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VCTK

VCTK is an open English speech corpus. We provide examples for building Transformer models on this dataset.

Data preparation

Download data, create splits and generate audio manifests with

python -m examples.speech_synthesis.preprocessing.get_vctk_audio_manifest \
  --output-data-root ${AUDIO_DATA_ROOT} \
  --output-manifest-root ${AUDIO_MANIFEST_ROOT}

To denoise audio and trim leading/trailing silence using signal processing based VAD, run

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

which generates a new audio TSV manifest under ${PROCESSED_DATA_ROOT} with updated path to the processed audio and a new column for SNR.

To do filtering by CER, follow the Automatic Evaluation section to run ASR model (add --eval-target to get_eval_manifest for evaluation on the reference audio; add --err-unit char to eval_asr to compute CER instead of WER). The example-level CER is saved to ${EVAL_OUTPUT_ROOT}/uer_cer.${SPLIT}.tsv.

Then, extract log-Mel spectrograms, generate feature manifest and create data configuration YAML with

python -m examples.speech_synthesis.preprocessing.get_feature_manifest \
  --audio-manifest-root ${PROCESSED_DATA_ROOT} \
  --output-root ${FEATURE_MANIFEST_ROOT} \
  --ipa-vocab --use-g2p \
  --snr-threshold 15 \
  --cer-threshold 0.1 --cer-tsv-path ${EVAL_OUTPUT_ROOT}/uer_cer.${SPLIT}.tsv

where we use phoneme inputs (--ipa-vocab --use-g2p) as example. For sample filtering, we set the SNR and CER threshold to 15 and 10%, respectively.

Training

(Please refer to the LJSpeech example.)

Inference

(Please refer to the LJSpeech example.)

Automatic Evaluation

(Please refer to the LJSpeech example.)

Results

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