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Dockerfile_nv 0 → 100644
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FROM pytorch/pytorch:2.3.0-cuda12.1-cudnn8-runtime
WORKDIR /app
RUN apt-get update && apt-get install -y \
curl \
python3 \
python3-pip \
ffmpeg \
&& apt-get clean
COPY requirements.txt /app/
RUN pip install --no-cache-dir -r requirements.txt
COPY . /app/
CMD ["python", "-m", "tts.gradio_api"]
LICENSE 0 → 100644
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README.md 0 → 100644
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# MegaTTS 3
骨干网络仅含0.45B参数，支持口音强度控制，适于实时语音交互，能满足不同场景下对语音口音克隆的多样化需求。
## 论文
`MegaTTS 3: Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesis`
- https://arxiv.org/pdf/2502.18924
## 模型结构
MegaTTS3 采用WaveVAE+Latent Diffusion Transformer双模块协同合作，创造出超高质量的语音合成效果。
<div align=center>
<img src="./doc/MegaTTS3.png"/>
</div>
## 算法原理
1、WaveVAE：负责将原始语音信号压缩成潜在向量，并通过解码器进行还原，确保语音合成的高保真度。
2、DiT：通过自注意力机制对潜在向量序列进行建模，将文本和语音信号之间进行细致的对齐。
3、CFG：调整文本引导参数（αtxt）和说话人引导参数（αspk），用户能够控制语音的发音特征、口音强度等，进而定制更加个性化的语音输出。
4、PeRFlow：将生成过程分割成多个时间段，在每个时间段内进行快速计算，从而显著降低了采样步骤的数量。
## 环境配置
```
mv MegaTTS3_pytorch MegaTTS3 # 去框架名后缀
```
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10-fixpy
# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：e77c15729879
docker run -it --shm-size=64G -v $PWD/MegaTTS3:/home/MegaTTS3 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name tts <your IMAGE ID> bash
cd /home/MegaTTS3
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
sh ffmpeg_apt.sh # ffmpeg=4.4.4，若无法直接运行完成安装，可拷贝出来单独依次运行脚本中的命令，以避免网络不畅的问题。
```
### Dockerfile（方法二）
```
cd /home/MegaTTS3/docker
docker build --no-cache -t tts:latest .
docker run --shm-size=64G --name tts -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../MegaTTS3:/home/MegaTTS3 -it tts bash
# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
sh ffmpeg_apt.sh # ffmpeg=4.4.4，若无法直接运行完成安装，可拷贝出来单独依次运行脚本中的命令，以避免网络不畅的问题。
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
- https://developer.hpccube.com/tool/
```
DTK驱动:dtk2504
python:python3.10
torch:2.4.1
torchvision:0.19.1
triton:3.0.0
vllm:0.6.2
flash-attn:2.6.1
deepspeed:0.14.2
apex:1.4.0
transformers:4.49.0
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
2、其它非特殊库参照requirements.txt安装
```
cd /home/MegaTTS3
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
sh ffmpeg_apt.sh # ffmpeg=4.4.4，若无法直接运行完成安装，可拷贝出来单独依次运行脚本中的命令，以避免网络不畅的问题。
```
## 数据集
`无`
## 训练
无
## 推理
预训练权重目录结构：
```
/home/MegaTTS3/checkpoints/
|── wavvae
|── diffusion_transformer
|── aligner_lm
|── duration_lm
...
└── g2p
mv MegaTTS3/* /home/MegaTTS3/checkpoints/ # MegaTTS3下的内容放到checkpoints目录下
```
### 单机单卡
```
cd /home/MegaTTS3
python tts/infer_cli.py --input_wav 'assets/Chinese_prompt.wav' --input_text "另一边的桌上,一位读书人嗤之以鼻道,'佛子三藏,神子燕小鱼是什么样的人物,李家的那个李子夜如何与他们相提并论？'" --output_dir ./gen
# 目前已支持fp32推理，后期将陆续支持fp16推理。
```
推理精度变量precision位于[infer_cli](./tts/infer_cli.py)的MegaTTS3DiTInfer类下。
更多资料可参考源项目中的[`readme_origin`](./readme_origin.md)。
## result
`输入: `
```
--input_wav assets/Chinese_prompt.wav
--input_text "另一边的桌上,一位读书人嗤之以鼻道,'佛子三藏,神子燕小鱼是什么样的人物,李家的那个李子夜如何与他们相提并论？'"
```
`输出:`
```
--output_dir gen/'[P]另一边的桌上,一位读书人嗤之以鼻道,'\''佛.wav'
```
官方效果示例：
<div align=center>
<img src="./doc/result.png"/>
</div>
### 精度
DCU与GPU精度一致，推理框架：pytorch。
## 应用场景
### 算法类别
`语音合成`
### 热点应用行业
`广媒,影视,动漫,医疗,家居,教育`
## 预训练权重
HF/github下载地址为：[ByteDance/MegaTTS3](https://huggingface.co/ByteDance/MegaTTS3)
## 源码仓库及问题反馈
- http://developer.sourcefind.cn/codes/modelzoo/MegaTTS3_pytorch.git
## 参考资料
- https://github.com/bytedance/MegaTTS3.git
checkpoints/README.md 0 → 100644
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---
language:
- en
- zh
license: apache-2.0
pipeline_tag: text-to-speech
---
# Model Description
This is a huggingface model card for MegaTTS 3 👋
