Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

Overview

Torch Time Stretch

Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

View on PyPI / View Documentation

Publish to PyPI Run tests PyPI version Number of downloads from PyPI per month Python version support Code Style: Black

About

This package includes two main features:

  • Time-stretch audio clips quickly using PyTorch (with CUDA support)
  • Calculate efficient time-stretch targets (useful for augmentation, where speed is more important than precise time-stretches)

Also check out torch-pitch-shift, a sister project for pitch-shifting.

Installation

pip install torch-time-stretch

Usage

Example

Check out example.py to see torch-time-stretch in action!

Documentation

See the documentation page for detailed documentation!

Contributing

Please feel free to submit issues or pull requests!

You might also like...
Additional code for Stable-baselines3 to load and upload models from the Hub.

Hugging Face x Stable-baselines3 A library to load and upload Stable-baselines3 models from the Hub. Installation With pip Examples [Todo: add colab t

BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation

BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation This is a demo implementation of BYOL for Audio (BYOL-A), a self-sup

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

JAX: Autograd and XLA Quickstart | Transformations | Install guide | Neural net libraries | Change logs | Reference docs | Code search News: JAX tops

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

JAX: Autograd and XLA Quickstart | Transformations | Install guide | Neural net libraries | Change logs | Reference docs | Code search News: JAX tops

Extending JAX with custom C++ and CUDA code

Extending JAX with custom C++ and CUDA code This repository is meant as a tutorial demonstrating the infrastructure required to provide custom ops in

Several simple examples for popular neural network toolkits calling custom CUDA operators.
Several simple examples for popular neural network toolkits calling custom CUDA operators.

Neural Network CUDA Example Several simple examples for neural network toolkits (PyTorch, TensorFlow, etc.) calling custom CUDA operators. We provide

Picasso: A CUDA-based Library for Deep Learning over 3D Meshes

The Picasso Library is intended for complex real-world applications with large-scale surfaces, while it also performs impressively on the small-scale applications over synthetic shape manifolds. We have upgraded the point cloud modules of SPH3D-GCN from homogeneous to heterogeneous representations, and included the upgraded modules into this latest work as well. We are happy to announce that the work is accepted to IEEE CVPR2021.

Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21)

Learning Structural Edits via Incremental Tree Transformations Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21) 1.

This Repo is the official CUDA implementation of ICCV 2019 Oral paper for CARAFE: Content-Aware ReAssembly of FEatures

Introduction This Repo is the official CUDA implementation of ICCV 2019 Oral paper for CARAFE: Content-Aware ReAssembly of FEatures. @inproceedings{Wa

Comments
  • RuntimeError: The size of tensor a (40264) must match the size of tensor b (173) at non-singleton dimension 1

    RuntimeError: The size of tensor a (40264) must match the size of tensor b (173) at non-singleton dimension 1

    I use same code in https://github.com/KentoNishi/torch-time-stretch/blob/master/example.py but get below error

    (librosa) ➜  torch-time-stretch git:(master) ✗ python example.py 
    Traceback (most recent call last):
      File "/home/jackie/code/github/torch-time-stretch/example.py", line 48, in <module>
        test_time_stretch_2_up()
      File "/home/jackie/code/github/torch-time-stretch/example.py", line 20, in test_time_stretch_2_up
        up = time_stretch(sample, Fraction(1, 2), SAMPLE_RATE)
      File "/home/jackie/code/github/torch-time-stretch/torch_time_stretch/main.py", line 116, in time_stretch
        output = stretcher(output)
      File "/home/jackie/anaconda3/envs/librosa/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
        return forward_call(*input, **kwargs)
      File "/home/jackie/anaconda3/envs/librosa/lib/python3.9/site-packages/torchaudio/transforms/_transforms.py", line 1059, in forward
        return F.phase_vocoder(complex_specgrams, rate, self.phase_advance)
      File "/home/jackie/anaconda3/envs/librosa/lib/python3.9/site-packages/torchaudio/functional/functional.py", line 743, in phase_vocoder
        phase = angle_1 - angle_0 - phase_advance
    RuntimeError: The size of tensor a (40264) must match the size of tensor b (173) at non-singleton dimension 1
    
    opened by Jackiexiao 4
  • Example ratios are reversed.

    Example ratios are reversed.

    Love it, thanks for making this! Tiny thing: In the example test_time_stretch_2_up should use 1/2 as a ratio, not 2/1. test_time_stretch_2_down should use that 2/1 (it's stretching the clip length by 2x).

    opened by hdemmer 1
  • Does it with mono-channel wav files?

