修改Dockerfile，使其直接利用最新的requirements.txt安装Python包；并在构建过程中预先下载moda ASR和nltk相关的模型到镜像中以便加快初次运行的速度

Dockerfile

@@ -2,7 +2,7 @@
 FROM cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04
 
 LABEL maintainer="breakstring@hotmail.com"
-LABEL version="dev-20240123.03"
+LABEL version="dev-20240127.f9387e0"
 LABEL description="Docker image for GPT-SoVITS"
 
 
@@ -18,27 +18,19 @@ RUN apt-get update && \
 WORKDIR /workspace
 COPY . /workspace
 
+# install python packages
+RUN pip install -r requirements.txt
+
 # Download models
 RUN chmod +x /workspace/Docker/download.sh && /workspace/Docker/download.sh
 
-# 本应该从 requirements.txt 里面安装package，但是由于funasr和modelscope的问题，暂时先在后面手工安装依赖包吧
-RUN pip install --no-cache-dir torch numpy scipy tensorboard librosa==0.9.2 numba==0.56.4 pytorch-lightning gradio==3.14.0 ffmpeg-python onnxruntime tqdm cn2an pypinyin pyopenjtalk g2p_en chardet transformers jieba psutil PyYAML
-# 这里强制指定了modelscope和funasr的版本，后面damo_asr的模型让它们自己下载
-RUN pip install --no-cache-dir modelscope~=1.10.0 torchaudio sentencepiece funasr~=0.8.7
+# Download moda ASR related
+RUN python /workspace/Docker/download.py
 
-# 先屏蔽掉，让容器里自己下载
-# Clone damo_asr
-#WORKDIR /workspace/tools/damo_asr/models
-#RUN git clone --depth 1 https://www.modelscope.cn/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch && \
-#    (cd speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch && git lfs pull)
-#RUN git clone --depth 1 https://www.modelscope.cn/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch.git speech_fsmn_vad_zh-cn-16k-common-pytorch && \
-#    (cd speech_fsmn_vad_zh-cn-16k-common-pytorch && git lfs pull)
-#RUN git clone --depth 1 https://www.modelscope.cn/iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch.git punc_ct-transformer_zh-cn-common-vocab272727-pytorch && \
-#    (cd punc_ct-transformer_zh-cn-common-vocab272727-pytorch && git lfs pull)
+# Download nltk realted
+RUN python -m nltk.downloader averaged_perceptron_tagger
+RUN python -m nltk.downloader cmudict
 
-#RUN parallel --will-cite -a /workspace/Docker/damo.sha256 "echo -n {} | sha256sum -c"
-
-#WORKDIR /workspace
 
 EXPOSE 9870
 EXPOSE 9871