- Paper: [MegaTTS 3: Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesis](https://huggingface.co/papers/2502.18924)
- Project Page (Audio Samples): <https://sditdemo.github.io/sditdemo/>
- github: <https://github.com/bytedance/MegaTTS3>
- [Demo Video](https://github.com/user-attachments/assets/0174c111-f392-4376-a34b-0b5b8164aacc)
- Huggingface Space: https://huggingface.co/spaces/ByteDance/MegaTTS3
## Installation
```sh
# Clone the repository
git clone https://github.com/bytedance/MegaTTS3
cd MegaTTS3
```
**Model Download**
```sh
huggingface-cli download ByteDance/MegaTTS3 --local-dir ./checkpoints --local-dir-use-symlinks False
```
**Requirements (for Linux)**
```sh
# Create a python 3.10 conda env (you could also use virtualenv)
conda create -n megatts3-env python=3.10
conda activate megatts3-env
pip install -r requirements.txt
# Set the root directory
export PYTHONPATH="/path/to/MegaTTS3:$PYTHONPATH"
# [Optional] Set GPU
export CUDA_VISIBLE_DEVICES=0
# If you encounter bugs with pydantic in inference, you should check if the versions of pydantic and gradio are matched.
# [Note] if you encounter bugs related with httpx, please check that whether your environmental variable "no_proxy" has patterns like "::"
```
**Requirements (for Windows)**
```sh
# [The Windows version is currently under testing]
# Comment below dependence in requirements.txt:
# # WeTextProcessing==1.0.4.1
# Create a python 3.10 conda env (you could also use virtualenv)
conda create -n megatts3-env python=3.10
conda activate megatts3-env
pip install -r requirements.txt
conda install -y -c conda-forge pynini==2.1.5
pip install WeTextProcessing==1.0.3
# [Optional] If you want GPU inference, you may need to install specific version of PyTorch for your GPU from https://pytorch.org/.
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
# [Note] if you encounter bugs related with `ffprobe` or `ffmpeg`, you can install it through `conda install -c conda-forge ffmpeg`
# Set environment variable for root directory
set PYTHONPATH="C:\path\to\MegaTTS3;%PYTHONPATH%" # Windows
$env:PYTHONPATH="C:\path\to\MegaTTS3;%PYTHONPATH%" # Powershell on Windows
conda env config vars set PYTHONPATH="C:\path\to\MegaTTS3;%PYTHONPATH%" # For conda users
# [Optional] Set GPU
set CUDA_VISIBLE_DEVICES=0 # Windows
$env:CUDA_VISIBLE_DEVICES=0 # Powershell on Windows
```
**Requirements (for Docker)**
```sh
# [The Docker version is currently under testing]
# ! You should download the pretrained checkpoint before running the following command
docker build . -t megatts3:latest
# For GPU inference
docker run -it -p 7929:7929 --gpus all -e CUDA_VISIBLE_DEVICES=0 megatts3:latest
# For CPU inference
docker run -it -p 7929:7929 megatts3:latest
# Visit http://0.0.0.0:7929/ for gradio.
```
> \[!TIP]
> \[IMPORTANT]
> For security issues, we do not upload the parameters of WaveVAE encoder to the above links. You can only use the pre-extracted latents from [link1](https://drive.google.com/drive/folders/1QhcHWcy20JfqWjgqZX1YM3I6i9u4oNlr?usp=sharing) for inference. If you want to synthesize speech for speaker A, you need "A.wav" and "A.npy" in the same directory. If you have any questions or suggestions for our model, please email us.
>
> This project is primarily intended for academic purposes. For academic datasets requiring evaluation, you may upload them to the voice request queue in [link2](https://drive.google.com/drive/folders/1gCWL1y_2xu9nIFhUX_OW5MbcFuB7J5Cl?usp=sharing) (within 24s for each clip). After verifying that your uploaded voices are free from safety issues, we will upload their latent files to [link1](https://drive.google.com/drive/folders/1QhcHWcy20JfqWjgqZX1YM3I6i9u4oNlr?usp=sharing) as soon as possible.
>
> In the coming days, we will also prepare and release the latent representations for some common TTS benchmarks.
