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Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

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* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab
This commit is contained in:
XXXXRT666
2025-04-07 09:42:47 +01:00
committed by GitHub
parent 9da7e17efe
commit 53cac93589
132 changed files with 8185 additions and 6648 deletions
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268
api_v2.py
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@@ -78,7 +78,7 @@ GET:
```
http://127.0.0.1:9880/set_gpt_weights?weights_path=GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
```
RESP:
RESP:
成功: 返回"success", http code 200
失败: 返回包含错误信息的 json, http code 400
@@ -92,11 +92,12 @@ GET:
http://127.0.0.1:9880/set_sovits_weights?weights_path=GPT_SoVITS/pretrained_models/s2G488k.pth
```
RESP:
RESP:
成功: 返回"success", http code 200
失败: 返回包含错误信息的 json, http code 400
"""
import os
import sys
import traceback
@@ -112,16 +113,15 @@ import wave
import signal
import numpy as np
import soundfile as sf
from fastapi import FastAPI, Request, HTTPException, Response
from fastapi import FastAPI, Response
from fastapi.responses import StreamingResponse, JSONResponse
from fastapi import FastAPI, UploadFile, File
import uvicorn
from io import BytesIO
from tools.i18n.i18n import I18nAuto
from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config
from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method_names as get_cut_method_names
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
# print(sys.path)
i18n = I18nAuto()
cut_method_names = get_cut_method_names()
@@ -145,6 +145,8 @@ print(tts_config)
tts_pipeline = TTS(tts_config)
APP = FastAPI()
class TTS_Request(BaseModel):
text: str = None
text_lang: str = None
@@ -152,58 +154,73 @@ class TTS_Request(BaseModel):
aux_ref_audio_paths: list = None
prompt_lang: str = None
prompt_text: str = ""
top_k:int = 5
top_p:float = 1
temperature:float = 1
text_split_method:str = "cut5"
batch_size:int = 1
batch_threshold:float = 0.75
split_bucket:bool = True
speed_factor:float = 1.0
fragment_interval:float = 0.3
seed:int = -1
media_type:str = "wav"
streaming_mode:bool = False
parallel_infer:bool = True
repetition_penalty:float = 1.35
sample_steps:int = 32
super_sampling:bool = False
top_k: int = 5
top_p: float = 1
temperature: float = 1
text_split_method: str = "cut5"
batch_size: int = 1
batch_threshold: float = 0.75
split_bucket: bool = True
speed_factor: float = 1.0
fragment_interval: float = 0.3
seed: int = -1
media_type: str = "wav"
streaming_mode: bool = False
parallel_infer: bool = True
repetition_penalty: float = 1.35
sample_steps: int = 32
super_sampling: bool = False
### modify from https://github.com/RVC-Boss/GPT-SoVITS/pull/894/files
def pack_ogg(io_buffer:BytesIO, data:np.ndarray, rate:int):
with sf.SoundFile(io_buffer, mode='w', samplerate=rate, channels=1, format='ogg') as audio_file:
def pack_ogg(io_buffer: BytesIO, data: np.ndarray, rate: int):
with sf.SoundFile(io_buffer, mode="w", samplerate=rate, channels=1, format="ogg") as audio_file:
audio_file.write(data)
return io_buffer
def pack_raw(io_buffer:BytesIO, data:np.ndarray, rate:int):
def pack_raw(io_buffer: BytesIO, data: np.ndarray, rate: int):
io_buffer.write(data.tobytes())
return io_buffer
def pack_wav(io_buffer:BytesIO, data:np.ndarray, rate:int):
def pack_wav(io_buffer: BytesIO, data: np.ndarray, rate: int):
io_buffer = BytesIO()
sf.write(io_buffer, data, rate, format='wav')
sf.write(io_buffer, data, rate, format="wav")
return io_buffer
def pack_aac(io_buffer:BytesIO, data:np.ndarray, rate:int):
process = subprocess.Popen([
'ffmpeg',
'-f', 's16le', # 输入16位有符号小端整数PCM
'-ar', str(rate), # 设置采样率
'-ac', '1', # 单声道
'-i', 'pipe:0', # 从管道读取输入
'-c:a', 'aac', # 音频编码器为AAC
'-b:a', '192k', # 比特率
'-vn', # 不包含视频
'-f', 'adts', # 输出AAC数据流格式
'pipe:1' # 将输出写入管道
], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
def pack_aac(io_buffer: BytesIO, data: np.ndarray, rate: int):
