src/f5_tts/train/README.md

Training

Prepare Dataset

Example data processing scripts for Emilia and Wenetspeech4TTS, and you may tailor your own one along with a Dataset class in src/f5_tts/model/dataset.py.

1. Datasets used for pretrained models

Download corresponding dataset first, and fill in the path in scripts.

# Prepare the Emilia dataset
python src/f5_tts/train/datasets/prepare_emilia.py

# Prepare the Wenetspeech4TTS dataset
python src/f5_tts/train/datasets/prepare_wenetspeech4tts.py

2. Create custom dataset with metadata.csv

Use guidance see #57 here.

python src/f5_tts/train/datasets/prepare_csv_wavs.py

Training & Finetuning

Once your datasets are prepared, you can start the training process.

1. Training script used for pretrained model

# setup accelerate config, e.g. use multi-gpu ddp, fp16
# will be to: ~/.cache/huggingface/accelerate/default_config.yaml     
accelerate config
accelerate launch src/f5_tts/train/train.py

2. Finetuning practice

Discussion board for Finetuning #57.

Gradio UI training/finetuning with src/f5_tts/train/finetune_gradio.py see #143.

3. Wandb Logging

The wandb/ dir will be created under path you run training/finetuning scripts.

By default, the training script does NOT use logging (assuming you didn't manually log in using wandb login).

To turn on wandb logging, you can either:

1. Manually login with wandb login: Learn more here
2. Automatically login programmatically by setting an environment variable: Get an API KEY at https://wandb.ai/site/ and set the environment variable as follows:

On Mac & Linux:

export WANDB_API_KEY=<YOUR WANDB API KEY>

On Windows:

set WANDB_API_KEY=<YOUR WANDB API KEY>

Moreover, if you couldn't access Wandb and want to log metrics offline, you can the environment variable as follows:

export WANDB_MODE=offline