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# VITS Recipe
[](https://huggingface.co/spaces/amphion/Text-to-Speech)
[](https://openxlab.org.cn/apps/detail/Amphion/Text-to-Speech)
In this recipe, we will show how to train VITS using Amphion's infrastructure. [VITS](https://arxiv.org/abs/2106.06103) is an end-to-end TTS architecture that utilizes a 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:
> ```bash
> cd Amphion
> ```
## 1. Data Preparation
### Dataset Download
You can use the commonly used TTS dataset to train the TTS model, e.g., LJSpeech, VCTK, Hi-Fi TTS, LibriTTS, etc. We strongly recommend using LJSpeech to train the single-speaker TTS model for the first time. While training the multi-speaker TTS model for the first time, we recommend using Hi-Fi TTS. The process of downloading the dataset has been 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": [
"LJSpeech",
//"hifitts"
],
"dataset_path": {
// TODO: Fill in your dataset path
"LJSpeech": "[LJSpeech dataset path]",
//"hifitts": "[Hi-Fi TTS dataset path]
},
```
## 2. Features Extraction
### Configuration
In `exp_config.json`, specify the `log_dir` for saving the checkpoints and logs, and specify the `processed_dir` for saving processed data. For preprocessing the multi-speaker TTS dataset, set `extract_audio` and `use_spkid` to `true`:
```json
// TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts"
"log_dir": "ckpts/tts",
"preprocess": {
//"extract_audio": true,
"use_phone": true,
// linguistic features
"extract_phone": true,
"phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)"
// TODO: Fill in the output data path. The default value is "Amphion/data"
"processed_dir": "data",
"sample_rate": 22050, //target sampling rate
"valid_file": "valid.json", //validation set
//"use_spkid": true, //use speaker ID to train multi-speaker TTS model
},
```
### Run
Run the `run.sh` as the preprocess stage (set `--stage 1`):
```bash
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 hyperparameters in the `exp_config.json`. They can work on a single NVIDIA-24g GPU. You can adjust them based on your GPU machines.
For training the multi-speaker TTS model, specify the `n_speakers` value to be greater (used for new speaker fine-tuning) than or equal to the number of speakers in your dataset(s) and set `multi_speaker_training` to `true`.
```json
"model": {
//"n_speakers": 10 //Number of speakers in the dataset(s) used. The default value is 0 if not specified.
},
"train": {
"batch_size": 16,
//"multi_speaker_training": true,
}
```
### 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]`.
```bash
sh egs/tts/VITS/run.sh --stage 2 --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`, run:
```bash
sh egs/tts/VITS/run.sh --stage 2 --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]`, run:
```bash
sh egs/tts/VITS/run.sh --stage 2 --name [YourExptName] \
--resume true \
--resume_from_ckpt_path "Amphion/ckpts/tts/[YourExptName]/checkpoint/[SpecificCheckpoint]"
```
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]`, run:
```bash
sh egs/tts/VITS/run.sh --stage 2 --name [YourExptName] \
--resume true \
--resume_from_ckpt_path "Amphion/ckpts/tts/[YourExptName]/checkpoint/[SpecificCheckpoint]" \
--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.
Here are some example scenarios to better understand how to use these arguments:
| Scenario | `--resume` | `--resume_from_ckpt_path` | `--resume_type` |
| ------ | -------- | ----------------------- | ------------- |
| You want to train from scratch | no | no | no |
| The machine breaks down during training and you want to resume training from the latest checkpoint | `true` | no | no |
| You find the latest model is overfitting and you want to re-train from the checkpoint before | `true` | `SpecificCheckpoint Path` | no |
| You want to fine-tune a model from another checkpoint | `true` | `SpecificCheckpoint Path` | `"finetune"` |
> **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
### Pre-trained Model Download
We released a pre-trained Amphion VITS model trained on LJSpeech. So you can download the pre-trained model [here](https://huggingface.co/amphion/vits-ljspeech) and generate speech according to the following inference instruction.
### 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`.<br> For Hi-Fi TTS dataset, the inference dataset would be `hifitts`. |
| `--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 the test set as template testing set.<br>For Hi-Fi TTS dataset, the testing set would be "`test`" split from Hi-Fi TTS during the feature extraction process. |
| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" |
| `--infer_speaker_name` | The target speaker's voice is to be synthesized.<br> (***Note: only applicable to multi-speaker TTS model***) | For Hi-Fi TTS dataset, the list of available speakers includes: "`hifitts_11614`", "`hifitts_11697`", "`hifitts_12787`", "`hifitts_6097`", "`hifitts_6670`", "`hifitts_6671`", "`hifitts_8051`", "`hifitts_9017`", "`hifitts_9136`", "`hifitts_92`". <br> You may find the list of available speakers from `spk2id.json` file generated in ```log_dir/[YourExptName]``` that you have specified in `exp_config.json`. |
### Run
#### Single text inference:
For the single-speaker TTS model, if you want to generate a single clip of speech from a given text, just run:
```bash
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."
```
For the multi-speaker TTS model, in addition to the above-mentioned arguments, you need to add ```infer_speaker_name``` argument, and run:
```bash
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." \
--infer_speaker_name "hifitts_92"
```
#### Batch inference:
For the single-speaker TTS model, if you want to generate speech of all testing sets split from LJSpeech, just run:
```bash
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"
```
For the multi-speaker TTS model, if you want to generate speech of all testing sets split from Hi-Fi TTS, the same procedure follows from above, with ```LJSpeech``` replaced by ```hifitts```.
```bash
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 "hifitts" \
--infer_testing_set "test"
```
We released a pre-trained Amphion VITS model trained on LJSpeech. So, you can download the pre-trained model [here](https://huggingface.co/amphion/vits-ljspeech) and generate speech following the above inference instructions. Meanwhile, the pre-trained multi-speaker VITS model trained on Hi-Fi TTS will be released soon. Stay tuned.
```bibtex
@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},
}
```
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