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README.md

@@ -1,196 +1,13 @@
-# 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-blue.svg)](https://swivid.github.io/F5-TTS/)
-[![space](https://img.shields.io/badge/🤗-Space%20demo-yellow)](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)
-
-**F5-TTS**: Diffusion Transformer with ConvNeXt V2, faster trained and inference.
-
-**E2 TTS**: Flat-UNet Transformer, closest reproduction.
-
-**Sway Sampling**: Inference-time flow step sampling strategy, greatly improves performance
-
-## Installation
-
-Clone the repository:
-
-```bash
-git clone https://github.com/SWivid/F5-TTS.git
-cd F5-TTS
-```
-
-Install torch with your CUDA version, e.g. :
-
-```bash
-pip install torch==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
-pip install torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
-```
-
-Install other packages:
-
-```bash
-pip install -r requirements.txt
-```
-
-## Prepare Dataset
-
-Example data processing scripts for Emilia and Wenetspeech4TTS, and you may tailor your own one along with a Dataset class in `model/dataset.py`.
-
-```bash
-# prepare custom dataset up to your need
-# download corresponding dataset first, and fill in the path in scripts
-
-# Prepare the Emilia dataset
-python scripts/prepare_emilia.py
-
-# Prepare the Wenetspeech4TTS dataset
-python scripts/prepare_wenetspeech4tts.py
-```
-
-## Training
-
-Once your datasets are prepared, you can start the training process.
-
-```bash
-# setup accelerate config, e.g. use multi-gpu ddp, fp16
-# will be to: ~/.cache/huggingface/accelerate/default_config.yaml     
-accelerate config
-accelerate launch train.py
-```
-An initial guidance on Finetuning [#57](https://github.com/SWivid/F5-TTS/discussions/57).
-
-## Inference
-
-To run inference with pretrained models, download the checkpoints from [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS), or automatically downloaded with `inference-cli` and `gradio_app`.
-
-Currently support 30s for a single generation, which is the **TOTAL** length of prompt audio and the generated. Batch inference with chunks is supported by `inference-cli` and `gradio_app`. 
-- To avoid possible inference failures, make sure you have seen through the following instructions.
-- A longer prompt audio allows shorter generated output. The part longer than 30s cannot be generated properly. Consider using a prompt audio <15s.
-- Uppercased letters will be uttered letter by letter, so use lowercased letters for normal words. 
-- Add some spaces (blank: " ") or punctuations (e.g. "," ".") to explicitly introduce some pauses. If first few words skipped in code-switched generation (cuz different speed with different languages), this might help.
-
-### CLI Inference
-
-Either you can specify everything in `inference-cli.toml` or override with flags. Leave `--ref_text ""` will have ASR model transcribe the reference audio automatically (use extra GPU memory). If encounter network error, consider use local ckpt, just set `ckpt_path` in `inference-cli.py`
-
-```bash
-python inference-cli.py \
---model "F5-TTS" \
---ref_audio "tests/ref_audio/test_en_1_ref_short.wav" \
---ref_text "Some call me nature, others call me mother nature." \
---gen_text "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences."
-
-python inference-cli.py \
---model "E2-TTS" \
---ref_audio "tests/ref_audio/test_zh_1_ref_short.wav" \
---ref_text "对，这就是我，万人敬仰的太乙真人。" \
---gen_text "突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道，我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"
-```
-
-### Gradio App
-Currently supported features:
-- Chunk inference
-- Podcast Generation
-- Multiple Speech-Type Generation
-
-You can launch a Gradio app (web interface) to launch a GUI for inference (will load ckpt from Huggingface, you may set `ckpt_path` to local file in `gradio_app.py`). Currently load ASR model, F5-TTS and E2 TTS all in once, thus use more GPU memory than `inference-cli`.
-
-```bash
-python gradio_app.py
-```
-
-You can specify the port/host:
-
-```bash
-python gradio_app.py --port 7860 --host 0.0.0.0
-```
-
-Or launch a share link:
-
-```bash
-python gradio_app.py --share
-```
-
-### Speech Editing
-
-To test speech editing capabilities, use the following command.
-
-```bash
-python speech_edit.py
-```
-
-## Evaluation
-
-### Prepare Test Datasets
-
-1. Seed-TTS test set: Download from [seed-tts-eval](https://github.com/BytedanceSpeech/seed-tts-eval).
-2. LibriSpeech test-clean: Download from [OpenSLR](http://www.openslr.org/12/).
-3. Unzip the downloaded datasets and place them in the data/ directory.
-4. Update the path for the test-clean data in `scripts/eval_infer_batch.py`
-5. Our filtered LibriSpeech-PC 4-10s subset is already under data/ in this repo
-
-### Batch Inference for Test Set
-
-To run batch inference for evaluations, execute the following commands:
-
-```bash
-# batch inference for evaluations
-accelerate config  # if not set before
-bash scripts/eval_infer_batch.sh
-```
-
-### Download Evaluation Model Checkpoints
-
-1. Chinese ASR Model: [Paraformer-zh](https://huggingface.co/funasr/paraformer-zh)
-2. English ASR Model: [Faster-Whisper](https://huggingface.co/Systran/faster-whisper-large-v3)
-3. WavLM Model: Download from [Google Drive](https://drive.google.com/file/d/1-aE1NfzpRCLxA4GUxX9ITI3F9LlbtEGP/view).
-
-### Objective Evaluation
-
-**Some Notes**
-
-For faster-whisper with CUDA 11:
-
-```bash
-pip install --force-reinstall ctranslate2==3.24.0
-```
-
-(Recommended) To avoid possible ASR failures, such as abnormal repetitions in output:
-
-```bash
-pip install faster-whisper==0.10.1
-```
-
-Update the path with your batch-inferenced results, and carry out WER / SIM evaluations:
-```bash
-# Evaluation for Seed-TTS test set
-python scripts/eval_seedtts_testset.py
-
-# Evaluation for LibriSpeech-PC test-clean (cross-sentence)
-python scripts/eval_librispeech_test_clean.py
-```
-
-## 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
-- [mrfakename](https://x.com/realmrfakename) huggingface space demo ~
-- [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
-
-## Citation
-```
-@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.
+---
+title: F5-TTS
+emoji: 🗣️
+colorFrom: green
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: true
+short_description: 'F5-TTS & E2-TTS: Zero-Shot Voice Cloning (Unofficial Demo)'
+sdk_version: 5.1.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference