whisperer

Go from raw audio files to a speaker separated text-audio datasets automatically.

Table of Contents

• Summary
• Key Features
• Instalation
• How to use:
  • Using Multiple-GPUS
  • Configuration
• To Do
• Acknowledgements

Summary

This repo takes a directory of audio files and converts them to a text-audio dataset with normalized distribution of audio lengths. See AnalyzeDataset.ipynb for examples of the dataset distributions across audio and text length

The output is a text-audio dataset that can be used for training a speech-to-text model or text-to-speech. The dataset structure is as follows:

│── /dataset
│   ├── metadata.txt
│   └── wavs/
│      ├── audio1.wav
│      └── audio2.wav

metadata.txt

peters_0.wav|Beautiful is better than ugly.
peters_1.wav|Explicit is better than implicit.

Key Features

• Audio files are automatically split by speakers
• Speakers are auto-labeled across the files
• Audio splits on silences
• Audio splitting is configurable
• The dataset creation is done so that it follows Gaussian-like distributions on clip length. Which, in turn, can lead to Gaussian-like distributions on the rest of the dataset statistics. Of course, this is highly dependent on your audio sources.
• Leverages the GPUs available on your machine. GPUs also be set explicitly if you only want to use some.

Instalation

You have two options

1. Install from PyPi with pip

pip install whisperer-ml

2. User Friendly WebApp Whisperer Web

Take a look at the Demo on your browser.

Note: Under Development but ready to be used

How to use:

1. Create data folder and move audio files to it

mkdir data data/raw_files

2. There are four commands

   1. Convert

      whisperer_ml convert path/to/data/raw_files
      

   2. Diarize

      whisperer_ml diarize path/to/data/raw_files
      

   3. Auto-Label

      whisperer_ml auto-label path/to/data/raw_files number_speakers
      

   4. Transcribe

      whisperer_ml transcribe path/to/data/raw_files your_dataset_name
      

   5. Help lists all commands

      whisperer_ml --help 
      

   6. You can run help on a specific command

      whisperer_ml convert --help
   

3. Use the AnalyseDataset.ipynb notebook to visualize the distribution of the dataset

4. Use the AnalyseSilence.ipynb notebook to experiment with silence detection configuration

Using Multiple-GPUS

The code automatically detects how many GPU's are available and distributes the audio files in data/wav_files evenly across the GPUs. The automatic detection is done through nvidia-smi.

You can to make the available GPU's explicit by setting the environment variable CUDA_AVAILABLE_DEVICES.

Configuration

Modify config.py file to change the parameters of the dataset creation. Including silence detection.

To Do

• Speech Diarization
• Replace click with typer

Acknowledgements

• AnalyseDataset.ipynb adapted from coqui-ai example
• OpenAI Whisper
• PyAnnote
• SpeechBrain