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A PyTorch implementation of "WaveFlow: A Compact Flow-based Model for Raw Audio"

WaveFlow : A Compact Flow-based Model for Raw Audio This is an unofficial PyTorch implementation of a paper "WaveFlow : A Compact Flow-based Model for Raw Audio". Currently WIP. The implementation details may not be faithful.
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Category: Python / Deep Learning
Watchers: 9
Star: 112
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Last update: Aug 4, 2022
Resource links
https://github.com/L0SG/WaveFlow

WaveFlow : A Compact Flow-based Model for Raw Audio

This is an unofficial PyTorch implementation of a paper "WaveFlow : A Compact Flow-based Model for Raw Audio".

Currently WIP. The implementation details may not be faithful.

Requirements

PyTorch 1.1.0 or later (tested on 1.3.0) & python 3.6 & Librosa

Examples

Step 1. Download Dataset

Step 2. Preprocessing (Preparing Mel Spectrogram)

python preprocessing.py --in_dir /path/to/ljspeech/data/root --out_dir ./ljspeech_data

Step 3. Train the Model

python train.py --model_name waveflow_h8_r64 --n_height 8 --res_channels 64 --n_layer_per_cycle 1

python train.py --model_name waveflow_h64_r64 --n_height 64 --res_channels 64 --n_layer_per_cycle 5

python train.py --model_name waveflow_h32_r128 --n_height 32 --res_channels 128 --n_layer_per_cycle 3

Step 4. Synthesize

Specify --load_step and --num_samples that looks like:

python synthesize.py --model_name waveflow_h8_r64 --n_height 8 --res_channels 64 --n_layer_per_cycle 1 --load_step 100000 --num_samples 5

References

0 Open More issues
Closed

The causality on the height dimension ?

It seems that the shift_1d is called to guarantee the causality on the height dimension. But the receptive field of the following Wavenet2D module is greater than 3 (8 layers, each has kernel size of 3). Then how dose the shift_1d function which only shifts the tensor on height dimension by one unit guarantee the causality?

opened Feb 13, 2020 by JesseYang 2
Closed

Released model and configurations

Hi, I listened the case your shared, very expressive. Can you share your models and configurations? I want to train a model using your configurations recently, and take the model as reference. Thanks very much

opened Feb 13, 2020 by scimagian 1
Closed

online speech syntensize and server code

Hi can share your test waves? are they like https://waveflow-demo.github.io/? another question, Is there any server synthesizer? Do you compare of time of synthesizing with tacatron (like Mozilla)? Is it faster? best regards.

opened Dec 4, 2019 by lalimili6 1
Closed

Bump numpy from 1.13.3 to 1.22.0 in /tacotron2_custom

Bumps numpy from 1.13.3 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

  • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
  • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
  • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
  • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
  • A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

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opened Jun 21, 2022 by dependabot[bot] 0

Contents

    omerbsezer Pytorch Tutorial, Pytorch with Google Colab, Pytorch Implementations: CNN, RNN, DCGAN, Transfer Learning, Chatbot, Pytorch Sample Codes
    leehomyc A PyTorch implementation of the paper Mixup: Beyond Empirical Risk Minimization in PyTorch
    duc0 Pytorch implementation of the DeepDream computer vision algorithm
    Eurus-Holmes :crystal_ball: Life is short, you need PyTorch.
    Angel-ML PyTorch On Angel, arming PyTorch with a powerful Parameter Server, which enable PyTorch to train very big models.
    veritas9872 Template repository to build PyTorch projects from source on any version of PyTorch/CUDA/cuDNN.
    kpoeppel A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch
    jwyang Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection
    zkisthebest An implementation for Siamese Region Proposal Network with PyTorch
    hjmshi A PyTorch implementation of L-BFGS.
    lufficc High quality, fast, modular reference implementation of SSD in PyTorch
    romulus0914 A PyTorch Implementation of MixNet
    ClementPinard Pytorch implementation of FlowNet by Dosovitskiy et al.
    ikostrikov PyTorch implementation of Trust Region Policy Optimization
    hanzhanggit Pytorch implementation for reproducing StackGAN_v2
    wmvanvliet Implementation of the HMAX model of vision in PyTorch
    chingyaoc PyTorch Implementation of REINFORCE for both discrete & continuous control
    corenel A PyTorch implementation for Adversarial Discriminative Domain Adaptation
    cedrickchee [NO MAINTENANCE INTENDED] A PyTorch implementation of CapsNet architecture in the NIPS 2017 paper "Dynamic Routing Between Capsules".
    jwyang A faster pytorch implementation of faster r-cnn
    xiadingZ pytorch implementation of video captioning
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