最新同步

README.md

@@ -1,241 +1,16 @@
-[![SVG Banners](https://svg-banners.vercel.app/api?type=origin&text1=CosyVoice🤠&text2=Text-to-Speech%20💖%20Large%20Language%20Model&width=800&height=210)](https://github.com/Akshay090/svg-banners)
+## 👉🏻 有什么改动 👈🏻
 
-## 👉🏻 CosyVoice 👈🏻
-**CosyVoice 2.0**: [Demos](https://funaudiollm.github.io/cosyvoice2/); [Paper](https://arxiv.org/abs/2412.10117); [Modelscope](https://www.modelscope.cn/studios/iic/CosyVoice2-0.5B); [HuggingFace](https://huggingface.co/spaces/FunAudioLLM/CosyVoice2-0.5B)
+1.**升级到cuda12.8适配50系显卡** 
+docker目录，修改了Dockerfile和requirements适配50系显卡，升级基础镜像到cuda12.8.0，已在Ubuntu24下测试通过。
 
-**CosyVoice 1.0**: [Demos](https://fun-audio-llm.github.io); [Paper](https://funaudiollm.github.io/pdf/CosyVoice_v1.pdf); [Modelscope](https://www.modelscope.cn/studios/iic/CosyVoice-300M)
+2.**Dockerfile不从仓库下载代码**
+不再从git下载源码，而是在docker run时映射宿主机源码目录到/workspace/CosyVoice，方便修改和测试。
 
-## Highlight🔥
+3.**接口：** 
+修改了runtime下的fastapi接口，把流式和非流式接口分开了
+每个接口里面新增了HttpHeader，包含模型输出的采样率
 
-**CosyVoice 2.0** has been released! Compared to version 1.0, the new version offers more accurate, more stable, faster, and better speech generation capabilities.
-### Multilingual
-- **Supported Language**: Chinese, English, Japanese, Korean, Chinese dialects (Cantonese, Sichuanese, Shanghainese, Tianjinese, Wuhanese, etc.)
-- **Crosslingual & Mixlingual**：Support zero-shot voice cloning for cross-lingual and code-switching scenarios.
-### Ultra-Low Latency
-- **Bidirectional Streaming Support**: CosyVoice 2.0 integrates offline and streaming modeling technologies.
-- **Rapid First Packet Synthesis**: Achieves latency as low as 150ms while maintaining high-quality audio output.
-### High Accuracy
-- **Improved Pronunciation**: Reduces pronunciation errors by 30% to 50% compared to CosyVoice 1.0.
-- **Benchmark Achievements**: Attains the lowest character error rate on the hard test set of the Seed-TTS evaluation set.
-### Strong Stability
-- **Consistency in Timbre**: Ensures reliable voice consistency for zero-shot and cross-language speech synthesis.
-- **Cross-language Synthesis**: Marked improvements compared to version 1.0.
-### Natural Experience
-- **Enhanced Prosody and Sound Quality**: Improved alignment of synthesized audio, raising MOS evaluation scores from 5.4 to 5.53.
-- **Emotional and Dialectal Flexibility**: Now supports more granular emotional controls and accent adjustments.
+## commit记录
+本项目不定期与官方源码同步，为了保证稳定性，记录经过测试的模型和代码的commitid：
 
