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| # VALL-E Recipe | |
| In this recipe, we will show how to train [VALL-E](https://arxiv.org/abs/2301.02111) 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: | |
| > ```bash | |
| > cd Amphion | |
| > ``` | |
| ## 1. Data Preparation | |
| ### Dataset Download | |
| You can use the commonly used TTS dataset to train VALL-E model, e.g., LibriTTS, etc. We strongly recommend you use LibriTTS to train VALL-E model for the first time. How to download dataset is detailed [here](../../datasets/README.md). | |
| ### 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. | |
| ```json | |
| "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`: | |
| ```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`): | |
| ```bash | |
| 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 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": 4, | |
| } | |
| ``` | |
| ### 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]`. | |
| Specifically, VALL-E need to train a 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 ckeckpoint path to the trained AR model. | |
| Train a AR moel, just run: | |
| ```bash | |
| sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 1 --name [YourExptName] | |
| ``` | |
| Train a NAR model, just run: | |
| ```bash | |
| sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 2 --ar_model_ckpt_dir [ARModelPath] --name [YourExptName] | |
| ``` | |
| <!-- > **NOTE:** To train a NAR model, `--checkpoint_path` should be set as the ckeckpoint path to the trained AR model. --> | |
| > **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: | |
| ```bash | |
| 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 will release a pre-trained VALL-E. So you can download the pre-trained model and generate speech following the above inference instruction. | |
| ```bibtex | |
| @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} | |
| } | |
| ``` |