Text-to-Speech

Setup

Choose a PyTorch container from NVIDIA PyTorch Container Versions and create a Dockerfile as /text2speech/docker/Dockerfile

1. Build and run docker

$ docker build --no-cache -t torcht2s .
$ docker run -it --rm --gpus all -p 2222:8888 -v /your/working/directory/text-to-speech/text2speech:/your/working/directory/text-to-speech/text2speech torcht2s

2. Add environment to jupyter notebook and launch jupyter notebook

$ python -m ipykernel install --user --name=torcht2s
$ jupyter notebook --ip=0.0.0.0 --port=8888 --no-browser --allow-root

3. Open a browser from your local machine and navigate to 127.0.0.1:2222/?token=${TOKEN} and enter your token specified in your terminal.

Data Preperation

Follow these steps to use custom dataset.

1. Prepare a directory with .wav files, filelists (training/validation split of the data) with transcripts and paths to .wav files under the text2speech/Fastpitch/dataset/ location. Those filelists should list a single utterance per line as:

<audio file path>|<transcript>

• Preprocess the data

2. Run the pre-processing script to calculate pitch and mels with text2speech/Fastpitch/data_preperation.ipynb

python prepare_dataset.py \ 
    --wav-text-filelists dataset/tts_data_train.txt \ 
                         dataset/tts_data_val.txt \
    --n-workers 0 \
    --batch-size 1 \
    --dataset-path dataset \
    --extract-pitch \
    --f0-method pyin \
    --extract-mels \

3. Prepare file lists with paths to pre-calculated pitch running create_picth_text_file() from text2speech/Fastpitch/data_preperation.ipynb Those filelists should list a single utterance per line as:

<audio file path>|<audio pitch .pt file path>|<transcript>

The complete dataset has the following structure:

./dataset
├── mels
├── pitch
├── wavs
├── tts_data_train.txt
├── tts_data_val.txt
├── tts_data_pitch_train.txt
├── tts_data_pitch_val.txt