-## Roadmap
-
-- [x] 2024/12
-
-    - [x] 25hz cosyvoice 2.0 released
-
-- [x] 2024/09
-
-    - [x] 25hz cosyvoice base model
-    - [x] 25hz cosyvoice voice conversion model
-
-- [x] 2024/08
-
-    - [x] Repetition Aware Sampling(RAS) inference for llm stability
-    - [x] Streaming inference mode support, including kv cache and sdpa for rtf optimization
-
-- [x] 2024/07
-
-    - [x] Flow matching training support
-    - [x] WeTextProcessing support when ttsfrd is not available
-    - [x] Fastapi server and client
-
-
-## Install
-
-**Clone and install**
-
-- Clone the repo
-``` sh
-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
-- Create Conda env:
-
-``` sh
-conda create -n cosyvoice -y python=3.10
-conda activate cosyvoice
-# pynini is required by WeTextProcessing, use conda to install it as it can be executed on all platform.
-conda install -y -c conda-forge pynini==2.1.5
-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 recommend that you download our pretrained `CosyVoice2-0.5B` `CosyVoice-300M` `CosyVoice-300M-SFT` `CosyVoice-300M-Instruct` model and `CosyVoice-ttsfrd` resource.
-
-``` python
-# SDK模型下载
-from modelscope import snapshot_download
-snapshot_download('iic/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B')
-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/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-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/iic/CosyVoice2-0.5B.git pretrained_models/CosyVoice2-0.5B
-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/CosyVoice-ttsfrd.git pretrained_models/CosyVoice-ttsfrd
-```
-
-Optionally, you can unzip `ttsfrd` resouce and install `ttsfrd` package for better text normalization performance.
-
-Notice that this step is not necessary. If you do not install `ttsfrd` package, we will use WeTextProcessing by default.
-
-``` sh
-cd pretrained_models/CosyVoice-ttsfrd/
-unzip resource.zip -d .
-pip install ttsfrd_dependency-0.1-py3-none-any.whl
-pip install ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl
-```
-
-**Basic Usage**
-
-We strongly recommend using `CosyVoice2-0.5B` for better performance.
-Follow code below for detailed usage of each model.
-
-``` python
-import sys
-sys.path.append('third_party/Matcha-TTS')
-from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
-from cosyvoice.utils.file_utils import load_wav
-import torchaudio
-```
-
-**CosyVoice2 Usage**
-```python
-cosyvoice = CosyVoice2('pretrained_models/CosyVoice2-0.5B', load_jit=False, load_trt=False, fp16=False, use_flow_cache=False)
-
-# NOTE if you want to reproduce the results on https://funaudiollm.github.io/cosyvoice2, please add text_frontend=False during inference
-# zero_shot usage
-prompt_speech_16k = load_wav('./asset/zero_shot_prompt.wav', 16000)
-for i, j in enumerate(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, stream=False)):
-    torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
-
-# save zero_shot spk for future usage
-assert cosyvoice.add_zero_shot_spk('希望你以后能够做的比我还好呦。', prompt_speech_16k, 'my_zero_shot_spk') is True
-for i, j in enumerate(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。', '', '', zero_shot_spk_id='my_zero_shot_spk', stream=False)):
-    torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
-cosyvoice.save_spkinfo()
-
-# fine grained control, for supported control, check cosyvoice/tokenizer/tokenizer.py#L248
-for i, j in enumerate(cosyvoice.inference_cross_lingual('在他讲述那个荒诞故事的过程中，他突然[laughter]停下来，因为他自己也被逗笑了[laughter]。', prompt_speech_16k, stream=False)):
-    torchaudio.save('fine_grained_control_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
-
-# instruct usage
-for i, j in enumerate(cosyvoice.inference_instruct2('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。', '用四川话说这句话', prompt_speech_16k, stream=False)):
-    torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
-
-# bistream usage, you can use generator as input, this is useful when using text llm model as input
-# NOTE you should still have some basic sentence split logic because llm can not handle arbitrary sentence length
-def text_generator():
-    yield '收到好友从远方寄来的生日礼物，'
-    yield '那份意外的惊喜与深深的祝福'
-    yield '让我心中充满了甜蜜的快乐，'
-    yield '笑容如花儿般绽放。'
-for i, j in enumerate(cosyvoice.inference_zero_shot(text_generator(), '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=False)):
-    torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
-```
-
-**CosyVoice Usage**
-```python
-cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-SFT', load_jit=False, load_trt=False, fp16=False)
-# sft usage
-print(cosyvoice.list_available_spks())
-# change stream=True for chunk stream inference
-for i, j in enumerate(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', stream=False)):
-    torchaudio.save('sft_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
-
-cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M')
-# zero_shot usage, <|zh|><|en|><|jp|><|yue|><|ko|> for Chinese/English/Japanese/Cantonese/Korean
-prompt_speech_16k = load_wav('./asset/zero_shot_prompt.wav', 16000)
-for i, j in enumerate(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, stream=False)):
-    torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
-# cross_lingual usage
-prompt_speech_16k = load_wav('./asset/cross_lingual_prompt.wav', 16000)
-for i, j in enumerate(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, stream=False)):
-    torchaudio.save('cross_lingual_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
-# vc usage
-prompt_speech_16k = load_wav('./asset/zero_shot_prompt.wav', 16000)
-source_speech_16k = load_wav('./asset/cross_lingual_prompt.wav', 16000)
-for i, j in enumerate(cosyvoice.inference_vc(source_speech_16k, prompt_speech_16k, stream=False)):
-    torchaudio.save('vc_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
-
-cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-Instruct')
-# instruct usage, support <laughter></laughter><strong></strong>[laughter][breath]
-for i, j in enumerate(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.', stream=False)):
-    torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
-```
-
-**Start web demo**
-
-You can use our web demo page to get familiar with CosyVoice quickly.
-
-Please see the demo website for details.
-
-``` python
-# change iic/CosyVoice-300M-SFT for sft inference, or iic/CosyVoice-300M-Instruct for instruct inference
-python3 webui.py --port 50000 --model_dir pretrained_models/CosyVoice-300M
-```
-
-**Advanced Usage**
-
-For advanced user, we have provided train and inference scripts in `examples/libritts/cosyvoice/run.sh`.
-
-**Build for deployment**
-
-Optionally, if you want service deployment,
-you can run following steps.
-
-``` sh
-cd runtime/python
-docker build -t cosyvoice:v1.0 .
-# change iic/CosyVoice-300M to iic/CosyVoice-300M-Instruct if you want to use instruct inference
-# for grpc usage
-docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/grpc && python3 server.py --port 50000 --max_conc 4 --model_dir iic/CosyVoice-300M && sleep infinity"
-cd grpc && python3 client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>
-# for fastapi usage
-docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/fastapi && python3 server.py --port 50000 --model_dir iic/CosyVoice-300M && sleep infinity"
-cd fastapi && 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 official 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.
+**2025/04/24**  modelscope的CosyVocie2官方模型：60b054e54afdd0d950e658dede3d2ef73d9d65b6，github代码：3bf48f125a8c25d3f9c386cdb3abf2b614391817

