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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}, | |
| } | |
| ``` | |