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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 released a pre-trained Amphion VALL-E model. So you can download the pre-trained model [here](https://huggingface.co/amphion/valle-libritts) 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}
}
``` |