process = subprocess.Popen(
[
"ffmpeg",
"-f",
"s16le", # 输入16位有符号小端整数PCM
"-ar",
str(rate), # 设置采样率
"-ac",
"1", # 单声道
"-i",
"pipe:0", # 从管道读取输入
"-c:a",
"aac", # 音频编码器为AAC
"-b:a",
"192k", # 比特率
"-vn", # 不包含视频
"-f",
"adts", # 输出AAC数据流格式
"pipe:1", # 将输出写入管道
],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
out, _ = process.communicate(input=data.tobytes())
io_buffer.write(out)
return io_buffer
def pack_audio(io_buffer:BytesIO, data:np.ndarray, rate:int, media_type:str):
def pack_audio(io_buffer: BytesIO, data: np.ndarray, rate: int, media_type: str):
if media_type == "ogg":
io_buffer = pack_ogg(io_buffer, data, rate)
elif media_type == "aac":
@@ -216,7 +233,6 @@ def pack_audio(io_buffer:BytesIO, data:np.ndarray, rate:int, media_type:str):
return io_buffer
# from https://huggingface.co/spaces/coqui/voice-chat-with-mistral/blob/main/app.py
def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=32000):
# This will create a wave header then append the frame input
@@ -233,7 +249,7 @@ def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=3
return wav_buf.read()
def handle_control(command:str):
def handle_control(command: str):
if command == "restart":
os.execl(sys.executable, sys.executable, *argv)
elif command == "exit":
@@ -241,43 +257,52 @@ def handle_control(command:str):
exit(0)
def check_params(req:dict):
text:str = req.get("text", "")
text_lang:str = req.get("text_lang", "")
ref_audio_path:str = req.get("ref_audio_path", "")
streaming_mode:bool = req.get("streaming_mode", False)
media_type:str = req.get("media_type", "wav")
prompt_lang:str = req.get("prompt_lang", "")
text_split_method:str = req.get("text_split_method", "cut5")
def check_params(req: dict):
text: str = req.get("text", "")
text_lang: str = req.get("text_lang", "")
ref_audio_path: str = req.get("ref_audio_path", "")
streaming_mode: bool = req.get("streaming_mode", False)
media_type: str = req.get("media_type", "wav")
prompt_lang: str = req.get("prompt_lang", "")
text_split_method: str = req.get("text_split_method", "cut5")
if ref_audio_path in [None, ""]:
return JSONResponse(status_code=400, content={"message": "ref_audio_path is required"})
if text in [None, ""]:
return JSONResponse(status_code=400, content={"message": "text is required"})
if (text_lang in [None, ""]) :
if text_lang in [None, ""]:
return JSONResponse(status_code=400, content={"message": "text_lang is required"})
elif text_lang.lower() not in tts_config.languages:
return JSONResponse(status_code=400, content={"message": f"text_lang: {text_lang} is not supported in version {tts_config.version}"})
if (prompt_lang in [None, ""]) :
return JSONResponse(
status_code=400,
content={"message": f"text_lang: {text_lang} is not supported in version {tts_config.version}"},
)
if prompt_lang in [None, ""]:
return JSONResponse(status_code=400, content={"message": "prompt_lang is required"})
elif prompt_lang.lower() not in tts_config.languages:
return JSONResponse(status_code=400, content={"message": f"prompt_lang: {prompt_lang} is not supported in version {tts_config.version}"})
return JSONResponse(
status_code=400,
content={"message": f"prompt_lang: {prompt_lang} is not supported in version {tts_config.version}"},
)
if media_type not in ["wav", "raw", "ogg", "aac"]:
return JSONResponse(status_code=400, content={"message": f"media_type: {media_type} is not supported"})
elif media_type == "ogg" and not streaming_mode:
elif media_type == "ogg" and not streaming_mode:
return JSONResponse(status_code=400, content={"message": "ogg format is not supported in non-streaming mode"})
if text_split_method not in cut_method_names:
return JSONResponse(status_code=400, content={"message": f"text_split_method:{text_split_method} is not supported"})
return JSONResponse(
status_code=400, content={"message": f"text_split_method:{text_split_method} is not supported"}
)
return None
async def tts_handle(req:dict):
async def tts_handle(req: dict):
"""
Text to speech handler.
