Update README.md

README.md

@@ -1,261 +1,10 @@
-# 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/)
-[![lab](https://img.shields.io/badge/Peng%20Cheng-Lab-grey?labelColor=lightgrey)](https://www.pcl.ac.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
-- **2025/03/12**: 🔥 F5-TTS v1 base model with better training and inference performance. [Few demo](https://swivid.github.io/F5-TTS_updates).
-- **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
-
-### Create a separate environment if needed
-
-```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 matched device
-
-<details>
-<summary>NVIDIA GPU</summary>
-
-> ```bash
-> # Install pytorch with your CUDA version, e.g.
-> pip install torch==2.4.0+cu124 torchaudio==2.4.0+cu124 --extra-index-url https://download.pytorch.org/whl/cu124
-> ```
-
-</details>
-
-<details>
-<summary>AMD GPU</summary>
-
-> ```bash
-> # Install pytorch with your ROCm version (Linux only), e.g.
-> pip install torch==2.5.1+rocm6.2 torchaudio==2.5.1+rocm6.2 --extra-index-url https://download.pytorch.org/whl/rocm6.2
-> ```
-
-</details>
-
-<details>
-<summary>Intel GPU</summary>
-
-> ```bash
-> # Install pytorch with your XPU version, e.g.
-> # Intel® Deep Learning Essentials or Intel® oneAPI Base Toolkit must be installed
-> pip install torch torchaudio --index-url https://download.pytorch.org/whl/test/xpu
-> 
-> # Intel GPU support is also available through IPEX (Intel® Extension for PyTorch)
-> # IPEX does not require the Intel® Deep Learning Essentials or Intel® oneAPI Base Toolkit
-> # See: https://pytorch-extension.intel.com/installation?request=platform
-> ```
-
-</details>
-
-<details>
-<summary>Apple Silicon</summary>
-
-> ```bash
-> # Install the stable pytorch, e.g.
-> pip install torch torchaudio
-> ```
-
-</details>
-
-### Then you can choose one from below:
-
-> ### 1. As a pip package (if just for inference)
-> 
-> ```bash
-> pip install f5-tts
-> ```
-> 
-> ### 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 use bigvgan as vocoder)
-> pip install -e .
-> ```
-
-### Docker usage also available
-```bash
-# Build from Dockerfile
-docker build -t f5tts:v1 .
-
-# Run from GitHub Container Registry
-docker container run --rm -it --gpus=all --mount 'type=volume,source=f5-tts,target=/root/.cache/huggingface/hub/' -p 7860:7860 ghcr.io/swivid/f5-tts:main
-
-# Quickstart if you want to just run the web interface (not CLI)
-docker container run --rm -it --gpus=all --mount 'type=volume,source=f5-tts,target=/root/.cache/huggingface/hub/' -p 7860:7860 ghcr.io/swivid/f5-tts:main f5-tts_infer-gradio --host 0.0.0.0
-```
-
-### Runtime
-
-Deployment solution with Triton and TensorRT-LLM.
-
-#### Benchmark Results
-Decoding on a single L20 GPU, using 26 different prompt_audio & target_text pairs, 16 NFE.
-
-| Model               | Concurrency    | Avg Latency | RTF    | Mode            |
-|---------------------|----------------|-------------|--------|-----------------|
-| F5-TTS Base (Vocos) | 2              | 253 ms      | 0.0394 | Client-Server   |
-| F5-TTS Base (Vocos) | 1 (Batch_size) | -           | 0.0402 | Offline TRT-LLM |
-| F5-TTS Base (Vocos) | 1 (Batch_size) | -           | 0.1467 | Offline Pytorch |
-
-See [detailed instructions](src/f5_tts/runtime/triton_trtllm/README.md) for more information.
-
-
-## Inference
-
-- In order to achieve desired performance, take a moment to read [detailed guidance](src/f5_tts/infer).
-- By properly searching the keywords of problem encountered, [issues](https://github.com/SWivid/F5-TTS/issues?q=is%3Aissue) are very helpful.
-
-### 1. Gradio App
-
-Currently supported features:
-
-- Basic TTS with Chunk Inference
-- Multi-Style / Multi-Speaker Generation
-- Voice Chat powered by Qwen2.5-3B-Instruct
-- [Custom inference with more language support](src/f5_tts/infer/SHARED.md)
-
-```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
-```
-
-<details>
-<summary>NVIDIA device docker compose file example</summary>
-
-```yaml
-services:
-  f5-tts:
-    image: ghcr.io/swivid/f5-tts:main
-    ports:
-      - "7860:7860"
-    environment:
-      GRADIO_SERVER_PORT: 7860
-    entrypoint: ["f5-tts_infer-gradio", "--port", "7860", "--host", "0.0.0.0"]
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-
-volumes:
-  f5-tts:
-    driver: local
-```
-
-</details>
-
-### 2. CLI Inference
-
-```bash
-# Run with flags
-# Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
-f5-tts_infer-cli --model F5TTS_v1_Base \
---ref_audio "provide_prompt_wav_path_here.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
-```
-
-
-## Training
-
-### 1. With Hugging Face Accelerate
-
-Refer to [training & finetuning guidance](src/f5_tts/train) for best practice.
-
-### 2. With Gradio App
-
-```bash
-# Quick start with Gradio web interface
-f5-tts_finetune-gradio
-```
-
-Read [training & finetuning guidance](src/f5_tts/train) for more instructions.
-
-
-## [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), [LibriTTS](https://arxiv.org/abs/1904.02882), [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) 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) and [BigVGAN](https://github.com/NVIDIA/BigVGAN) as vocoder
-- [FunASR](https://github.com/modelscope/FunASR), [faster-whisper](https://github.com/SYSTRAN/faster-whisper), [UniSpeech](https://github.com/microsoft/UniSpeech), [SpeechMOS](https://github.com/tarepan/SpeechMOS) 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)
-- [Yuekai Zhang](https://github.com/yuekaizhang) Triton and TensorRT-LLM support ~
-
-## 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.
+---
+title: F5-TTS Fine-tuned Demo
+emoji: 🎙️
+colorFrom: indigo
+colorTo: pink
+sdk: gradio
+sdk_version: "4.29.0"
+app_file: app.py
+pinned: false
+---