Hugging Face's logo

Spaces:
Svngoku
/
maskgct-audio-lab
Running on Zero

App Files Community
maskgct-audio-lab / egs /tts /VALLE /README.md
Hecheng0625's picture
Hecheng0625
Upload 167 files
8c92a11 verified
preview code
|
raw
history blame
9.79 kB

VALL-E Recipe

In this recipe, we will show how to train VALL-E using Amphion's infrastructure. VALL-E is a zero-shot TTS architecture that uses a neural codec language model with discrete codes.

There are four stages in total:

  1. Data preparation
  2. Features extraction
  3. Training
  4. Inference

NOTE: You need to run every command of this recipe in the Amphion root path:

cd Amphion

1. Data Preparation

Dataset Download

You can use the commonly used TTS dataset to train the VALL-E model, e.g., LibriTTS, etc. We strongly recommend you use LibriTTS to train the VALL-E model for the first time. How to download the dataset is detailed here.

Configuration

After downloading the dataset, you can set the dataset paths in exp_config.json. Note that you can change the dataset list to use your preferred datasets.

    "dataset": [
        "libritts",
    ],
    "dataset_path": {
        // TODO: Fill in your dataset path
        "libritts": "[LibriTTS dataset path]",
    },

2. Features Extraction

Configuration

Specify the processed_dir and the log_dir and for saving the processed data and the checkpoints in exp_config.json:

    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts"
    "log_dir": "ckpts/tts",
    "preprocess": {
        // TODO: Fill in the output data path. The default value is "Amphion/data"
        "processed_dir": "data",
        ...
    },

Run

Run the run.sh as the preprocess stage (set --stage 1):

sh egs/tts/VALLE/run.sh --stage 1

NOTE: The CUDA_VISIBLE_DEVICES is set as "0" in default. You can change it when running run.sh by specifying such as --gpu "1".

3. Training

Configuration

We provide the default hyperparameters in the exp_config.json. They can work on a single NVIDIA-24g GPU. You can adjust them based on your GPU machines.

"train": {
        "batch_size": 4,
    }

Train From Scratch

Run the run.sh as the training stage (set --stage 2). Specify an experimental name to run the following command. The tensorboard logs and checkpoints will be saved in Amphion/ckpts/tts/[YourExptName].

Specifically, VALL-E needs to train an autoregressive (AR) model and then a non-autoregressive (NAR) model. So, you can set --model_train_stage 1 to train AR model, and set --model_train_stage 2 to train NAR model, where --ar_model_ckpt_dir should be set as the checkpoint path to the trained AR model.

Train an AR model, just run:

sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 1 --name [YourExptName]

Train a NAR model, just run:

sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 2 --ar_model_ckpt_dir [ARModelPath] --name [YourExptName]

Train From Existing Source

We support training from existing sources for various purposes. You can resume training the model from a checkpoint or fine-tune a model from another checkpoint.

By setting --resume true, the training will resume from the latest checkpoint from the current [YourExptName] by default. For example, if you want to resume training from the latest checkpoint in Amphion/ckpts/tts/[YourExptName]/checkpoint,

Train an AR model, just run:

sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 1 --name [YourExptName] \
    --resume true

Train a NAR model, just run:

sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 2 --ar_model_ckpt_dir [ARModelPath] --name [YourExptName] \
    --resume true

You can also choose a specific checkpoint for retraining by --resume_from_ckpt_path argument. For example, if you want to resume training from the checkpoint Amphion/ckpts/tts/[YourExptName]/checkpoint/[SpecificCheckpoint],

Train an AR model, just run:

sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 1 --name [YourExptName] \
    --resume true \
    --resume_from_ckpt_path "Amphion/ckpts/tts/[YourExptName]/checkpoint/[SpecificARCheckpoint]"

Train a NAR model, just run:

sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 2 --ar_model_ckpt_dir [ARModelPath] --name [YourExptName] \
    --resume true \
    --resume_from_ckpt_path "Amphion/ckpts/tts/[YourExptName]/checkpoint/[SpecificNARCheckpoint]"

If you want to fine-tune from another checkpoint, just use --resume_type and set it to "finetune". For example, If you want to fine-tune the model from the checkpoint Amphion/ckpts/tts/[AnotherExperiment]/checkpoint/[SpecificCheckpoint],

Train an AR model, just run:

sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 1 --name [YourExptName] \
    --resume true \
    --resume_from_ckpt_path "Amphion/ckpts/tts/[YourExptName]/checkpoint/[SpecificARCheckpoint]" \
    --resume_type "finetune"

Train a NAR model, just run:

sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 2 --ar_model_ckpt_dir [ARModelPath] --name [YourExptName] \
    --resume true \
    --resume_from_ckpt_path "Amphion/ckpts/tts/[YourExptName]/checkpoint/[SpecificNARCheckpoint]" \
    --resume_type "finetune"

NOTE: The --resume_type is set as "resume" in default. It's not necessary to specify it when resuming training.

The difference between "resume" and "finetune" is that the "finetune" will only load the pretrained model weights from the checkpoint, while the "resume" will load all the training states (including optimizer, scheduler, etc.) from the checkpoint.

NOTE: The CUDA_VISIBLE_DEVICES is set as "0" in default. You can change it when running run.sh by specifying such as --gpu "0,1,2,3".

4. Inference

Configuration

For inference, you need to specify the following configurations when running run.sh:

Parameters Description Example
--infer_expt_dir The experimental directory of NAR model which contains checkpoint Amphion/ckpts/tts/[YourExptName]
--infer_output_dir The output directory to save inferred audios. Amphion/ckpts/tts/[YourExptName]/result
--infer_mode The inference mode, e.g., "single", "batch". "single" to generate a clip of speech, "batch" to generate a batch of speech at a time.
--infer_text The text to be synthesized. "This is a clip of generated speech with the given text from a TTS model."
--infer_text_prompt The text prompt for inference. The text prompt should be aligned with the audio prompt.
--infer_audio_prompt The audio prompt for inference. The audio prompt should be aligned with text prompt.
--test_list_file The test list file used for batch inference. The format of test list file is text|text_prompt|audio_prompt.

Run

For example, if you want to generate a single clip of speech, just run:

sh egs/tts/VALLE/run.sh --stage 3 --gpu "0" \
    --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \
    --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \
    --infer_mode "single" \
    --infer_text "This is a clip of generated speech with the given text from a TTS model." \
    --infer_text_prompt "But even the unsuccessful dramatist has his moments." \
    --infer_audio_prompt egs/tts/VALLE/prompt_examples/7176_92135_000004_000000.wav

We have released pre-trained VALL-E models, so you can download the pre-trained model and then generate speech following the above inference instruction. Specifically,

  1. The pre-trained VALL-E trained on LibriTTS can be downloaded here.
  2. The pre-trained VALL-E trained on the part of Libri-light (about 6k hours) can be downloaded here.
@article{wang2023neural,
  title={Neural codec language models are zero-shot text to speech synthesizers},
  author={Wang, Chengyi and Chen, Sanyuan and Wu, Yu and Zhang, Ziqiang and Zhou, Long and Liu, Shujie and Chen, Zhuo and Liu, Yanqing and Wang, Huaming and Li, Jinyu and others},
  journal={arXiv preprint arXiv:2301.02111},
  year={2023}
}