Args:
req (dict):
req (dict):
{
"text": "", # str.(required) text to be synthesized
"text_lang: "", # str.(required) language of the text to be synthesized
@@ -298,14 +323,14 @@ async def tts_handle(req:dict):
"media_type": "wav", # str. media type of the output audio, support "wav", "raw", "ogg", "aac".
"streaming_mode": False, # bool. whether to return a streaming response.
"parallel_infer": True, # bool.(optional) whether to use parallel inference.
"repetition_penalty": 1.35 # float.(optional) repetition penalty for T2S model.
"repetition_penalty": 1.35 # float.(optional) repetition penalty for T2S model.
"sample_steps": 32, # int. number of sampling steps for VITS model V3.
"super_sampling": False, # bool. whether to use super-sampling for audio when using VITS model V3.
"super_sampling": False, # bool. whether to use super-sampling for audio when using VITS model V3.
}
returns:
StreamingResponse: audio stream response.
"""
streaming_mode = req.get("streaming_mode", False)
return_fragment = req.get("return_fragment", False)
media_type = req.get("media_type", "wav")
@@ -316,12 +341,13 @@ async def tts_handle(req:dict):
if streaming_mode or return_fragment:
req["return_fragment"] = True
try:
tts_generator=tts_pipeline.run(req)
tts_generator = tts_pipeline.run(req)
if streaming_mode:
def streaming_generator(tts_generator:Generator, media_type:str):
def streaming_generator(tts_generator: Generator, media_type: str):
if_frist_chunk = True
for sr, chunk in tts_generator:
if if_frist_chunk and media_type == "wav":
@@ -329,19 +355,22 @@ async def tts_handle(req:dict):
media_type = "raw"
if_frist_chunk = False
yield pack_audio(BytesIO(), chunk, sr, media_type).getvalue()
# _media_type = f"audio/{media_type}" if not (streaming_mode and media_type in ["wav", "raw"]) else f"audio/x-{media_type}"
return StreamingResponse(streaming_generator(tts_generator, media_type, ), media_type=f"audio/{media_type}")
return StreamingResponse(
streaming_generator(
tts_generator,
media_type,
),
media_type=f"audio/{media_type}",
)
else:
sr, audio_data = next(tts_generator)
audio_data = pack_audio(BytesIO(), audio_data, sr, media_type).getvalue()
return Response(audio_data, media_type=f"audio/{media_type}")
except Exception as e:
return JSONResponse(status_code=400, content={"message": f"tts failed", "Exception": str(e)})
return JSONResponse(status_code=400, content={"message": "tts failed", "Exception": str(e)})
@APP.get("/control")
@@ -351,32 +380,31 @@ async def control(command: str = None):
handle_control(command)
@APP.get("/tts")
async def tts_get_endpoint(
text: str = None,
text_lang: str = None,
ref_audio_path: str = None,
aux_ref_audio_paths:list = None,
prompt_lang: str = None,
prompt_text: str = "",
top_k:int = 5,
top_p:float = 1,
temperature:float = 1,
text_split_method:str = "cut0",
batch_size:int = 1,
batch_threshold:float = 0.75,
split_bucket:bool = True,
speed_factor:float = 1.0,
fragment_interval:float = 0.3,
seed:int = -1,
media_type:str = "wav",
streaming_mode:bool = False,
parallel_infer:bool = True,
repetition_penalty:float = 1.35,
sample_steps:int =32,
super_sampling:bool = False
):
text: str = None,
text_lang: str = None,
ref_audio_path: str = None,
aux_ref_audio_paths: list = None,
prompt_lang: str = None,
prompt_text: str = "",
top_k: int = 5,
top_p: float = 1,
temperature: float = 1,
text_split_method: str = "cut0",
batch_size: int = 1,
batch_threshold: float = 0.75,
split_bucket: bool = True,
speed_factor: float = 1.0,
fragment_interval: float = 0.3,
seed: int = -1,
media_type: str = "wav",
streaming_mode: bool = False,
parallel_infer: bool = True,
repetition_penalty: float = 1.35,
sample_steps: int = 32,
