Tidy Tunes

Tidy Tunes is an easy-to-use pipeline for mining high-quality audio data for speech generation models. To do so, it chains multiple open source models while minimizing dependencies.

Specifically, the pipeline includes (see also https://www.arxiv.org/pdf/2409.03283):

• voice source segmentation
• segmentation based on voice activity detection
• speaker segmentation
• rolloff frequency filtering
• filtering based on PESQ of denoised audio
• DNSMOS filtering
• spoken language identification filtering

It provides two commands, one for downloading audios from sources like YouTube, and one for processing the downloaded audio efficiently.

Installation

You can install Tidy Tunes directly from the GitHub repository using pip:

pip install git+https://github.com/meaningTeam/tidy-tunes.git

If you wish, clone the repository or install additional dependencies:

git clone [email protected]:meaningTeam/tidy-tunes.git && cd tidy-tunes
pip install -e .[dev]

Usage

The CLI offers two commands, one for downloading audio from videos from the internet (YouTube), the other one for processing audio files with a stack of data-cleaning models.

To download the audios, use:

tidytunes download-youtube [OPTIONS] [VIDEO_IDS]...

Options:
  -d, --download_dir PATH       Ouput directory
  -k, --proxy-api-key TEXT      API key for the proxy server.  [required]
  -e, --proxy-endpoint TEXT     Proxy endpoint address.
  -w, --max-workers INTEGER     Number of parallel download threads.
  -l, --subtitle-language TEXT  Language code for substitle language.

You can place your secret proxy API_KEY into ~/.tidytunes and it will be auto-discovered by the script, for example:

API_KEY=Wk1yM8sY9M4S7rT3g5Xq2L6bV0NfJHp

To process the downloaded audios, use:

tidytunes process-audios [OPTIONS] [AUDIO_PATHS]...

Options:
  -c, --config PATH               Path to the YAML config file defining pipeline components and parameters.  [required]
  -o, --out PATH                  Destination directory to save processed
                                  files.  [required]
  -d, --device [cpu|cuda]         Device on which to run the pipeline.
  -w, --overwrite                 Overwrite processed files.

You can find the default config at configs/full_en.yaml. If needed, modify or disable the pipeline components and their parameters.

Using in Python

Tidy Tunes can also be used directly in Python. Example usage:

from tidytunes.utils import Audio, trim_audios, partition
from tidytunes.pipeline_components import find_segments_with_single_speaker, get_dnsmos

device = "cuda"
path = "path/to/my_audio.flac"
output_dir = "processed/"

audio_segments = [Audio.from_file(path)]

timestamps = find_segments_with_single_speaker(audio_segments, min_duration=3.2, device=device)
audio_segments = trim_audios(audio_segments, timestamps)

mos = get_dnsmos(audio_segments, device=device)
audio_segments, poor_quality_audio_segments = partition(audio_segments, by=mos > 3.3)

for segment in zipaudio_segments:
    segment.play()
    segment.to_file(output_dir)

Citation

If used, please cite it as follows:

@misc{tidytunes,
  author = {Tomas Nekvinda and Jan Vainer and Vojtech Srdecny},
  title = {Tidy Tunes: An Easy-to-Use Pipeline for Mining High-Quality Audio Data},
  year = {2025},
  howpublished = {\url{https://github.com/meaningTeam/tidy-tunes}},
  note = {Version 1.0.1}
}

License

Tidy Tunes is released under the MIT License. See LICENSE for details.