Everything is done from main repo folder so TensorflowTTS/

0. Optional* Download and prepare libritts (helper to prepare libri in examples/fastspeech2_libritts/libri_experiment/prepare_libri.ipynb)

• Dataset structure after finish this step:

  |- TensorFlowTTS/
  |   |- LibriTTS/
  |   |-  |- train-clean-100/
  |   |-  |- SPEAKERS.txt
  |   |-  |- ...
  |   |- libritts/
  |   |-  |- 200/
  |   |-  |-  |- 200_124139_000001_000000.txt
  |   |-  |-  |- 200_124139_000001_000000.wav
  |   |-  |-  |- ...
  |   |-  |- 250/
  |   |-  |- ...
  |   |- tensorflow_tts/
  |       |- models/
  |       |- ...
  

1. Extract Duration (use examples/mfa_extraction or pretrained tacotron2)
2. Optional* build docker

• bash examples/fastspeech2_libritts/scripts/build.sh
  

3. Optional* run docker

• bash examples/fastspeech2_libritts/scripts/interactive.sh
  

4. Preprocessing:

• tensorflow-tts-preprocess --rootdir ./libritts \
    --outdir ./dump_libritts \
    --config preprocess/libritts_preprocess.yaml \
    --dataset libritts
  

5. Normalization:

• tensorflow-tts-normalize --rootdir ./dump_libritts \
    --outdir ./dump_libritts \
    --config preprocess/libritts_preprocess.yaml \
    --dataset libritts
  

6. Change CharactorDurationF0EnergyMelDataset speaker mapper in fastspeech2_dataset to match your dataset (if you use libri with mfa_extraction you didnt need to change anything)
7. Change train_libri.sh to match your dataset and run:

• bash examples/fastspeech2_libritts/scripts/train_libri.sh
  

8. Optional* If u have problems with tensor sizes mismatch check step 5 in examples/mfa_extraction directory

This version is using popular train.txt '|' split used in other repos. Training files should looks like this =>

Wav Path | Text | Speaker Name

Wav Path2 | Text | Speaker Name