# VITS Recipe [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Spaces-yellow)](https://huggingface.co/spaces/amphion/Text-to-Speech) [![openxlab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](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`.
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.
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.
(***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`".
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}, } ```