool,
-        help="save audio of test set inference to disk",
-    )
-
-    a = parser.parse_args()
-
-    with open(a.config) as f:
-        data = f.read()
-
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-
-    build_env(a.config, "config.json", a.checkpoint_path)
-
-    torch.manual_seed(h.seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(h.seed)
-        h.num_gpus = torch.cuda.device_count()
-        h.batch_size = int(h.batch_size / h.num_gpus)
-        print(f"Batch size per GPU: {h.batch_size}")
-    else:
-        pass
-
-    if h.num_gpus > 1:
-        mp.spawn(
-            train,
-            nprocs=h.num_gpus,
-            args=(
-                a,
-                h,
-            ),
-        )
-    else:
-        train(0, a, h)
-
-
-if __name__ == "__main__":
-    main()

GPT_SoVITS/BigVGAN/utils0.py

@@ -1,99 +0,0 @@
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-
-matplotlib.use("Agg")
-import matplotlib.pylab as plt
-from .meldataset import MAX_WAV_VALUE
-from scipy.io.wavfile import write
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-def plot_spectrogram_clipped(spectrogram, clip_max=2.0):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(
-        spectrogram,
-        aspect="auto",
-        origin="lower",
-        interpolation="none",
-        vmin=1e-6,
-        vmax=clip_max,
-    )
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print(f"Loading '{filepath}'")
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print(f"Saving checkpoint to {filepath}")
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def scan_checkpoint(cp_dir, prefix, renamed_file=None):
-    # Fallback to original scanning logic first
-    pattern = os.path.join(cp_dir, prefix + "????????")
-    cp_list = glob.glob(pattern)
-
-    if len(cp_list) > 0:
-        last_checkpoint_path = sorted(cp_list)[-1]
-        print(f"[INFO] Resuming from checkpoint: '{last_checkpoint_path}'")
-        return last_checkpoint_path
-
-    # If no pattern-based checkpoints are found, check for renamed file
-    if renamed_file:
-        renamed_path = os.path.join(cp_dir, renamed_file)
-        if os.path.isfile(renamed_path):
-            print(f"[INFO] Resuming from renamed checkpoint: '{renamed_file}'")
-            return renamed_path
-
-    return None
-
-
-def save_audio(audio, path, sr):
-    # wav: torch with 1d shape
-    audio = audio * MAX_WAV_VALUE
-    audio = audio.cpu().numpy().astype("int16")
-    write(path, sr, audio)

GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py

@@ -1,37 +1,31 @@
-import os
-import sys
-import threading
+
+import os, sys
 
 from tqdm import tqdm
-
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 
 import re
 import torch
-from text.LangSegmenter import LangSegmenter
-from text import chinese
+import LangSegment
+
 from typing import Dict, List, Tuple
 from text.cleaner import clean_text
 from text import cleaned_text_to_sequence
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method
 
-from tools.i18n.i18n import I18nAuto, scan_language_list
+from tools.i18n.i18n import I18nAuto
 
-language = os.environ.get("language", "Auto")
-language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
-i18n = I18nAuto(language=language)
-punctuation = set(["!", "?", "…", ",", ".", "-"])
+i18n = I18nAuto()
+punctuation = set(['!', '?', '…', ',', '.', '-'," "])
 
-
-def get_first(text: str) -> str:
+def get_first(text:str) -> str:
     pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
     text = re.split(pattern, text)[0].strip()
     return text
 
-
-def merge_short_text_in_array(texts: str, threshold: int) -> list:
+def merge_short_text_in_array(texts:str, threshold:int) -> list:
     if (len(texts)) < 2:
         return texts
     result = []
@@ -41,7 +35,7 @@ def merge_short_text_in_array(texts: str, threshold: int) -> list:
         if len(text) >= threshold:
             result.append(text)
             text = ""
-    if len(text) > 0:
+    if (len(text) > 0):
         if len(result) == 0:
             result.append(text)
         else:
@@ -49,24 +43,28 @@ def merge_short_text_in_array(texts: str, threshold: int) -> list:
     return result
 