    Does it with mono-channel wav files?

    my audio clip is in mono 16khz audio, [ 0 0 0 ... 63 100 127], so it will throw

    ---> 15 down = time_stretch(sample, Fraction(2, 1), SAMPLE_RATE)
         16 wavfile.write(
         17     "./stretched_down_2.wav",
         18     SAMPLE_RATE,
         19     np.swapaxes(down.cpu()[0].numpy(), 0, 0).astype(dtype),
         20 )
    
    File /opt/conda/envs/classify-audio/lib/python3.9/site-packages/torch_time_stretch/main.py:108, in time_stretch(input, stretch, sample_rate, n_fft, hop_length)
        106 if not hop_length:
        107     hop_length = n_fft // 32
    --> 108 batch_size, channels, samples = input.shape
        109 # resampler = T.Resample(sample_rate, int(sample_rate / stretch)).to(input.device)
        110 output = input
    
    ValueError: not enough values to unpack (expected 3, got 2)
    
    opened by ti3x 0
Releases(v1.0.3)
Owner
Kento Nishi
17-year-old programmer at Lynbrook High School, with strong interests in AI/Machine Learning. Open source developer and researcher at the Four Eyes Lab.
Kento Nishi
🌊 Online machine learning in Python

In a nutshell River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition

OnlineML 4k Jan 02, 2023
HeartRate detector with ArduinoandPython - Use Arduino and Python create a heartrate detector.

Syllabus of Contents Syllabus of Contents Introduction Of Project Features Develop With Python code introduction Installation License Developer Contac

1 Jan 05, 2022
Learning What and Where to Draw

###Learning What and Where to Draw Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee This is the code for our NIPS 201

Scott Ellison Reed 337 Nov 18, 2022
Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

1 Jan 23, 2022
TensorFlow Similarity is a python package focused on making similarity learning quick and easy.

TensorFlow Similarity is a python package focused on making similarity learning quick and easy.

912 Jan 08, 2023
The official implementation of Equalization Loss v1 & v2 (CVPR 2020, 2021) based on MMDetection.

The Equalization Losses for Long-tailed Object Detection and Instance Segmentation This repo is official implementation CVPR 2021 paper: Equalization

Jingru Tan 129 Dec 16, 2022
HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation

HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation Official PyTroch implementation of HPRNet. HPRNet: Hierarchical Point Regre

Nermin Samet 53 Dec 04, 2022
A curated (most recent) list of resources for Learning with Noisy Labels

A curated (most recent) list of resources for Learning with Noisy Labels

Jiaheng Wei 321 Jan 09, 2023
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution

DRSAN A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution Karam Park, Jae Woong Soh, and Nam Ik Cho Environments U

4 May 10, 2022
Implementation of CVPR'21: RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction

RfD-Net [Project Page] [Paper] [Video] RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction Yinyu Nie, Ji Hou, Xiaoguang Han, Matthi

Yinyu Nie 162 Jan 06, 2023
Wordplay, an artificial Intelligence based crossword puzzle solver.

Wordplay, AI based crossword puzzle solver A crossword is a word puzzle that usually takes the form of a square or a rectangular grid of white- and bl

Vaibhaw 4 Nov 16, 2022
A foreign language learning aid using a neural network to predict probability of translating foreign words

Langy Langy is a reading-focused foreign language learning aid orientated towards young children. Reading is an activity that every child knows. It is

Shona Lowden 6 Nov 17, 2021
My take on a practical implementation of Linformer for Pytorch.

Linformer Pytorch Implementation A practical implementation of the Linformer paper. This is attention with only linear complexity in n, allowing for v

Peter 349 Dec 25, 2022
Official PyTorch Implementation of Learning Architectures for Binary Networks

Learning Architectures for Binary Networks An Pytorch Implementation of the paper Learning Architectures for Binary Networks (BNAS) (ECCV 2020) If you

Computer Vision Lab. @ GIST 25 Jun 09, 2022
Explanatory Learning: Beyond Empiricism in Neural Networks

Explanatory Learning This is the official repository for "Explanatory Learning: Beyond Empiricism in Neural Networks". Datasets Download the datasets

GLADIA Research Group 10 Dec 06, 2022
Ros2-voiceroid2 - ROS2 wrapper package of VOICEROID2

ros2_voiceroid2 ROS2 wrapper package of VOICEROID2 Windows Only Installation Ins

Nkyoku 1 Jan 23, 2022
3rd Place Solution of the Traffic4Cast Core Challenge @ NeurIPS 2021

3rd Place Solution of Traffic4Cast 2021 Core Challenge This is the code for our solution to the NeurIPS 2021 Traffic4Cast Core Challenge. Paper Our so

7 Jul 25, 2022
This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios"

TinyWeaklyIsolationForest This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised a

2 Mar 21, 2022
Data-depth-inference - Data depth inference with python

Welcome! This readme will guide you through the use of the code in this reposito

Marco 3 Feb 08, 2022
This repo is about implementing different approaches of pose estimation and also is a sub-task of the smart hospital bed project :smile:

Pose-Estimation This repo is a sub-task of the smart hospital bed project which is about implementing the task of pose estimation 😄 Many thanks to th

Max 11 Oct 17, 2022