FFmpeg, cuda, cudnn

git clone https://github.com/TotzkePaul/TTSDatasetCreator/

cd ./TTSDatasetCreator

For reference: conda env create -f ./win10_conda_env/transcribe.yml

I recommend just going through the requirements.txt and install each of those individually. Tested with python=3.6

Create Workspace, Models, DeepSpeechModels and Dataset Folders

python ./TTSDatasetCreator/init.py

Go to https://github.com/mozilla/DeepSpeech/releases/tag/v0.8.2 and deepspeech-0.8.2-models.pbmm and deepspeech-0.8.2-models.scorer to DeepSpeechModels

Pick a Workspace name (eg Rogan) and save source audio to ../Workspace/{WorkspaceName}/src

split.py uses pydub to split files into 15 minute wav files. 16000hz for deepspeech and 22050 for Tacotron

python split.py --name={WorkspaceName}

For multiple speakers, go to https://gong-io.github.io/gecko/ and open a wav file from ../Workspace/$Name/wavs

Segment 10 5-second clips for each speaker and label the speaker (Left hand side - Segment Labeling).

Hit save and save to ../Workspace/$Name/speakers as same_filename.wav.speaker.json

python speaker_split.py --output speakers --name Rogan --min 3000 --max 8000

This will create subfolders under ../Workspace/{$Name}/speakers with the names of the speaker labels.

Those folders contain 3-second to 8-second clips of the speaker. These will be used to auto-tag other files.

The more clips, the longer the next step runs but (ideally) more accurate segmentation labeling.

python segment.py --name $Name

This will create same_filename.wav.segment.json for each wav in ../Workspace/$Name/wavs

You can review and edit this file in gecko

python transcribe.py --model ../DeepSpeechModels/deepspeech-0.8.2-models.pbmm --scorer ../DeepSpeechModels/deepspeech-0.8.2-models.scorer --name={WorkspaceName}

This will create same_filename.wav.transcript.json for each wav in ../Workspace/$Name/wavs

Have approximate transcripts? Save txt files to ../Workspace/$Name/hot_words as audiofile1.wav.txt or audiofile1.txt

Currently, this just extracts all unique words and boosts them by the same amount.

If you see warnings like missing dll, either update tensorflow or use the following:

conda install -c anaconda cudatoolkit=10.1

conda install -c anaconda cudnn

Review corresponding wav and transcript files using gecko.

To override the auto generated transcript, save the gecko save file as same_filename.wav.transcript.json in ../Workspace/$Name/gecko

python gecko_split.py --output ../Datasets --name {$Name} --min 0 --max 12000 --speaker "Joe Rogan"

--speaker is optional. It filters the clips where gecko's speaker label is the name.

This creates a subfolder in ../Datasets/ and creates a tacotron, waveglow and flowtron compatibile dataset.

The complete dataset for clips 0 to 12 seconds now exists in ./Datasets/{WorkspaceName}

{WorkspaceName}_wg.txt is for waveglow;

{WorkspaceName}_train.txt and {WorkspaceName}_val.txt are for tacotron2

{WorkspaceName}_flowtron_train.txt and {WorkspaceName}_flowtron_val.txt are for flowtron and mellowtron