## Inference
**Command-Line Usage (Standard)**
```bash
# p_w (intelligibility weight), t_w (similarity weight). Typically, prompt with more noises requires higher p_w and t_w
python tts/infer_cli.py --input_wav 'assets/Chinese_prompt.wav' --input_text "另一边的桌上,一位读书人嗤之以鼻道,'佛子三藏,神子燕小鱼是什么样的人物,李家的那个李子夜如何与他们相提并论？'" --output_dir ./gen
# As long as audio volume and pronunciation are appropriate, increasing --t_w within reasonable ranges (2.0~5.0)
# will increase the generated speech's expressiveness and similarity (especially for some emotional cases).
python tts/infer_cli.py --input_wav 'assets/English_prompt.wav' --input_text 'As his long promised tariff threat turned into reality this week, top human advisers began fielding a wave of calls from business leaders, particularly in the automotive sector, along with lawmakers who were sounding the alarm.' --output_dir ./gen --p_w 2.0 --t_w 3.0
```
**Command-Line Usage (for TTS with Accents)**
```bash
# When p_w (intelligibility weight) ≈ 1.0, the generated audio closely retains the speaker’s original accent. As p_w increases, it shifts toward standard pronunciation.
# t_w (similarity weight) is typically set 0–3 points higher than p_w for optimal results.
# Useful for accented TTS or solving the accent problems in cross-lingual TTS.
python tts/infer_cli.py --input_wav 'assets/English_prompt.wav' --input_text '这是一条有口音的音频。' --output_dir ./gen --p_w 1.0 --t_w 3.0
python tts/infer_cli.py --input_wav 'assets/English_prompt.wav' --input_text '这条音频的发音标准一些了吗？' --output_dir ./gen --p_w 2.5 --t_w 2.5
```
**Web UI Usage**
```bash
# We also support cpu inference, but it may take about 30 seconds (for 10 inference steps).
python tts/gradio_api.py
```
## Security
If you discover a potential security issue in this project, or think you may
have discovered a security issue, we ask that you notify Bytedance Security via our [security center](https://security.bytedance.com/src) or [sec@bytedance.com](sec@bytedance.com).
Please do **not** create a public issue.
## License
This project is licensed under the [Apache-2.0 License](LICENSE).
## BibTeX Entry and Citation Info
This repo contains forced-align version of `Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesis` and the WavVAE is mainly based on `Wavtokenizer: an efficient acoustic discrete codec tokenizer for audio language modeling`. Compared to the model described in paper, the repository includes additional models. These models not only enhance the stability and cloning capabilities of the algorithm but can also be independently utilized to serve a wider range of scenarios.
```
@article{jiang2025sparse,
title={Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesis},
author={Jiang, Ziyue and Ren, Yi and Li, Ruiqi and Ji, Shengpeng and Ye, Zhenhui and Zhang, Chen and Jionghao, Bai and Yang, Xiaoda and Zuo, Jialong and Zhang, Yu and others},
journal={arXiv preprint arXiv:2502.18924},
year={2025}
}
@article{ji2024wavtokenizer,
title={Wavtokenizer: an efficient acoustic discrete codec tokenizer for audio language modeling},
author={Ji, Shengpeng and Jiang, Ziyue and Wang, Wen and Chen, Yifu and Fang, Minghui and Zuo, Jialong and Yang, Qian and Cheng, Xize and Wang, Zehan and Li, Ruiqi and others},
journal={arXiv preprint arXiv:2408.16532},
year={2024}
}
```
\ No newline at end of file
docker/Dockerfile 0 → 100644
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FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10-fixpy
ENV DEBIAN_FRONTEND=noninteractive
# RUN yum update && yum install -y git cmake wget build-essential
# RUN source /opt/dtk-dtk25.04/env.sh
# # 安装pip相关依赖
COPY requirements.txt requirements.txt
RUN pip3 install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
docker/requirements.txt 0 → 100644
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# torch==2.3.0
# torchaudio==2.3.0
numpy<2
setproctitle==1.3.3
attrdict==2.0.1
librosa==0.10.2.post1
langdetect==1.0.9
pydub==0.25.1
pyloudnorm==0.1.1
modelscope==1.22.2
WeTextProcessing==1.0.4.1
transformers>=4.41.2,<=4.49.0,!=4.46.*,!=4.47.*,!=4.48.*;python_version<'3.10'
transformers>=4.41.2,<=4.49.0,!=4.46.*,!=4.47.*,!=4.48.0;python_version>='3.10'
x-transformers==1.44.4
torchdiffeq==0.2.5
openai-whisper==20240930
httpx==0.28.1
gradio==5.23.1
ffmpeg_apt.sh 0 → 100644
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apt-get update
apt-get upgrade -y
apt-get install libflac8=1.3.3-2build2
apt-get install yasm nasm -y
apt-get install -y libmad0 libmad0-dev libid3tag0 libid3tag0-dev libmp3lame-dev libflac-dev libvorbis-dev
# wget --no-check-certificate https://www.ffmpeg.org/releases/ffmpeg-4.4.4.tar.gz
tar xzf ffmpeg-4.4.4.tar.gz
cd ffmpeg-4.4.4
./configure --enable-shared --enable-libmp3lame
make -j32
make install
cd -
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
# rm -rf ffmpeg-4.4.4 ffmpeg-4.4.4.tar.gz
icon.png 0 → 100644
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icon.png

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