 
+
+
+
+
 class TextPreprocessor:
-    def __init__(self, bert_model: AutoModelForMaskedLM, tokenizer: AutoTokenizer, device: torch.device):
+    def __init__(self, bert_model:AutoModelForMaskedLM, 
+                 tokenizer:AutoTokenizer, device:torch.device):
         self.bert_model = bert_model
         self.tokenizer = tokenizer
         self.device = device
-        self.bert_lock = threading.RLock()
-
-    def preprocess(self, text: str, lang: str, text_split_method: str, version: str = "v2") -> List[Dict]:
-        print(f"############ {i18n('切分文本')} ############")
-        text = self.replace_consecutive_punctuation(text)
+        
+    def preprocess(self, text:str, lang:str, text_split_method:str)->List[Dict]:
+        print(i18n("############ 切分文本 ############"))
+        text = self.replace_consecutive_punctuation(text) # 变量命名应该是写错了
         texts = self.pre_seg_text(text, lang, text_split_method)
         result = []
-        print(f"############ {i18n('提取文本Bert特征')} ############")
+        print(i18n("############ 提取文本Bert特征 ############"))
         for text in tqdm(texts):
-            phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang, version)
-            if phones is None or norm_text == "":
+            phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang)
+            if phones is None:
                 continue
-            res = {
+            res={
                 "phones": phones,
                 "bert_features": bert_features,
                 "norm_text": norm_text,
@@ -74,119 +72,115 @@ class TextPreprocessor:
             result.append(res)
         return result
 
-    def pre_seg_text(self, text: str, lang: str, text_split_method: str):
+    def pre_seg_text(self, text:str, lang:str, text_split_method:str):
         text = text.strip("\n")
-        if len(text) == 0:
-            return []
-        if text[0] not in splits and len(get_first(text)) < 4:
+        if (text[0] not in splits and len(get_first(text)) < 4): 
             text = "。" + text if lang != "en" else "." + text
         print(i18n("实际输入的目标文本:"))
         print(text)
-
+        
         seg_method = get_seg_method(text_split_method)
         text = seg_method(text)
-
+        
         while "\n\n" in text:
             text = text.replace("\n\n", "\n")
 
         _texts = text.split("\n")
-        _texts = self.filter_text(_texts)
+        _texts = self.process_text(_texts)
         _texts = merge_short_text_in_array(_texts, 5)
         texts = []
 
+        
         for text in _texts:
             # 解决输入目标文本的空行导致报错的问题
-            if len(text.strip()) == 0:
-                continue
-            if not re.sub("\W+", "", text):
+            if (len(text.strip()) == 0):
+               continue
+            if not re.sub("\W+", "", text):       
                 # 检测一下，如果是纯符号，就跳过。
                 continue
-            if text[-1] not in splits:
-                text += "。" if lang != "en" else "."
-
+            if (text[-1] not in splits): text += "。" if lang != "en" else "."
+            
             # 解决句子过长导致Bert报错的问题
-            if len(text) > 510:
+            if (len(text) > 510):
                 texts.extend(split_big_text(text))
             else:
                 texts.append(text)
-
+            
         print(i18n("实际输入的目标文本(切句后):"))
         print(texts)
         return texts
+    
+    def segment_and_extract_feature_for_text(self, texts:list, language:str)->Tuple[list, torch.Tensor, str]:
+        textlist, langlist = self.seg_text(texts, language)
+        if len(textlist) == 0:
+            return None, None, None
+        
+        phones, bert_features, norm_text = self.extract_bert_feature(textlist, langlist)
+        return phones, bert_features, norm_text
 
-    def segment_and_extract_feature_for_text(
-        self, text: str, language: str, version: str = "v1"
-    ) -> Tuple[list, torch.Tensor, str]:
-        return self.get_phones_and_bert(text, language, version)
 
