# 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 The pretrained model checkpoints can be reached at [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS) and [⭐ Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), 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 Install packages for evaluation: ```bash pip install -r requirements_eval.txt ``` **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. 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.