v1.0

aa0c8efc · chenzk · aa0c8efc · aa0c8efc · aa0c8efc · aa0c8efc
Commit aa0c8efc authored Nov 06, 2024 by chenzk
20 changed files
--- a/.gitignore
+++ b/.gitignore
+# Customed
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+runs/
+data/
+ckpts/
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+# Byte-compiled / optimized / DLL files
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+# pyenv
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--- a/.gitmodules
+++ b/.gitmodules
+[submodule "src/third_party/BigVGAN"]
+	path = src/third_party/BigVGAN
+	url = https://github.com/NVIDIA/BigVGAN.git
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.7.0
+    hooks:
+      # Run the linter.
+      - id: ruff
+        args: [--fix]
+      # Run the formatter.
+      - id: ruff-format
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+      - id: check-yaml
--- a/Dockerfile
+++ b/Dockerfile
+FROM pytorch/pytorch:2.4.0-cuda12.4-cudnn9-devel
+USER root
+ARG DEBIAN_FRONTEND=noninteractive
+LABEL github_repo="https://github.com/SWivid/F5-TTS"
+RUN set -x \
+    && apt-get update \
+    && apt-get -y install wget curl man git less openssl libssl-dev unzip unar build-essential aria2 tmux vim \
+    && apt-get install -y openssh-server sox libsox-fmt-all libsox-fmt-mp3 libsndfile1-dev ffmpeg \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+WORKDIR /workspace
+RUN git clone https://github.com/SWivid/F5-TTS.git \
+    && cd F5-TTS \
+    && pip install -e .[eval]
+ENV SHELL=/bin/bash
+WORKDIR /workspace/F5-TTS
--- a/LICENSE
+++ b/LICENSE
+MIT License
+Copyright (c) 2024 Yushen CHEN
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
--- a/Qwen/Qwen2.5-3B-Instruct/README.md
+++ b/Qwen/Qwen2.5-3B-Instruct/README.md
+---
+license: other
+license_name: qwen-research
+license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-3B
+tags:
+- chat
+library_name: transformers
+---
+# Qwen2.5-3B-Instruct
+## Introduction
+Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
+- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
+- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
+- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
+- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. 
+**This repo contains the instruction-tuned 3B Qwen2.5 model**, which has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining & Post-training
+- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
+- Number of Parameters: 3.09B
+- Number of Paramaters (Non-Embedding): 2.77B
+- Number of Layers: 36
+- Number of Attention Heads (GQA): 16 for Q and 2 for KV
+- Context Length: Full 32,768 tokens and generation 8192 tokens
+For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
+## Requirements
+The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
+With `transformers<4.37.0`, you will encounter the following error:
+```
+KeyError: 'qwen2'
+```
+## Quickstart
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "Qwen/Qwen2.5-3B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+## Evaluation & Performance
+Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
+For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
+## Citation
+If you find our work helpful, feel free to give us a cite.
+```
+@misc{qwen2.5,
+    title = {Qwen2.5: A Party of Foundation Models},
+    url = {https://qwenlm.github.io/blog/qwen2.5/},
+    author = {Qwen Team},
+    month = {September},
+    year = {2024}
+}
+@article{qwen2,
+      title={Qwen2 Technical Report}, 
+      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
+      journal={arXiv preprint arXiv:2407.10671},
+      year={2024}
+}
+```
\ No newline at end of file
--- a/README.md
+++ b/README.md
+# F5-TTS
+F5-TTS能根据文本内容自动生成带有情感的语音，无论是愤怒、喜悦还是悲伤，都能精准把握情感变化，难辨真伪。
+## 论文
+`F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching`
+- https://arxiv.org/pdf/2410.06885
+## 模型结构
+F5-TTS利用ConvNeXt V2提取文本特征、DiT（核心骨干网络）生成完整的梅尔频谱，利用预训练好的解码器把梅尔频谱解码成声音。
+<div align=center>
+    <img src="./doc/f5tts.png"/>
+</div>
+## 算法原理
+从高斯噪声开始，通过扩散模型逐步生成目标音频特征，最终输出为梅尔频谱，后续通过声码器将其转换为音频波形，DiT 的并行化能力为 TTS 提供了显著的加速效果。
+<div align=center>
+    <img src="./doc/algorithm.png"/>
+</div>
+## 环境配置
+```
+mv f5-tts_pytorch F5-TTS # 去框架名后缀
+```
+### Docker（方法一）
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.2-py3.10
+# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：83714c19d308
+docker run -it --shm-size=64G -v $PWD/F5-TTS:/home/F5-TTS -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name f5tts <your IMAGE ID> bash
+cd /home/F5-TTS
+pip install -e . # f5-tts==0.0.0
+# 安装ffmpeg
+apt update
+apt-get install ffmpeg
+# 解决gradio获取外网可访问的URL
+cp -r frpc_linux_amd64 /usr/local/lib/python3.10/site-packages/gradio/frpc_linux_amd64_v0.2
+chmod +x /usr/local/lib/python3.10/site-packages/gradio/frpc_linux_amd64_v0.2
+```
+### Dockerfile（方法二）
+```
+cd /home/F5-TTS/docker
+docker build --no-cache -t f5tts:latest .