-    def get_phones_and_bert(self, text: str, language: str, version: str, final: bool = False):
-        with self.bert_lock:
-            if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
-                # language = language.replace("all_","")
-                formattext = text
-                while "  " in formattext:
-                    formattext = formattext.replace("  ", " ")
-                if language == "all_zh":
-                    if re.search(r"[A-Za-z]", formattext):
-                        formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
-                        formattext = chinese.mix_text_normalize(formattext)
-                        return self.get_phones_and_bert(formattext, "zh", version)
-                    else:
-                        phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
-                        bert = self.get_bert_feature(norm_text, word2ph).to(self.device)
-                elif language == "all_yue" and re.search(r"[A-Za-z]", formattext):
-                    formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
-                    formattext = chinese.mix_text_normalize(formattext)
-                    return self.get_phones_and_bert(formattext, "yue", version)
+    def seg_text(self, text:str, language:str)->Tuple[list, list]:
+
+        textlist=[]
+        langlist=[]
+        if language in ["auto", "zh", "ja"]:
+            LangSegment.setfilters(["zh","ja","en","ko"])
+            for tmp in LangSegment.getTexts(text):
+                if tmp["text"] == "":
+                    continue
+                if tmp["lang"] == "ko":
+                    langlist.append("zh")
+                elif tmp["lang"] == "en":
+                    langlist.append("en")
                 else:
-                    phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
-                    bert = torch.zeros(
-                        (1024, len(phones)),
-                        dtype=torch.float32,
-                    ).to(self.device)
-            elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
-                textlist = []
-                langlist = []
-                if language == "auto":
-                    for tmp in LangSegmenter.getTexts(text):
-                        langlist.append(tmp["lang"])
-                        textlist.append(tmp["text"])
-                elif language == "auto_yue":
-                    for tmp in LangSegmenter.getTexts(text):
-                        if tmp["lang"] == "zh":
-                            tmp["lang"] = "yue"
-                        langlist.append(tmp["lang"])
-                        textlist.append(tmp["text"])
-                else:
-                    for tmp in LangSegmenter.getTexts(text):
-                        if tmp["lang"] == "en":
-                            langlist.append(tmp["lang"])
-                        else:
-                            # 因无法区别中日韩文汉字,以用户输入为准
-                            langlist.append(language)
-                        textlist.append(tmp["text"])
-                # print(textlist)
-                # print(langlist)
-                phones_list = []
-                bert_list = []
-                norm_text_list = []
-                for i in range(len(textlist)):
-                    lang = langlist[i]
-                    phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang, version)
-                    bert = self.get_bert_inf(phones, word2ph, norm_text, lang)
-                    phones_list.append(phones)
-                    norm_text_list.append(norm_text)
-                    bert_list.append(bert)
-                bert = torch.cat(bert_list, dim=1)
-                phones = sum(phones_list, [])
-                norm_text = "".join(norm_text_list)
+                    # 因无法区别中日文汉字,以用户输入为准
+                    langlist.append(language if language!="auto" else tmp["lang"])
+                textlist.append(tmp["text"])
+        elif language == "en":
+            LangSegment.setfilters(["en"])
+            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
+            while "  " in formattext:
+                formattext = formattext.replace("  ", " ")
+            if formattext != "":
+                textlist.append(formattext)
+                langlist.append("en")
+            
+        elif language in ["all_zh","all_ja"]:
 
-            if not final and len(phones) < 6:
-                return self.get_phones_and_bert("." + text, language, version, final=True)
+            formattext = text
+            while "  " in formattext:
+                formattext = formattext.replace("  ", " ")
+            language = language.replace("all_","")
+            if text == "":
+                return [],[]
+            textlist.append(formattext)
+            langlist.append(language) 
+        
+        else:
+            raise ValueError(f"language {language} not supported")
+        
+        return textlist, langlist
+    
 
-            return phones, bert, norm_text
+    def extract_bert_feature(self, textlist:list, langlist:list):
+        phones_list = []
+        bert_feature_list = []
+        norm_text_list = []
+        for i in range(len(textlist)):
+            lang = langlist[i]
+            phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang)
+            _bert_feature = self.get_bert_inf(phones, word2ph, norm_text, lang)
+            # phones_list.append(phones)
+            phones_list.extend(phones)
+            norm_text_list.append(norm_text)
+            bert_feature_list.append(_bert_feature)
+        bert_feature = torch.cat(bert_feature_list, dim=1)
+        # phones = sum(phones_list, [])
+        norm_text = ''.join(norm_text_list)
+        return phones_list, bert_feature, norm_text
 
-    def get_bert_feature(self, text: str, word2ph: list) -> torch.Tensor:
+
+    def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor:
         with torch.no_grad():
             inputs = self.tokenizer(text, return_tensors="pt")
             for i in inputs:
@@ -200,15 +194,14 @@ class TextPreprocessor:
             phone_level_feature.append(repeat_feature)
         phone_level_feature = torch.cat(phone_level_feature, dim=0)
         return phone_level_feature.T
-
-    def clean_text_inf(self, text: str, language: str, version: str = "v2"):
-        language = language.replace("all_", "")
-        phones, word2ph, norm_text = clean_text(text, language, version)
-        phones = cleaned_text_to_sequence(phones, version)
+    
+    def clean_text_inf(self, text:str, language:str):
+        phones, word2ph, norm_text = clean_text(text, language)
+        phones = cleaned_text_to_sequence(phones)
         return phones, word2ph, norm_text
 
-    def get_bert_inf(self, phones: list, word2ph: list, norm_text: str, language: str):
-        language = language.replace("all_", "")
+    def get_bert_inf(self, phones:list, word2ph:list, norm_text:str, language:str):
+        language=language.replace("all_","")
         if language == "zh":
             feature = self.get_bert_feature(norm_text, word2ph).to(self.device)
         else:
@@ -218,20 +211,24 @@ class TextPreprocessor:
             ).to(self.device)
 
         return feature
-
-    def filter_text(self, texts):
-        _text = []
-        if all(text in [None, " ", "\n", ""] for text in texts):
+    
+    def process_text(self,texts):
+        _text=[]
+        if all(text in [None, " ", "\n",""] for text in texts):
             raise ValueError(i18n("请输入有效文本"))
         for text in texts:
-            if text in [None, " ", ""]:
+            if text in  [None, " ", ""]:
                 pass
             else:
                 _text.append(text)
         return _text
+    
 
-    def replace_consecutive_punctuation(self, text):
-        punctuations = "".join(re.escape(p) for p in punctuation)
-        pattern = f"([{punctuations}])([{punctuations}])+"
-        result = re.sub(pattern, r"\1", text)
+    def replace_consecutive_punctuation(self,text):
+        punctuations = ''.join(re.escape(p) for p in punctuation)
+        pattern = f'([{punctuations}])([{punctuations}])+'
+        result = re.sub(pattern, r'\1', text)
         return result
+
+
+

GPT_SoVITS/TTS_infer_pack/__init__.py

@@ -1 +1 @@
-from . import TTS, text_segmentation_method
+from . import TTS, text_segmentation_method

GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py

@@ -1,57 +1,43 @@
+
+
+
+
 import re
 from typing import Callable
+from tools.i18n.i18n import I18nAuto
 
-punctuation = set(["!", "?", "…", ",", ".", "-", " "])
+i18n = I18nAuto()
+punctuation = set(['!', '?', '…', ',', '.', '-'," "])
 METHODS = dict()
 
-
-def get_method(name: str) -> Callable:
+def get_method(name:str)->Callable:
     method = METHODS.get(name, None)
     if method is None:
         raise ValueError(f"Method {name} not found")
     return method
 
-
-def get_method_names() -> list:
+def get_method_names()->list:
     return list(METHODS.keys())
 
-
 def register_method(name):
     def decorator(func):
         METHODS[name] = func
         return func
-
     return decorator
 
