Add MFCC inverse to waveform
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- Dominant language
- Python
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Description
🚀 The feature
recover waveform from MFCC like librosa.feature.inverse.mfcc_to_audio function
Motivation, pitch
I am working a problem about speech synthesis and I use librosa.feature.inverse.mfcc_to_audio to recover waveform from MFCC, but librosa's function cost too much CPU calculation and I want to use torchaudio and GPU to accelerate it.
And if there is a function can recover waveform from MFCC, I can use it in my deep learning training and calculate synthesis loss from deep learning model.
Alternatives
librosa.feature.inverse.mfcc_to_audio to recover waveform from MFCC
but it cost CPU and can not use in deep learning training
Additional context
No response
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by comparing the requested behavior with librosa.feature.inverse.mfcc_to_audio and review the repository's existing audio transforms and GPU-compatible signal-processing entry points. Done means providing an MFCC-to-waveform operation usable with PyTorch tensors and GPU-backed training, with behavior validated against the librosa alternative; the issue names no repository files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- audio-video-rtc, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100