npvcc2016: Python loader of npVCC2016 speech corpus
Project description
npvcc2016 - Python loader of npVCC2016Corpus
npvcc2016
is a Python package for loader of npVCC2016 non-parallel speech corpus.
For machine learning, corpus/dataset is indispensable - but troublesome - part.
We need portable & flexible loader for streamline development.
npvcc2016
is the one!!
Demo
Python/PyTorch
pip install npvcc2016
from npvcc2016.PyTorch.dataset.waveform import NpVCC2016_wave
dataset = NpVCC2016(train=True, download=True)
for datum in dataset:
print("Yeah, data is acquired with only two line of code!!")
print(datum) # (datum, label) tuple provided
npvcc2016
transparently downloads corpus, structures the data and provides standarized datasets.
What you have to do is only instantiating the class!
APIs
Current npvcc2016
support PyTorch.
As interface, PyTorch's Dataset
and PyTorch-Lightning's DataModule
are provided.
npVCC2016 corpus is speech corpus, so we provide waveform
dataset and spectrogram
dataset for both interfaces.
- PyTorch
- (pure PyTorch) dataset
- waveform:
NpVCC2016_wave
- spectrogram:
NpVCC2016_spec
- waveform:
- PyTorch-Lightning
- waveform:
NpVCC2016_wave_DataModule
- spectrogram:
NpVCC2016_spec_DataModule
- waveform:
- (pure PyTorch) dataset
Dependency Notes
PyTorch version
PyTorch version: PyTorch v1.6 is working (We checked with v1.6.0).
For dependency resolution, we do NOT explicitly specify the compatible versions.
PyTorch have several distributions for various environment (e.g. compatible CUDA version.)
Unfortunately it make dependency version management complicated for dependency management system.
In our case, the system poetry
cannot handle cuda variant string (e.g. torch>=1.6.0
cannot accept 1.6.0+cu101
.)
In order to resolve this problem, we use torch==*
, it is equal to no version specification.
Setup.py
could resolve this problem (e.g. torchaudio
's setup.py
), but we will not bet our effort to this hacky method.
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