-
-splits = {
-    "，",
-    "。",
-    "？",
-    "！",
-    ",",
-    ".",
-    "?",
-    "!",
-    "~",
-    ":",
-    "：",
-    "—",
-    "…",
-}
-
+splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
 
 def split_big_text(text, max_len=510):
     # 定义全角和半角标点符号
     punctuation = "".join(splits)
 
     # 切割文本
-    segments = re.split("([" + punctuation + "])", text)
-
+    segments = re.split('([' + punctuation + '])', text)
+    
     # 初始化结果列表和当前片段
     result = []
-    current_segment = ""
-
+    current_segment = ''
+    
     for segment in segments:
         # 如果当前片段加上新的片段长度超过max_len，就将当前片段加入结果列表，并重置当前片段
         if len(current_segment + segment) > max_len:
@@ -59,14 +45,15 @@ def split_big_text(text, max_len=510):
             current_segment = segment
         else:
             current_segment += segment
-
+    
     # 将最后一个片段加入结果列表
     if current_segment:
         result.append(current_segment)
-
+    
     return result
 
 
+
 def split(todo_text):
     todo_text = todo_text.replace("……", "。").replace("——", "，")
     if todo_text[-1] not in splits:
@@ -101,17 +88,17 @@ def cut1(inp):
     inp = inp.strip("\n")
     inps = split(inp)
     split_idx = list(range(0, len(inps), 4))
-    split_idx[-1] = None
+    # split_idx[-1] = None
+    split_idx.append(None)
     if len(split_idx) > 1:
         opts = []
         for idx in range(len(split_idx) - 1):
-            opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
+            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
     else:
         opts = [inp]
     opts = [item for item in opts if not set(item).issubset(punctuation)]
     return "\n".join(opts)
 
-
 # 凑50字一切
 @register_method("cut2")
 def cut2(inp):
@@ -138,7 +125,6 @@ def cut2(inp):
     opts = [item for item in opts if not set(item).issubset(punctuation)]
     return "\n".join(opts)
 
-
 # 按中文句号。切
 @register_method("cut3")
 def cut3(inp):
@@ -147,43 +133,35 @@ def cut3(inp):
     opts = [item for item in opts if not set(item).issubset(punctuation)]
     return "\n".join(opts)
 
-
-# 按英文句号.切
+#按英文句号.切
 @register_method("cut4")
 def cut4(inp):
     inp = inp.strip("\n")
-    opts = re.split(r"(?<!\d)\.(?!\d)", inp.strip("."))
+    opts = ["%s" % item for item in inp.strip(".").split(".")]
     opts = [item for item in opts if not set(item).issubset(punctuation)]
     return "\n".join(opts)
 
-
 # 按标点符号切
 # contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
 @register_method("cut5")
 def cut5(inp):
+    # if not re.search(r'[^\w\s]', inp[-1]):
+    # inp += '。'
     inp = inp.strip("\n")
-    punds = {",", ".", ";", "?", "!", "、", "，", "。", "？", "！", ";", "：", "…"}
-    mergeitems = []
-    items = []
-
-    for i, char in enumerate(inp):
-        if char in punds:
-            if char == "." and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
-                items.append(char)
-            else:
-                items.append(char)
-                mergeitems.append("".join(items))
-                items = []
-        else:
-            items.append(char)
-
-    if items:
-        mergeitems.append("".join(items))
-
-    opt = [item for item in mergeitems if not set(item).issubset(punds)]
-    return "\n".join(opt)
+    # punds = r'[,.;?!、，。？！;：…]'
+    punds = r'[,.;?!、，。？！；：:…]'
+    items = re.split(f'({punds})', inp)
+    mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
+    # 在句子不存在符号或句尾无符号的时候保证文本完整
+    if len(items)%2 == 1:
+        mergeitems.append(items[-1])
+    opts = [item for item in mergeitems if not set(item).issubset(punctuation)]
+    opts = "\n".join(opts)
+    return opts
 
