Skip to main content

YAAPT Pitch Tracking function in PyTorch

Project description

yaapt.torch

Implementation of the YAAPT pitch tracker in PyTorch.
This version relies solely on PyTorch, does not hog all CPU threads (making it possible to use in a dataloader), is torch.jit-able, and is batch compatible.

Setup

For using (no development required)

pip install yaapt.torch

To install for development, clone the repository, and then run the following from within the root directory.

pip install -e .

Usage

import torchaudio, yaapt_torch
audio, sr = torchaudio.load("audio.wav")

yaapt_opts = {
    "sr": sr,
    "frame_length": 35.0,
    "frame_space": 20.0,
    "nccf_thresh1": 0.25,
    "tda_frame_length": 25.0,
}

pitch = yaapt_torch.yaapt(
    audio, # (Batch, feature) torch.tensor input
    yaapt_opts,
)
print(pitch) # (Batch, F0) torch.tensor output

Credits

This repository aims to wrap up these implementations in easy-installable PyPi packages, which can be used directly in PyTorch based neural network training.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

yaapt.torch-1.0.0.tar.gz (15.9 kB view details)

Uploaded Source

File details

Details for the file yaapt.torch-1.0.0.tar.gz.

File metadata

  • Download URL: yaapt.torch-1.0.0.tar.gz
  • Upload date:
  • Size: 15.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.8.10

File hashes

Hashes for yaapt.torch-1.0.0.tar.gz
Algorithm Hash digest
SHA256 59e7770ac304f862916a88e0a462873d49b3ae404b774f5bf77e84debfcf6af0
MD5 d5657c1916a99a8f00d55c03db5f42c9
BLAKE2b-256 8450be9d5e6f2e1b61e9ca835d7fe7fb1f5f3f4925b2c16c66d903ce39ec8680

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page