no message

deploy.py

@@ -24,7 +24,7 @@ def run_shell_script(script_path):
 # 使用方法示例
 # 假设有一个名为example.sh的脚本文件在当前目录下
 run_shell_script('deploy.sh')
-   
+
 class Args:
     def __init__(self):
         self.port = 5000
@@ -43,4 +43,4 @@ prompt_sr, target_sr = 16000, 22050
 default_data = np.zeros(target_sr)
 
 # 调用 main 时传递 args
-main(args)
+main(args,sft_spk)

deploy.sh

@@ -1,23 +1,33 @@
 #!/bin/bash
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+sh Miniconda3-latest-Linux-x86_64.sh -b
+source ~/miniconda3/bin/activate
 
-cat /usr/local/cuda/version.txt
+conda create -n cosyvoice python=3.8
+conda activate cosyvoice
+conda install -y -c conda-forge pynini==2.1.5
 # pynini is required by WeTextProcessing, use conda to install it as it can be executed on all platform.
-pip install pynini==2.1.5
-pip install -r requirements.txt
+pip install -r requirements_.txt
 
 # If you encounter sox compatibility issues
 # ubuntu
+apt-get -y update
 apt-get install sox libsox-dev
 
 mkdir -p pretrained_models
-huggingface-cli download model-scope/CosyVoice-300M --local-dir pretrained_models/CosyVoice-300M --token=$hf_token
-huggingface-cli download model-scope/CosyVoice-300M-SFT --local-dir pretrained_models/CosyVoice-300M-SFT --token=$hf_token
+git clone https://www.modelscope.cn/iic/CosyVoice-300M.git pretrained_models/CosyVoice-300M
+#git clone https://www.modelscope.cn/iic/CosyVoice-300M-25Hz.git pretrained_models/CosyVoice-300M-25Hz
+#git clone https://www.modelscope.cn/iic/CosyVoice-300M-SFT.git pretrained_models/CosyVoice-300M-SFT
+#git clone https://www.modelscope.cn/iic/CosyVoice-300M-Instruct.git pretrained_models/CosyVoice-300M-Instruct
+git clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git pretrained_models/CosyVoice-ttsfrd
+#huggingface-cli download model-scope/CosyVoice-300M --local-dir pretrained_models/CosyVoice-300M --token=$hf_token
+#huggingface-cli download model-scope/CosyVoice-300M-SFT --local-dir pretrained_models/CosyVoice-300M-SFT --token=$hf_token
 #huggingface-cli download FunAudioLLM/CosyVoice-ttsfrd --local-dir pretrained_models/CosyVoice-ttsfrd --token=$hf_token
 
 ls pretrained_models
 
-#cd pretrained_models/CosyVoice-ttsfrd/
-#unzip resource.zip -d .
-#pip install ttsfrd-0.3.6-cp38-cp38-linux_x86_64.whl
+cd pretrained_models/CosyVoice-ttsfrd/
+unzip resource.zip -d .
+pip install ttsfrd-0.3.6-cp38-cp38-linux_x86_64.whl
 
 export PYTHONPATH=third_party/Matcha-TTS

webui.py

@@ -132,7 +132,7 @@ def generate_audio(tts_text, mode_checkbox_group, sft_dropdown, prompt_text, pro
             yield (target_sr, i['tts_speech'].numpy().flatten())
 
 
-def main(args):
+def main(args,sft_spk):
     with gr.Blocks() as demo:
         gr.Markdown("### 代码库 [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) \
                     预训练模型 [CosyVoice-300M](https://www.modelscope.cn/models/iic/CosyVoice-300M) \