super_sampling: bool = False,
):
req = {
"text": text,
"text_lang": text_lang.lower(),
@@ -388,21 +416,21 @@ async def tts_get_endpoint(
"top_p": top_p,
"temperature": temperature,
"text_split_method": text_split_method,
"batch_size":int(batch_size),
"batch_threshold":float(batch_threshold),
"speed_factor":float(speed_factor),
"split_bucket":split_bucket,
"fragment_interval":fragment_interval,
"seed":seed,
"media_type":media_type,
"streaming_mode":streaming_mode,
"parallel_infer":parallel_infer,
"repetition_penalty":float(repetition_penalty),
"sample_steps":int(sample_steps),
"super_sampling":super_sampling
"batch_size": int(batch_size),
"batch_threshold": float(batch_threshold),
"speed_factor": float(speed_factor),
"split_bucket": split_bucket,
"fragment_interval": fragment_interval,
"seed": seed,
"media_type": media_type,
"streaming_mode": streaming_mode,
"parallel_infer": parallel_infer,
"repetition_penalty": float(repetition_penalty),
"sample_steps": int(sample_steps),
"super_sampling": super_sampling,
}
return await tts_handle(req)
@APP.post("/tts")
async def tts_post_endpoint(request: TTS_Request):
@@ -415,7 +443,7 @@ async def set_refer_aduio(refer_audio_path: str = None):
try:
tts_pipeline.set_ref_audio(refer_audio_path)
except Exception as e:
return JSONResponse(status_code=400, content={"message": f"set refer audio failed", "Exception": str(e)})
return JSONResponse(status_code=400, content={"message": "set refer audio failed", "Exception": str(e)})
return JSONResponse(status_code=200, content={"message": "success"})
@@ -425,18 +453,19 @@ async def set_refer_aduio(refer_audio_path: str = None):
# # 检查文件类型，确保是音频文件
# if not audio_file.content_type.startswith("audio/"):
# return JSONResponse(status_code=400, content={"message": "file type is not supported"})
# os.makedirs("uploaded_audio", exist_ok=True)
# save_path = os.path.join("uploaded_audio", audio_file.filename)
# # 保存音频文件到服务器上的一个目录
# with open(save_path , "wb") as buffer:
# buffer.write(await audio_file.read())
# tts_pipeline.set_ref_audio(save_path)
# except Exception as e:
# return JSONResponse(status_code=400, content={"message": f"set refer audio failed", "Exception": str(e)})
# return JSONResponse(status_code=200, content={"message": "success"})
@APP.get("/set_gpt_weights")
async def set_gpt_weights(weights_path: str = None):
try:
@@ -444,7 +473,7 @@ async def set_gpt_weights(weights_path: str = None):
return JSONResponse(status_code=400, content={"message": "gpt weight path is required"})
tts_pipeline.init_t2s_weights(weights_path)
except Exception as e:
return JSONResponse(status_code=400, content={"message": f"change gpt weight failed", "Exception": str(e)})
return JSONResponse(status_code=400, content={"message": "change gpt weight failed", "Exception": str(e)})
return JSONResponse(status_code=200, content={"message": "success"})
@@ -456,17 +485,16 @@ async def set_sovits_weights(weights_path: str = None):
return JSONResponse(status_code=400, content={"message": "sovits weight path is required"})
tts_pipeline.init_vits_weights(weights_path)
except Exception as e:
return JSONResponse(status_code=400, content={"message": f"change sovits weight failed", "Exception": str(e)})
return JSONResponse(status_code=400, content={"message": "change sovits weight failed", "Exception": str(e)})
return JSONResponse(status_code=200, content={"message": "success"})
if __name__ == "__main__":
try:
if host == 'None': # 在调用时使用 -a None 参数，可以让api监听双栈
if host == "None": # 在调用时使用 -a None 参数，可以让api监听双栈
host = None
uvicorn.run(app=APP, host=host, port=port, workers=1)
except Exception as e:
except Exception:
traceback.print_exc()
os.kill(os.getpid(), signal.SIGTERM)
exit(0)