deploy-hf.py

@@ -0,0 +1,49 @@
+import logging
+import subprocess
+logging.basicConfig(level=logging.INFO)
+
+def run_shell_script(script_path):
+    """
+    运行指定路径的shell脚本，并打印输出到控制台。
+
+    :param script_path: Shell脚本的文件路径
+    """
+    try:
+        # 使用subprocess.Popen来运行shell脚本
+        with subprocess.Popen(['bash', script_path], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True) as proc:
+            # 读取输出
+            for line in proc.stdout:
+                print(line, end='')  # 实时打印输出
+            proc.stdout.close()
+            return_code = proc.wait()
+            if return_code:
+                print(f"Shell脚本运行出错，返回码：{return_code}")
+    except Exception as e:
+        print(f"运行shell脚本时发生错误：{e}")
+
+# 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')
+ # SDK模型下载
+from modelscope import snapshot_download
+snapshot_download('iic/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M')
+
+class Args:
+    def __init__(self):
+        self.port = 5000
+        self.model_dir = 'pretrained_models/CosyVoice-300M'
+
+from webui import main
+from cosyvoice.cli.cosyvoice import CosyVoice
+import numpy as np
+
+# 创建 args 实例
+args = Args()
+
+cosyvoice = CosyVoice(args.model_dir)
+sft_spk = cosyvoice.list_avaliable_spks()
+prompt_sr, target_sr = 16000, 22050
+default_data = np.zeros(target_sr)
+
+# 调用 main 时传递 args
+main(args,sft_spk)

deploy-hf.sh

@@ -0,0 +1,34 @@
+#!/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
+
+#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 -r requirements_.txt
+
+# If you encounter sox compatibility issues
+# ubuntu
+apt-get -y update && apt-get -y install sox libsox-dev
+
+mkdir -p pretrained_models
+#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
+
+export PYTHONPATH=third_party/Matcha-TTS
+
+python3 webui.py

docker/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04
+FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04
 
 ARG VENV_NAME="cosyvoice"
 ENV VENV=$VENV_NAME
@@ -42,10 +42,11 @@ WORKDIR /workspace
 
 ENV PYTHONPATH="${PYTHONPATH}:/workspace/CosyVoice:/workspace/CosyVoice/third_party/Matcha-TTS"
 
-RUN git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git
-
+#RUN git clone --recursive https://github.com/tanbw/CosyVoice.git
+COPY requirements.txt CosyVoice/requirements.txt
 RUN conda activate ${VENV} && conda install -y -c conda-forge pynini==2.1.5
 RUN conda activate ${VENV} && cd CosyVoice && \
+    pip install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128 && \
     pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
-
+RUN rm -rf CosyVoice
 WORKDIR /workspace/CosyVoice

docker/requirements.txt

@@ -0,0 +1,35 @@
+--extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/ # microsoft/onnxruntime#21684
+conformer==0.3.2
+deepspeed==0.14.2; sys_platform == 'linux'
+diffusers==0.29.0
+gdown==5.1.0
+gradio==5.4.0
+grpcio==1.57.0
+grpcio-tools==1.57.0
+hydra-core==1.3.2
+HyperPyYAML==1.2.2
+inflect==7.3.1
+librosa==0.10.2
+lightning==2.2.4
+matplotlib==3.7.5
+modelscope==1.20.0
+networkx==3.1
+omegaconf==2.3.0
+onnx==1.16.0
+onnxruntime-gpu==1.21.0; sys_platform == 'linux'
+onnxruntime==1.21.0; sys_platform == 'darwin' or sys_platform == 'win32'
+openai-whisper==20231117
+protobuf==4.25
+pyarrow==18.1.0
+pydantic==2.7.0
+pyworld==0.3.4
+rich==13.7.1
+soundfile==0.12.1
+tensorboard==2.14.0
+tensorrt==10.9.0.34; sys_platform == 'linux'
+transformers==4.40.1
+uvicorn==0.30.0
+wget==3.2
+fastapi==0.115.6
+fastapi-cli==0.0.4
+WeTextProcessing==1.0.3