 
-if __name__ == "__main__":
+
+if __name__ == '__main__':
     method = get_method("cut5")
     print(method("你好，我是小明。你好，我是小红。你好，我是小刚。你好，我是小张。"))
+    

GPT_SoVITS/configs/.gitignore

@@ -1 +0,0 @@
-*.yaml

GPT_SoVITS/configs/s1longer-v2.yaml

@@ -1,31 +0,0 @@
-train:
-  seed: 1234
-  epochs: 20
-  batch_size: 8
-  save_every_n_epoch: 1
-  precision: 16-mixed
-  gradient_clip: 1.0
-optimizer:
-  lr: 0.01
-  lr_init: 0.00001
-  lr_end: 0.0001
-  warmup_steps: 2000
-  decay_steps: 40000
-data:
-  max_eval_sample: 8
-  max_sec: 54
-  num_workers: 4
-  pad_val: 1024 # same with EOS in model
-model:
-  vocab_size: 1025
-  phoneme_vocab_size: 732
-  embedding_dim: 512
-  hidden_dim: 512
-  head: 16
-  linear_units: 2048
-  n_layer: 24
-  dropout: 0
-  EOS: 1024
-  random_bert: 0
-inference:
-  top_k: 15

GPT_SoVITS/configs/s2.json

@@ -18,8 +18,7 @@
     "warmup_epochs": 0,
     "c_mel": 45,
     "c_kl": 1.0,
-    "text_low_lr_rate": 0.4, 
-    "grad_ckpt": false
+    "text_low_lr_rate": 0.4
   },
   "data": {
     "max_wav_value": 32768.0,

GPT_SoVITS/configs/s2v2Pro.json

@@ -1,91 +0,0 @@
-{
-  "train": {
-    "log_interval": 100,
-    "eval_interval": 500,
-    "seed": 1234,
-    "epochs": 100,
-    "learning_rate": 0.0001,
-    "betas": [
-      0.8,
-      0.99
-    ],
-    "eps": 1e-09,
-    "batch_size": 32,
-    "fp16_run": true,
-    "lr_decay": 0.999875,
-    "segment_size": 20480,
-    "init_lr_ratio": 1,
-    "warmup_epochs": 0,
-    "c_mel": 45,
-    "c_kl": 1.0,
-    "text_low_lr_rate": 0.4, 
-    "grad_ckpt": false
-  },
-  "data": {
-    "max_wav_value": 32768.0,
-    "sampling_rate": 32000,
-    "filter_length": 2048,
-    "hop_length": 640,
-    "win_length": 2048,
-    "n_mel_channels": 128,
-    "mel_fmin": 0.0,
-    "mel_fmax": null,
-    "add_blank": true,
-    "n_speakers": 300,
-    "cleaned_text": true
-  },
-  "model": {
-    "inter_channels": 192,
-    "hidden_channels": 192,
-    "filter_channels": 768,
-    "n_heads": 2,
-    "n_layers": 6,
-    "kernel_size": 3,
-    "p_dropout": 0.0,
-    "resblock": "1",
-    "resblock_kernel_sizes": [
-      3,
-      7,
-      11
-    ],
-    "resblock_dilation_sizes": [
-      [
-        1,
-        3,
-        5
-      ],
-      [
-        1,
-        3,
-        5
-      ],
-      [
-        1,
-        3,
-        5
-      ]
-    ],
-    "upsample_rates": [
-      10,
-      8,
-      2,
-      2,
-      2
-    ],
-    "upsample_initial_channel": 512,
-    "upsample_kernel_sizes": [
-      16,
-      16,
-      8,
-      2,
-      2
-    ],
-    "n_layers_q": 3,
-    "use_spectral_norm": false,
-    "gin_channels": 1024,
-    "semantic_frame_rate": "25hz",
-    "freeze_quantizer": true
-  },
-  "s2_ckpt_dir": "logs/s2/big2k1",
-  "content_module": "cnhubert"
-}