ASLP-lab/Cosyvoice2-Yue

👉🏻 WenetSpeech-Yue 👈🏻

WenetSpeech-Yue: Demos; Paper; Github; HuggingFace

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WenetSpeech-Yue TTS Models have been released!
This repository contains two versions of the TTS models:

ASLP-lab/Cosyvoice2-Yue: The base model for Cantonese TTS.
ASLP-lab/Cosyvoice2-Yue-ZoengJyutGaai: A fine-tuned, higher-quality version for more natural speech generation.

Roadmap

2025/9
- 25hz WenetSpeech-Yue TTS models released

Install

Clone and install

Clone the repo

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:

conda create -n cosyvoice 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

Basic Usage

We strongly recommend using CosyVoice2-0.5B for better performance. Follow code below for detailed usage of each model.

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

cosyvoice = CosyVoice2('ASLP-lab/Cosyvoice2-Yue', load_jit=False, load_trt=False, fp16=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('zero_shot_prompt.wav', 16000)

# instruct usage
for i, j in enumerate(cosyvoice.inference_instruct2('收到朋友从远方寄嚟嘅生日礼物，呢份意外嘅惊喜同埋满满嘅祝福令我内心充满咗甜蜜嘅快乐，个笑容就好似花咁咧盛开住。', '用粤语说这句话', prompt_speech_16k, stream=False)):
    torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)

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