+docker run --shm-size=64G --name f5tts -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../F5-TTS:/home/F5-TTS -it f5tts 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。
+cd /home/F5-TTS
+pip install -e . # f5-tts==0.0.0
+# 安装ffmpeg
+apt update
+apt-get install ffmpeg
+# 解决gradio获取外网可访问的URL
+cp -r frpc_linux_amd64 /usr/local/lib/python3.10/site-packages/gradio/frpc_linux_amd64_v0.2
+chmod +x /usr/local/lib/python3.10/site-packages/gradio/frpc_linux_amd64_v0.2
+```
+### 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:dtk24.04.2
+python:python3.10
+torch:2.3.0
+torchvision:0.18.1
+torchaudio:2.1.2
+triton:2.1.0
+flash-attn:2.0.4
+deepspeed:0.14.2
+apex:1.3.0
+xformers:0.0.25
+```
+`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/F5-TTS
+pip install -e . # f5-tts==0.0.0
+# 安装ffmpeg
+apt update
+apt-get install ffmpeg
+# 解决gradio获取外网可访问的URL
+cp -r frpc_linux_amd64 /usr/local/lib/python3.10/site-packages/gradio/frpc_linux_amd64_v0.2
+chmod +x /usr/local/lib/python3.10/site-packages/gradio/frpc_linux_amd64_v0.2
+```
+## 数据集
+1、公开数据集（部分数据需要从开源官网申请账号获取）：`Emilia_Dataset`、`WenetSpeech4TTS`
+2、自定义数据集（参照源github issue中作者提供的制作方式进行制作）：`custom dataset`
+本文的使用说明仅提供推理步骤，若读者对音频数据集的制作感兴趣，可研究源项目的训练与微调说明文档[`src/f5_tts/train/README.md`](./src/f5_tts/train/README.md)。
+## 训练
+`暂无，敬请期待未来开放。`
+参考源项目的训练与微调说明文档[`src/f5_tts/train/README.md`](./src/f5_tts/train/README.md)。
+## 推理
+```
+# 方法一：终端命令推理
+sh infer.sh
+# 方法二：gradio网页推理
+f5-tts_infer-gradio --share #可生成带live关键字的外网可访问url，实测gradio的生成效果听起来比终端生成效果好听一些。
+```
+更多资料可参考源项目的[`README_origin`](./README_origin.md)
+## result
+以终端命令推理示例：
+`输入: `
+```
+ref_audio: "input.wav"
+ref_text: "The content, subtitle or transcription of reference audio." 
+gen_text: "Some text you want TTS model generate for you."
