HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement

Overview

HiFi++ : a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement

This is the unofficial implementation of Vocoder part of HiFi++ : a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement.

  • Currently, this repo is WIP but you can start your training without any error.

Training:

python train.py --config config_v2.json

Citations:

@misc{https://doi.org/10.48550/arxiv.2203.13086,
  doi = {10.48550/ARXIV.2203.13086},
  
  url = {https://arxiv.org/abs/2203.13086},
  
  author = {Andreev, Pavel and Alanov, Aibek and Ivanov, Oleg and Vetrov, Dmitry},
  
  keywords = {Sound (cs.SD), Machine Learning (cs.LG), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
  
  title = {HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

References:

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Comments
  • may be a mistake in MRF?

    may be a mistake in MRF?

    it seems the paper use a serial ResBlock(kernel size=3,7,11) but the coder use parallel ResBlock image

    https://github.com/rishikksh20/HiFiplusplus-pytorch/blob/8be0d0e0092d4f609c37bfbeede5a9ad9bd7470a/models.py#L120

    I think this is the key of high quality, but the RTF will be much bigger than melgan and hifigan v2. MAC is not a performance indicator.

    opened by azraelkuan 4
  • Any idea about the discriminator architecture?

    Any idea about the discriminator architecture?

    Hi, amazing work! I'm also trying to implement HIFI++ but I can't find any info regarding the discriminator architecture. In the paper they claim to have designed a much smaller MSD discriminators. Do you have any idea?

    Further, we demonstrate that we can substitude MSD discriminators that operate on different input resolutions for identical much smaller discriminators that process the waveform on the initial resolution.

    opened by gnsrla12 3
  • Bump numpy from 1.17.4 to 1.21.0

    Bump numpy from 1.17.4 to 1.21.0

    Bumps numpy from 1.17.4 to 1.21.0.

    Release notes

    Sourced from numpy's releases.

    v1.21.0

    NumPy 1.21.0 Release Notes

    The NumPy 1.21.0 release highlights are

    • continued SIMD work covering more functions and platforms,
    • initial work on the new dtype infrastructure and casting,
    • universal2 wheels for Python 3.8 and Python 3.9 on Mac,
    • improved documentation,
    • improved annotations,
    • new PCG64DXSM bitgenerator for random numbers.

    In addition there are the usual large number of bug fixes and other improvements.

    The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

    :warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

    • Optimization level -O3 results in many wrong warnings when running the tests.
    • On some hardware NumPy will hang in an infinite loop.

    New functions

    Add PCG64DXSM BitGenerator

    Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

    See upgrading-pcg64 for more details.

    (gh-18906)

    Expired deprecations

    • The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

    ... (truncated)

    Commits
    • b235f9e Merge pull request #19283 from charris/prepare-1.21.0-release
    • 34aebc2 MAINT: Update 1.21.0-notes.rst
    • 493b64b MAINT: Update 1.21.0-changelog.rst
    • 07d7e72 MAINT: Remove accidentally created directory.
    • 032fca5 Merge pull request #19280 from charris/backport-19277
    • 7d25b81 BUG: Fix refcount leak in ResultType
    • fa5754e BUG: Add missing DECREF in new path
    • 61127bb Merge pull request #19268 from charris/backport-19264
    • 143d45f Merge pull request #19269 from charris/backport-19228
    • d80e473 BUG: Removed typing for == and != in dtypes
    • Additional commits viewable in compare view

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  • Single frequency lines in Spectrogram of generated wavs

    Single frequency lines in Spectrogram of generated wavs

    Hi, I traned a model for the Bandwidth expansion task using the code and VCTK dataset. But I got bad generated wavs, which contain Single frequency lines in Spectrogram , as shown in the figure 截屏2022-11-01 18 56 34

    opened by williamsz 0
Owner
Rishikesh (ऋषिकेश)
Deep Learning/ AI Researcher | Open Source enthusiast | Text to Speech | Speech Synthesis | Generative Models | Object detection | Computer Vision
Rishikesh (ऋषिकेश)
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