update

README.md

@@ -1,4 +1,8 @@
 # CosyVoice
+## 👉🏻 [CosyVoice Demos](https://fun-audio-llm.github.io/) 👈🏻
+[[CosyVoice Paper](https://fun-audio-llm.github.io/pdf/CosyVoice_v1.pdf)][[CosyVoice Studio](https://www.modelscope.cn/studios/iic/CosyVoice-300M)][[CosyVoice Code](https://github.com/FunAudioLLM/CosyVoice)]
+
+For `SenseVoice`, visit [SenseVoice repo](https://github.com/FunAudioLLM/SenseVoice) and [SenseVoice space](https://www.modelscope.cn/studios/iic/SenseVoice).
 
 ## Install
 
@@ -6,7 +10,10 @@
 
 - Clone the repo
 ``` sh
-git clone https://github.com/modelscope/cosyvoice.git
+git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git
+# If you failed to clone submodule due to network failures, please run following command until success
+cd CosyVoice
+git submodule update --init --recursive
 ```
 
 - Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
@@ -16,72 +23,99 @@ git clone https://github.com/modelscope/cosyvoice.git
 conda create -n cosyvoice python=3.8
 conda activate cosyvoice
 pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
+
+# If you encounter sox compatibility issues
+# ubuntu
+sudo apt-get install sox libsox-dev
+# centos
+sudo yum install sox sox-devel
 ```
 
 **Model download**
 
-We strongly recommand that you download our pretrained multi_lingual and mutli_emotion model.
+We strongly recommand that you download our pretrained `CosyVoice-300M` `CosyVoice-300M-SFT` `CosyVoice-300M-Instruct` model and `speech_kantts_ttsfrd` resource.
 
 If you are expert in this field, and you are only interested in training your own CosyVoice model from scratch, you can skip this step.
 
+``` python
+# SDK模型下载
+from modelscope import snapshot_download
+snapshot_download('iic/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M')
+snapshot_download('iic/CosyVoice-300M-SFT', local_dir='pretrained_models/CosyVoice-300M-SFT')
+snapshot_download('iic/CosyVoice-300M-Instruct', local_dir='pretrained_models/CosyVoice-300M-Instruct')
+snapshot_download('iic/speech_kantts_ttsfrd', local_dir='pretrained_models/speech_kantts_ttsfrd')
+```
+
 ``` sh
+# git模型下载，请确保已安装git lfs
 mkdir -p pretrained_models
-git clone https://www.modelscope.cn/CosyVoice/multi_lingual_cosytts.git pretrained_models/multi_lingual_cosytts
-git clone https://www.modelscope.cn/CosyVoice/multi_emotion_cosytts.git pretrained_models/multi_emotion_cosytts
+git clone https://www.modelscope.cn/iic/CosyVoice-300M.git pretrained_models/CosyVoice-300M
+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/speech_kantts_ttsfrd.git pretrained_models/speech_kantts_ttsfrd
+```
+
+Unzip `ttsfrd` resouce and install `ttsfrd` package
+``` sh
+cd pretrained_models/speech_kantts_ttsfrd/
+unzip resource.zip -d .
+pip install ttsfrd-0.3.6-cp38-cp38-linux_x86_64.whl
 ```
 
 **Basic Usage**
 
-For zero_shot and sft inference, please use models in `pretrained_models/multi_lingual_cosytts`
+For zero_shot/cross_lingual inference, please use `CosyVoice-300M` model.
+For sft inference, please use `CosyVoice-300M-SFT` model.
+For instruct inference, please use `CosyVoice-300M-Instruct` model.
+First, add `third_party/AcademiCodec` and `third_party/Matcha-TTS` to your `PYTHONPATH`.
 
+``` sh
+export PYTHONPATH=third_party/AcademiCodec:third_party/Matcha-TTS
 ```
+
+``` python
 from cosyvoice.cli.cosyvoice import CosyVoice
 from cosyvoice.utils.file_utils import load_wav
 import torchaudio
 
-cosyvoice = CosyVoice('pretrained_models/multi_lingual_cosytts')
-
+cosyvoice = CosyVoice('speech_tts/CosyVoice-300M-SFT')
 # sft usage
 print(cosyvoice.list_avaliable_spks())
-output = cosyvoice.inference_sft('hello, my name is Jack. What is your name?', 'aishuo')
+output = cosyvoice.inference_sft('你好，我是通义生成式语音大模型，请问有什么可以帮您的吗？', '中文女')
 torchaudio.save('sft.wav', output['tts_speech'], 22050)
 
