# 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 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](../../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 preprocess 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 hyperparameters in the `exp_config.json`. They can work on a single NVIDIA-24g GPU. You can adjust them based on your GPU machines. ```json "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: ```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] ``` ### 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: ```bash sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 1 --name [YourExptName] \ --resume true ``` 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] \ --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: ```bash 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: ```bash 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: ```bash 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: ```bash 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: ```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 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](https://github.com/open-mmlab/Amphion/tree/main/egs/datasets#libritts) can be downloaded [here](https://huggingface.co./amphion/valle-libritts). 2. The pre-trained VALL-E trained on the part of [Libri-light](https://ai.meta.com/tools/libri-light/) (about 6k hours) can be downloaded [here](https://huggingface.co./amphion/valle_librilight_6k). ```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} } ```