egs/tts/VITS/README.md

VITS Recipe

In this recipe, we will show how to train VITS using Amphion's infrastructure. VITS is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning.

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 TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download 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": [
        "LJSpeech",
    ],
    "dataset_path": {
        // TODO: Fill in your dataset path
        "LJSpeech": "[LJSpeech 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 preproces stage (set --stage 1):

sh egs/tts/VITS/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 hyparameters in the exp_config.json. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines.

"train": {
        "batch_size": 16,
    }

Run

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

sh egs/tts/VITS/run.sh --stage 2 --name [YourExptName]

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 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_dataset	The dataset used for inference.	For LJSpeech dataset, the inference dataset would be LJSpeech.
--infer_testing_set	The subset of the inference dataset used for inference, e.g., train, test, golden_test	For LJSpeech dataset, the testing set would be  "test" split from LJSpeech at the feature extraction, or "golden_test" cherry-picked from test set as template testing set.
--infer_text	The text to be synthesized.	"This is a clip of generated speech with the given text from a TTS model."

Run

For example, if you want to generate speech of all testing set split from LJSpeech, just run:

sh egs/tts/VITS/run.sh --stage 3 --gpu "0" \
    --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \
    --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \
    --infer_mode "batch" \
    --infer_dataset "LJSpeech" \
    --infer_testing_set "test"

Or, if you want to generate a single clip of speech from a given text, just run:

sh egs/tts/VITS/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."

We released a pre-trained Amphion VITS model trained on LJSpeech. So you can download the pre-trained model here and generate speech following the above inference instruction.

@inproceedings{kim2021conditional,
  title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech},
  author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee},
  booktitle={International Conference on Machine Learning},
  pages={5530--5540},
  year={2021},
}