fairseq/examples/speech_synthesis/docs/vctk_example.md

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# VCTK

[VCTK](https://datashare.ed.ac.uk/handle/10283/3443) is an open English speech corpus. We provide examples
for building [Transformer](https://arxiv.org/abs/1809.08895) models on this dataset.


## Data preparation
Download data, create splits and generate audio manifests with
```bash
python -m examples.speech_synthesis.preprocessing.get_vctk_audio_manifest \
  --output-data-root ${AUDIO_DATA_ROOT} \
  --output-manifest-root ${AUDIO_MANIFEST_ROOT}
```

Then, extract log-Mel spectrograms, generate feature manifest and create data configuration YAML with
```bash
python -m examples.speech_synthesis.preprocessing.get_feature_manifest \
  --audio-manifest-root ${AUDIO_MANIFEST_ROOT} \
  --output-root ${FEATURE_MANIFEST_ROOT} \
  --ipa-vocab --use-g2p
```
where we use phoneme inputs (`--ipa-vocab --use-g2p`) as example.

To denoise audio and trim leading/trailing silence using signal processing based VAD, run
```bash
for SPLIT in dev test train; do
    python -m examples.speech_synthesis.preprocessing.denoise_and_vad_audio \
      --audio-manifest ${AUDIO_MANIFEST_ROOT}/${SPLIT}.audio.tsv \
      --output-dir ${PROCESSED_DATA_ROOT} \
      --denoise --vad --vad-agg-level 3
done
```

## Training
(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#transformer).)

## Inference
(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#inference).)

## Automatic Evaluation
(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#automatic-evaluation).)

## Results

| --arch | Params | Test MCD | Model |
|---|---|---|---|
| tts_transformer | 54M | 3.4 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2/vctk_transformer_phn.tar) |

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