93 lines
3.2 KiB
Markdown
93 lines
3.2 KiB
Markdown
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# Training
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Check your FFmpeg installation:
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```bash
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ffmpeg -version
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```
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If not found, install it first (or skip assuming you know of other backends available).
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## Prepare Dataset
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Example data processing scripts, and you may tailor your own one along with a Dataset class in `src/f5_tts/model/dataset.py`.
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### 1. Some specific Datasets preparing scripts
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Download corresponding dataset first, and fill in the path in scripts.
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```bash
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# Prepare the Emilia dataset
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python src/f5_tts/train/datasets/prepare_emilia.py
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# Prepare the Wenetspeech4TTS dataset
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python src/f5_tts/train/datasets/prepare_wenetspeech4tts.py
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# Prepare the LibriTTS dataset
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python src/f5_tts/train/datasets/prepare_libritts.py
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# Prepare the LJSpeech dataset
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python src/f5_tts/train/datasets/prepare_ljspeech.py
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```
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### 2. Create custom dataset with metadata.csv
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Use guidance see [#57 here](https://github.com/SWivid/F5-TTS/discussions/57#discussioncomment-10959029).
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```bash
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python src/f5_tts/train/datasets/prepare_csv_wavs.py
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```
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## Training & Finetuning
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Once your datasets are prepared, you can start the training process.
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### 1. Training script used for pretrained model
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```bash
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# setup accelerate config, e.g. use multi-gpu ddp, fp16
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# will be to: ~/.cache/huggingface/accelerate/default_config.yaml
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accelerate config
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# .yaml files are under src/f5_tts/configs directory
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accelerate launch src/f5_tts/train/train.py --config-name F5TTS_v1_Base.yaml
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# possible to overwrite accelerate and hydra config
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accelerate launch --mixed_precision=fp16 src/f5_tts/train/train.py --config-name F5TTS_v1_Base.yaml ++datasets.batch_size_per_gpu=19200
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```
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### 2. Finetuning practice
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Discussion board for Finetuning [#57](https://github.com/SWivid/F5-TTS/discussions/57).
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Gradio UI training/finetuning with `src/f5_tts/train/finetune_gradio.py` see [#143](https://github.com/SWivid/F5-TTS/discussions/143).
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If want to finetune with a variant version e.g. *F5TTS_v1_Base_no_zero_init*, manually download pretrained checkpoint from model weight repository and fill in the path correspondingly on web interface.
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If use tensorboard as logger, install it first with `pip install tensorboard`.
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<ins>The `use_ema = True` might be harmful for early-stage finetuned checkpoints</ins> (which goes just few updates, thus ema weights still dominated by pretrained ones), try turn it off with finetune gradio option or `load_model(..., use_ema=False)`, see if offer better results.
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### 3. W&B Logging
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The `wandb/` dir will be created under path you run training/finetuning scripts.
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By default, the training script does NOT use logging (assuming you didn't manually log in using `wandb login`).
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To turn on wandb logging, you can either:
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1. Manually login with `wandb login`: Learn more [here](https://docs.wandb.ai/ref/cli/wandb-login)
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2. Automatically login programmatically by setting an environment variable: Get an API KEY at https://wandb.ai/authorize and set the environment variable as follows:
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On Mac & Linux:
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```
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export WANDB_API_KEY=<YOUR WANDB API KEY>
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```
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On Windows:
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```
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set WANDB_API_KEY=<YOUR WANDB API KEY>
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```
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Moreover, if you couldn't access W&B and want to log metrics offline, you can set the environment variable as follows:
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```
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export WANDB_MODE=offline
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```
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