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
AOT-GAN for High-Resolution Image Inpainting (codebase for image inpainting)

AOT-GAN for High-Resolution Image Inpainting Arxiv Paper | AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image Inpainting Yanhong

Multimedia Research 214 Jan 03, 2023
SPT_LSA_ViT - Implementation for Visual Transformer for Small-size Datasets

Vision Transformer for Small-Size Datasets Seung Hoon Lee and Seunghyun Lee and Byung Cheol Song | Paper Inha University Abstract Recently, the Vision

Lee SeungHoon 87 Jan 01, 2023
PyTorch implementation of Decoupling Value and Policy for Generalization in Reinforcement Learning

PyTorch implementation of Decoupling Value and Policy for Generalization in Reinforcement Learning

48 Dec 08, 2022
A set of examples around hub for creating and processing datasets

Examples for Hub - Dataset Format for AI A repository showcasing examples of using Hub Uploading Dataset Places365 Colab Tutorials Notebook Link Getti

Activeloop 11 Dec 14, 2022
Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility ICCV2021

Vis2Mesh This is the offical repository of the paper: Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Lear

71 Dec 25, 2022
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning Preprocess file of the dataset used in implicit sub-populations: (Demographic groups

<a href=[email protected]"> 4 Oct 14, 2022
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

880 Jan 07, 2023
This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-grained Classification".

HA-in-Fine-Grained-Classification This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-g

16 Oct 29, 2022
StarGAN v2 - Official PyTorch Implementation (CVPR 2020)

StarGAN v2 - Official PyTorch Implementation StarGAN v2: Diverse Image Synthesis for Multiple Domains Yunjey Choi*, Youngjung Uh*, Jaejun Yoo*, Jung-W

Clova AI Research 3.1k Jan 09, 2023
Node Dependent Local Smoothing for Scalable Graph Learning

Node Dependent Local Smoothing for Scalable Graph Learning Requirements Environments: Xeon Gold 5120 (CPU), 384GB(RAM), TITAN RTX (GPU), Ubuntu 16.04

Wentao Zhang 15 Nov 28, 2022
Spatial Action Maps for Mobile Manipulation (RSS 2020)

spatial-action-maps Update: Please see our new spatial-intention-maps repository, which extends this work to multi-agent settings. It contains many ne

Jimmy Wu 27 Nov 30, 2022
Simple API for UCI Machine Learning Dataset Repository (search, download, analyze)

A simple API for working with University of California, Irvine (UCI) Machine Learning (ML) repository Table of Contents Introduction About Page of the

Tirthajyoti Sarkar 223 Dec 05, 2022
PyTorch for Semantic Segmentation

PyTorch for Semantic Segmentation This repository contains some models for semantic segmentation and the pipeline of training and testing models, impl

Zijun Deng 1.7k Jan 06, 2023
Image Segmentation with U-Net Algorithm on Carvana Dataset using AWS Sagemaker

Image Segmentation with U-Net Algorithm on Carvana Dataset using AWS Sagemaker This is a full project of image segmentation using the model built with

Htin Aung Lu 1 Jan 04, 2022
This repository contains the code and models for the following paper.

DC-ShadowNet Introduction This is an implementation of the following paper DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised

AuAgCu 65 Dec 27, 2022
Teaches a student network from the knowledge obtained via training of a larger teacher network

Distilling-the-knowledge-in-neural-network Teaches a student network from the knowledge obtained via training of a larger teacher network This is an i

Abhishek Sinha 146 Dec 11, 2022
CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable.

CausalNLP CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable. Install pip install -U

Arun S. Maiya 95 Jan 03, 2023
Official Pytorch Implementation of 3DV2021 paper: SAFA: Structure Aware Face Animation.

SAFA: Structure Aware Face Animation (3DV2021) Official Pytorch Implementation of 3DV2021 paper: SAFA: Structure Aware Face Animation. Getting Started

QiulinW 122 Dec 23, 2022
Answer a series of contextually-dependent questions like they may occur in natural human-to-human conversations.

SCAI-QReCC-21 [leaderboards] [registration] [forum] [contact] [SCAI] Answer a series of contextually-dependent questions like they may occur in natura

19 Sep 28, 2022
A diff tool for language models

LMdiff Qualitative comparison of large language models. Demo & Paper: http://lmdiff.net LMdiff is a MIT-IBM Watson AI Lab collaboration between: Hendr

Hendrik Strobelt 27 Dec 29, 2022