fish-speech/docs/en/finetune.md

Fine-tuning

Obviously, when you opened this page, you were not satisfied with the performance of the few-shot pre-trained model. You want to fine-tune a model to improve its performance on your dataset.

Fish Speech consists of two modules: VQGAN and LLAMA.

!!! info

You should first conduct the following test to determine if you need to fine-tune `VQGAN`:
```bash
python tools/vqgan/inference.py -i test.wav
```
This test will generate a `fake.wav` file. If the timbre of this file differs from the speaker's original voice, or if the quality is not high, you need to fine-tune `VQGAN`.

Similarly, you can refer to [Inference](inference.md) to run `generate.py` and evaluate if the prosody meets your expectations. If it does not, then you need to fine-tune `LLAMA`.

Fine-tuning VQGAN

1. Prepare the Dataset

.
├── SPK1
│   ├── 21.15-26.44.mp3
│   ├── 27.51-29.98.mp3
│   └── 30.1-32.71.mp3
└── SPK2
    └── 38.79-40.85.mp3

You need to format your dataset as shown above and place it under data/demo. Audio files can have .mp3, .wav, or .flac extensions.

2. Split Training and Validation Sets

python tools/vqgan/create_train_split.py data/demo

This command will create data/demo/vq_train_filelist.txt and data/demo/vq_val_filelist.txt in the data/demo directory, to be used for training and validation respectively.

!!!info

For the VITS format, you can specify a file list using `--filelist xxx.list`.  
Please note that the audio files in `filelist` must also be located in the `data/demo` folder.

3. Start Training

python fish_speech/train.py --config-name vqgan_finetune

!!! note

You can modify training parameters by editing `fish_speech/configs/vqgan_finetune.yaml`, but in most cases, this won't be necessary.

4. Test the Audio

python tools/vqgan/inference.py -i test.wav --checkpoint-path results/vqgan_finetune/checkpoints/step_000010000.ckpt

You can review fake.wav to assess the fine-tuning results.

!!! note

You may also try other checkpoints. We suggest using the earliest checkpoint that meets your requirements, as they often perform better on out-of-distribution (OOD) data.

Fine-tuning LLAMA

1. Prepare the dataset

.
├── SPK1
│   ├── 21.15-26.44.lab
│   ├── 21.15-26.44.mp3
│   ├── 27.51-29.98.lab
│   ├── 27.51-29.98.mp3
│   ├── 30.1-32.71.lab
│   └── 30.1-32.71.mp3
└── SPK2
    ├── 38.79-40.85.lab
    └── 38.79-40.85.mp3

You need to convert your dataset into the above format and place it under data/demo. The audio file can have the extensions .mp3, .wav, or .flac, and the annotation file can have the extensions .lab or .txt.

!!! note

You can modify the dataset path and mix datasets by modifying `fish_speech/configs/data/finetune.yaml`.

2. Batch extraction of semantic tokens

Make sure you have downloaded the VQGAN weights. If not, run the following command:

huggingface-cli download fishaudio/speech-lm-v1 vqgan-v1.pth --local-dir checkpoints

You can then run the following command to extract semantic tokens:

python tools/vqgan/extract_vq.py data/demo \
    --num-workers 1 --batch-size 16 \
    --config-name "vqgan_pretrain" \
    --checkpoint-path "checkpoints/vqgan-v1.pth"

!!! note

You can adjust `--num-workers` and `--batch-size` to increase extraction speed, but please make sure not to exceed your GPU memory limit.  
For the VITS format, you can specify a file list using `--filelist xxx.list`.

This command will create .npy files in the data/demo directory, as shown below:

.
├── SPK1
│   ├── 21.15-26.44.lab
│   ├── 21.15-26.44.mp3
│   ├── 21.15-26.44.npy
│   ├── 27.51-29.98.lab
│   ├── 27.51-29.98.mp3
│   ├── 27.51-29.98.npy
│   ├── 30.1-32.71.lab
│   ├── 30.1-32.71.mp3
│   └── 30.1-32.71.npy
└── SPK2
    ├── 38.79-40.85.lab
    ├── 38.79-40.85.mp3
    └── 38.79-40.85.npy

3. Pack the dataset into protobuf

python tools/llama/build_dataset.py \
    --config "fish_speech/configs/data/finetune.yaml" \
    --output "data/quantized-dataset-ft.protos"

After the command finishes executing, you should see the quantized-dataset-ft.protos file in the data directory.

!!!info

For the VITS format, you can specify a file list using `--filelist xxx.list`.

4. Start the Rust data server

Loading and shuffling the dataset is very slow and memory-consuming. Therefore, we use a Rust server to load and shuffle the data. This server is based on GRPC and can be installed using the following method:

cd data_server
cargo build --release

After the compilation is complete, you can start the server using the following command:

export RUST_LOG=info # Optional, for debugging
data_server/target/release/data_server \
    --files "data/quantized-dataset-ft.protos" 

!!! note

You can specify multiple `--files` parameters to load multiple datasets.

5. Finally, start the fine-tuning

Similarly, make sure you have downloaded the LLAMA weights. If not, run the following command:

huggingface-cli download fishaudio/speech-lm-v1 text2semantic-400m-v0.2-4k.pth --local-dir checkpoints

Finally, you can start the fine-tuning by running the following command:

python fish_speech/train.py --config-name text2semantic_finetune

!!! info

If you want to use lora, please use `--config-name text2semantic_finetune_lora` to start fine-tuning.

!!! note

You can modify the training parameters such as `batch_size`, `gradient_accumulation_steps`, etc. to fit your GPU memory by modifying `fish_speech/configs/text2semantic_finetune.yaml`.

After training is complete, you can refer to the inference section, and use --speaker SPK1 to generate speech.

!!! info

By default, the model will only learn the speaker's speech patterns and not the timbre. You still need to use prompts to ensure timbre stability.
If you want to learn the timbre, you can increase the number of training steps, but this may lead to overfitting.