+```
+`输出:`
+```
+tests/infer_cli_out.wav
+```
+### 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
+`金融,电商,教育,制造,医疗,能源`
+## 预训练权重
+预训练权重快速下载中心：[SCNet AIModels](http://113.200.138.88:18080/aimodels)。
+项目中的预训练权重可从快速下载通道下载：[SWivid/F5-TTS](http://113.200.138.88:18080/aimodels/swivid/F5-TTS.git) 、[SWivid/E2-TTS](http://113.200.138.88:18080/aimodels/swivid/E2-TTS.git)、[charactr/vocos-mel-24khz](http://113.200.138.88:18080/aimodels/charactr/vocos-mel-24khz.git)、[openai/whisper-large-v3-turbo](http://113.200.138.88:18080/aimodels/openai/whisper-large-v3-turbo.git)、[Qwen/Qwen2.5-3B-Instruct](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-3B-Instruct.git)。
+Hugging Face下载地址为：[SWivid/F5-TTS](https://huggingface.co/SWivid/F5-TTS/tree/main/F5TTS_Base) 、[SWivid/E2-TTS](https://huggingface.co/SWivid/E2-TTS/tree/main/E2TTS_Base)、[charactr/vocos-mel-24khz](https://huggingface.co/charactr/vocos-mel-24khz)、[openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo)、[Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)。
+本README中，推理所需预训练权重的完整目录结构如下：
+```
+/home/F5-TTS
+    ├── SWivid/F5-TTS/F5TTS_Base
+    ├── SWivid/E2-TTS/E2TTS_Base
+    ├── charactr/vocos-mel-24khz
+    ├── openai/whisper-large-v3-turbo
+    ├── Qwen/Qwen2.5-3B-Instruct
+    ...
+```    
+## 源码仓库及问题反馈
+- http://developer.sourcefind.cn/codes/modelzoo/f5-tts_pytorch.git
+## 参考资料
+- https://github.com/SWivid/F5-TTS.git
--- a/README_origin.md
+++ b/README_origin.md
+# F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
+[![python](https://img.shields.io/badge/Python-3.10-brightgreen)](https://github.com/SWivid/F5-TTS)
+[![arXiv](https://img.shields.io/badge/arXiv-2410.06885-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2410.06885)
+[![demo](https://img.shields.io/badge/GitHub-Demo%20page-orange.svg)](https://swivid.github.io/F5-TTS/)
+[![hfspace](https://img.shields.io/badge/🤗-Space%20demo-yellow)](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)
+[![msspace](https://img.shields.io/badge/🤖-Space%20demo-blue)](https://modelscope.cn/studios/modelscope/E2-F5-TTS)
+[![lab](https://img.shields.io/badge/X--LANCE-Lab-grey?labelColor=lightgrey)](https://x-lance.sjtu.edu.cn/)
+<img src="https://github.com/user-attachments/assets/12d7749c-071a-427c-81bf-b87b91def670" alt="Watermark" style="width: 40px; height: auto">
+**F5-TTS**: Diffusion Transformer with ConvNeXt V2, faster trained and inference.
+**E2 TTS**: Flat-UNet Transformer, closest reproduction from [paper](https://arxiv.org/abs/2406.18009).
+**Sway Sampling**: Inference-time flow step sampling strategy, greatly improves performance
+### Thanks to all the contributors !
+## News
+- **2024/10/08**: F5-TTS & E2 TTS base models on [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS), [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), [🟣 Wisemodel](https://wisemodel.cn/models/SJTU_X-LANCE/F5-TTS_Emilia-ZH-EN).
+## Installation
+```bash
+# Create a python 3.10 conda env (you could also use virtualenv)
+conda create -n f5-tts python=3.10
+conda activate f5-tts
+# Install pytorch with your CUDA version, e.g.
+pip install torch==2.3.0+cu118 torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
+```
+Then you can choose from a few options below:
+### 1. As a pip package (if just for inference)
+```bash
+pip install git+https://github.com/SWivid/F5-TTS.git
+```
+### 2. Local editable (if also do training, finetuning)
+```bash
+git clone https://github.com/SWivid/F5-TTS.git
+cd F5-TTS
+# git submodule update --init --recursive  # (optional, if need bigvgan)
+pip install -e .
+```
+If initialize submodule, you should add the following code at the beginning of `src/third_party/BigVGAN/bigvgan.py`.
+```python
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+```
+### 3. Docker usage
+```bash
+# Build from Dockerfile
+docker build -t f5tts:v1 .
+# Or pull from GitHub Container Registry
+docker pull ghcr.io/swivid/f5-tts:main
+```
+## Inference
+### 1. Gradio App
+Currently supported features:
+- Basic TTS with Chunk Inference
+- Multi-Style / Multi-Speaker Generation
+- Voice Chat powered by Qwen2.5-3B-Instruct
+```bash
+# Launch a Gradio app (web interface)
+f5-tts_infer-gradio
+# Specify the port/host
+f5-tts_infer-gradio --port 7860 --host 0.0.0.0
+# Launch a share link
+f5-tts_infer-gradio --share
+```
+### 2. CLI Inference
+```bash
+# Run with flags
+# Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
+f5-tts_infer-cli \
+--model "F5-TTS" \
+--ref_audio "ref_audio.wav" \
+--ref_text "The content, subtitle or transcription of reference audio." \
+--gen_text "Some text you want TTS model generate for you."