+cosyvoice = CosyVoice('speech_tts/CosyVoice-300M')
 # zero_shot usage
-prompt_speech_22050 = load_wav('1089_134686_000002_000000.wav', 22050)
-output = cosyvoice.inference_zero_shot('hello, my name is Jack. What is your name?', 'It would be a gloomy secret night.', prompt_speech_22050)
+prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000)
+output = cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', prompt_speech_16k)
 torchaudio.save('zero_shot.wav', output['tts_speech'], 22050)
-```
-
-For instruct inference, please use models in `pretrained_models/multi_emotion_cosytts`
-
-```
-from cosyvoice.cli.cosyvoice import CosyVoice
-from cosyvoice.utils.file_utils import load_wav
-import torchaudio
-
-cosyvoice = CosyVoice('pretrained_models/multi_emotion_cosytts')
+# cross_lingual usage
+prompt_speech_16k = load_wav('cross_lingual_prompt.wav', 16000)
+output = cosyvoice.inference_cross_lingual('<|en|>And then later on, fully acquiring that company. So keeping management in line, interest in line with the asset that\'s coming into the family is a reason why sometimes we don\'t buy the whole thing.', prompt_speech_16k)
+torchaudio.save('cross_lingual.wav', output['tts_speech'], 22050)
 
+cosyvoice = CosyVoice('speech_tts/CosyVoice-300M-Instruct')
 # instruct usage
-prompt_speech_22050 = load_wav('1089_134686_000002_000000.wav', 22050)
-output = cosyvoice.inference_instruct('hello, my name is Jack. What is your name?', 'It would be a gloomy secret night.', prompt_speech_22050, 'A serene woman articulates thoughtfully in a high pitch and slow tempo, exuding a peaceful and joyful aura.')
+output = cosyvoice.inference_instruct('在面对挑战时，他展现了非凡的<strong>勇气</strong>与<strong>智慧</strong>。', '中文男', 'Theo \'Crimson\', is a fiery, passionate rebel leader. Fights with fervor for justice, but struggles with impulsiveness.')
 torchaudio.save('instruct.wav', output['tts_speech'], 22050)
 ```
 
-**Advanced Usage**
-
-For advanced user, we have provided train and inference scripts in `examples/libritts/cosyvoice/run.sh`.
-You can get familiar with CosyVoice following this recipie.
-
 **Start web demo**
 
 You can use our web demo page to get familiar with CosyVoice quickly.
-We only support zero_shot/sft inference in web demo.
+We support sft/zero_shot/cross_lingual/instruct inference in web demo.
 
 Please see the demo website for details.
 
+``` python
+# change speech_tts/CosyVoice-300M-SFT for sft inference, or speech_tts/CosyVoice-300M-Instruct for instruct inference
+python3 webui.py --port 50000 --model_dir speech_tts/CosyVoice-300M
 ```
-python3 webui.py --port 50000 --model_dir pretrained_models/multi_lingual_cosytts
-```
+
+**Advanced Usage**
+
+For advanced user, we have provided train and inference scripts in `examples/libritts/cosyvoice/run.sh`.
+You can get familiar with CosyVoice following this recipie.
 
 **Build for deployment**
 
@@ -91,7 +125,26 @@ you can run following steps. Otherwise, you can just ignore this step.
 ``` sh
 cd runtime/python
 docker build -t cosyvoice:v1.0 .
-# change multi_lingual_cosytts to multi_emotion_cosytts if you want to use instruct inference
-docker run -d --runtime=nvidia -v `pwd`/../../pretrained_models/multi_lingual_cosytts:/opt/cosyvoice/cosyvoice/runtime/pretrained_models -p 50000:50000 cosyvoice:v1.0
-python3 client.py --port 50000 --mode <sft|zero_shot|instruct>
-```
+# change speech_tts/CosyVoice-300M to speech_tts/CosyVoice-300M-Instruct if you want to use instruct inference
+docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python && python3 server.py --port 50000 --max_conc 4 --model_dir speech_tts/CosyVoice-300M && sleep infinity"
+python3 client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>
+```
+
+## Discussion & Communication
+
+You can directly discuss on [Github Issues](https://github.com/FunAudioLLM/CosyVoice/issues).
+
+You can also scan the QR code to join our officla Dingding chat group.
+
+<img src="./asset/dingding.png" width="250px">
+
+## Acknowledge
+
+1. We borrowed a lot of code from [FunASR](https://github.com/modelscope/FunASR).
+2. We borrowed a lot of code from [FunCodec](https://github.com/modelscope/FunCodec).
+3. We borrowed a lot of code from [Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS).
+4. We borrowed a lot of code from [AcademiCodec](https://github.com/yangdongchao/AcademiCodec).
+5. We borrowed a lot of code from [WeNet](https://github.com/wenet-e2e/wenet).
+
+## Disclaimer
+The content provided above is for academic purposes only and is intended to demonstrate technical capabilities. Some examples are sourced from the internet. If any content infringes on your rights, please contact us to request its removal.