Audio pipeline for TTS Datasets

This project aims to provide high level APIs for various feature engineering techniques for processing audio files. The implementation follows a modularized and config based approach, so any dataset processing pipeline can be built and managed using the same.

Creating new pipeline

Creat an yaml file under config/pipelines/ directory with the following structure

pipeline:
  loader:
    target: manager.Downloader
    args:
      configs:
        - config/datasets/data-config.yaml
      save_dir: raw_data/yt_data
  manager:
    target: manager.YoutubeRunner
  processors:
    - name: chunking
      target: modules.AudioChunking
      args:
        model_choice: pydub_chunking
    - name: denoise_audio
      target: modules.DenoiseAudio
      args:
        model_choice: meta_denoiser_dns48
    - name: audio_superres
      target: modules.SuperResAudio
      args:
        model_choice: voicefixer
    ...

Pipeline Schema

• Loader: The entry point for fetching data from various sources like S3, local systems, or blob storage.
• Manager: Specifies the manager class responsible for running the pipeline.
• Processors: An ordered list of processors to apply for feature extraction or other manipulations.

If new feature extractors or manager are required for your needs, check the modules/ directory for understanding the structure and create or update the objects as needed.

Run pipleines

python workers/pipeline.py --configs <space separated path to config(s)> 

Acknowledgements

credit a few of the amazing folks in the community that have helped to this happen:

• bud-studio - For providing a initial framework