+# Run with default setting. src/f5_tts/infer/examples/basic/basic.toml
+f5-tts_infer-cli
+# Or with your own .toml file
+f5-tts_infer-cli -c custom.toml
+# Multi voice. See src/f5_tts/infer/README.md
+f5-tts_infer-cli -c src/f5_tts/infer/examples/multi/story.toml
+```
+### 3. More instructions
+- In order to have better generation results, take a moment to read [detailed guidance](src/f5_tts/infer).
+- The [Issues](https://github.com/SWivid/F5-TTS/issues?q=is%3Aissue) are very useful, please try to find the solution by properly searching the keywords of problem encountered. If no answer found, then feel free to open an issue.
+## Training
+### 1. Gradio App
+Read [training & finetuning guidance](src/f5_tts/train) for more instructions.
+```bash
+# Quick start with Gradio web interface
+f5-tts_finetune-gradio
+```
+## [Evaluation](src/f5_tts/eval)
+## Development
+Use pre-commit to ensure code quality (will run linters and formatters automatically)
+```bash
+pip install pre-commit
+pre-commit install
+```
+When making a pull request, before each commit, run: 
+```bash
+pre-commit run --all-files
+```
+Note: Some model components have linting exceptions for E722 to accommodate tensor notation
+## Acknowledgements
+- [E2-TTS](https://arxiv.org/abs/2406.18009) brilliant work, simple and effective
+- [Emilia](https://arxiv.org/abs/2407.05361), [WenetSpeech4TTS](https://arxiv.org/abs/2406.05763) valuable datasets
+- [lucidrains](https://github.com/lucidrains) initial CFM structure with also [bfs18](https://github.com/bfs18) for discussion
+- [SD3](https://arxiv.org/abs/2403.03206) & [Hugging Face diffusers](https://github.com/huggingface/diffusers) DiT and MMDiT code structure
+- [torchdiffeq](https://github.com/rtqichen/torchdiffeq) as ODE solver, [Vocos](https://huggingface.co/charactr/vocos-mel-24khz) as vocoder
+- [FunASR](https://github.com/modelscope/FunASR), [faster-whisper](https://github.com/SYSTRAN/faster-whisper), [UniSpeech](https://github.com/microsoft/UniSpeech) for evaluation tools
+- [ctc-forced-aligner](https://github.com/MahmoudAshraf97/ctc-forced-aligner) for speech edit test
+- [mrfakename](https://x.com/realmrfakename) huggingface space demo ~
+- [f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx/tree/main) Implementation with MLX framework by [Lucas Newman](https://github.com/lucasnewman)
+- [F5-TTS-ONNX](https://github.com/DakeQQ/F5-TTS-ONNX) ONNX Runtime version by [DakeQQ](https://github.com/DakeQQ)
+## Citation
+If our work and codebase is useful for you, please cite as:
+```
+@article{chen-etal-2024-f5tts,
+      title={F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching}, 
+      author={Yushen Chen and Zhikang Niu and Ziyang Ma and Keqi Deng and Chunhui Wang and Jian Zhao and Kai Yu and Xie Chen},
+      journal={arXiv preprint arXiv:2410.06885},
+      year={2024},
+}
+```
+## License
+Our code is released under MIT License. The pre-trained models are licensed under the CC-BY-NC license due to the training data Emilia, which is an in-the-wild dataset. Sorry for any inconvenience this may cause.
--- a/SWivid/E2-TTS/E2TTS_Base/README.md
+++ b/SWivid/E2-TTS/E2TTS_Base/README.md
--- a/SWivid/F5-TTS/F5TTS_Base/README.md
+++ b/SWivid/F5-TTS/F5TTS_Base/README.md
+---
+license: cc-by-nc-4.0
+pipeline_tag: text-to-speech
+library_name: f5-tts
+datasets:
+- amphion/Emilia-Dataset
+---
+### 2024/10/14. We change the License of this ckpt repo to CC-BY-NC-4.0 following the used training set Emilia, which is an in-the-wild dataset. Sorry for any inconvenience this may cause. Our codebase remains under the MIT license.
+Download [F5-TTS](https://huggingface.co/SWivid/F5-TTS/tree/main/F5TTS_Base) or [E2 TTS](https://huggingface.co/SWivid/E2-TTS/tree/main/E2TTS_Base) and place under ckpts/
+```
+ckpts/
+    E2TTS_Base/
+        model_1200000.pt
+    F5TTS_Base/
+        model_1200000.pt
+```
+Inference with .safetensors option
+```
+ckpts/
+    E2TTS_Base/
+        model_1200000.safetensors
+    F5TTS_Base/
+        model_1200000.safetensors
+```
+Github: https://github.com/SWivid/F5-TTS      
+Paper: [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://huggingface.co/papers/2410.06885)
\ No newline at end of file
--- a/charactr/vocos-mel-24khz/README.md
+++ b/charactr/vocos-mel-24khz/README.md
+---
+license: mit
+---
+# Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis
+[Audio samples](https://charactr-platform.github.io/vocos/) |
+Paper [[abs]](https://arxiv.org/abs/2306.00814) [[pdf]](https://arxiv.org/pdf/2306.00814.pdf)
+Vocos is a fast neural vocoder designed to synthesize audio waveforms from acoustic features. Trained using a Generative
+Adversarial Network (GAN) objective, Vocos can generate waveforms in a single forward pass. Unlike other typical
+GAN-based vocoders, Vocos does not model audio samples in the time domain. Instead, it generates spectral
+coefficients, facilitating rapid audio reconstruction through inverse Fourier transform.
+## Installation
+To use Vocos only in inference mode, install it using:
+```bash
+pip install vocos
+```
+If you wish to train the model, install it with additional dependencies:
+```bash
+pip install vocos[train]
+```
+## Usage
+### Reconstruct audio from mel-spectrogram
+```python
+import torch
+from vocos import Vocos
+vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+mel = torch.randn(1, 100, 256)  # B, C, T
+audio = vocos.decode(mel)
+```
+Copy-synthesis from a file:
+```python
+import torchaudio
+y, sr = torchaudio.load(YOUR_AUDIO_FILE)
+if y.size(0) > 1:  # mix to mono
+    y = y.mean(dim=0, keepdim=True)
+y = torchaudio.functional.resample(y, orig_freq=sr, new_freq=24000)
+y_hat = vocos(y)
+```
+## Citation
+If this code contributes to your research, please cite our work:
+```
+@article{siuzdak2023vocos,
+  title={Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis},
+  author={Siuzdak, Hubert},
+  journal={arXiv preprint arXiv:2306.00814},
+  year={2023}
+}
+```
+## License
+The code in this repository is released under the MIT license.
\ No newline at end of file
--- a/doc/algorithm.png
+++ b/doc/algorithm.png
--- a/doc/f5tts.png
+++ b/doc/f5tts.png
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.2-py3.10
+ENV DEBIAN_FRONTEND=noninteractive
+# RUN yum update && yum install -y git cmake wget build-essential
+# RUN source /opt/dtk-24.04.2/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
--- a/docker/requirements.txt
+++ b/docker/requirements.txt
--- a/docker_start.sh
+++ b/docker_start.sh
+docker run -it --shm-size=64G -v $PWD/F5-TTS:/home/F5-TTS -v /public/DL_DATA/AI:/home/AI -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=//dev/dri/ --group-add video --name f5tts 8f910eb2ffa1 bash                                                                                                                                                                                       
+# python -m torch.utils.collect_env
--- a/frpc_linux_amd64
+++ b/frpc_linux_amd64
--- a/icon.png
+++ b/icon.png
--- a/infer.sh
+++ b/infer.sh
+f5-tts_infer-cli --model "F5-TTS" --load_vocoder_from_local  --ref_audio "input0.wav" --ref_text "The content, subtitle or transcription of reference audio." --gen_text "Some text you want TTS model generate for you."
+# f5-tts_infer-cli --model "F5-TTS" --load_vocoder_from_local  --ref_audio "input0.wav" --gen_text "Even when running the whisper-base, it's still very slow, compared to what it should be (on a 4090 GPU)"
--- a/input.wav
+++ b/input.wav