diffsptk
========
*diffsptk* is a differentiable version of [SPTK](https://github.com/sp-nitech/SPTK) based on the PyTorch framework.
[](https://sp-nitech.github.io/diffsptk/latest/)
[](https://sp-nitech.github.io/diffsptk/1.2.1/)
[](https://pepy.tech/project/diffsptk)
[](https://pypi.python.org/pypi/diffsptk)
[](https://pypi.python.org/pypi/diffsptk)
[](https://pypi.python.org/pypi/diffsptk)
[](https://app.codecov.io/gh/sp-nitech/diffsptk)
[](https://github.com/sp-nitech/diffsptk/blob/master/LICENSE)
[](https://github.com/sp-nitech/diffsptk/actions)
[](https://github.com/psf/black)
Requirements
------------
- Python 3.8+
- PyTorch 1.11.0+
Documentation
-------------
- See [this page](https://sp-nitech.github.io/diffsptk/latest/) for a reference manual.
- Our [paper](https://www.isca-speech.org/archive/ssw_2023/yoshimura23_ssw.html) is available on the ISCA Archive.
Installation
------------
The latest stable release can be installed through PyPI by running
```sh
pip install diffsptk
```
The development release can be installed from the master branch:
```sh
pip install git+https://github.com/sp-nitech/diffsptk.git@master
```
Examples
--------
### Mel-cepstral analysis and synthesis
```python
import diffsptk
# Set analysis condition.
fl = 400
fp = 80
n_fft = 512
M = 24
# Read waveform.
x, sr = diffsptk.read("assets/data.wav")
# Compute STFT amplitude of x.
stft = diffsptk.STFT(frame_length=fl, frame_period=fp, fft_length=n_fft)
X = stft(x)
# Estimate mel-cepstrum of x.
alpha = diffsptk.get_alpha(sr)
mcep = diffsptk.MelCepstralAnalysis(cep_order=M, fft_length=n_fft, alpha=alpha, n_iter=10)
mc = mcep(X)
# Reconstruct x.
mlsa = diffsptk.MLSA(filter_order=M, frame_period=fp, alpha=alpha, taylor_order=30)
x_hat = mlsa(mlsa(x, -mc), mc)
# Write reconstructed waveform.
diffsptk.write("reconst.wav", x_hat, sr)
# Compute error.
error = (x_hat - x).abs().sum()
print(error)
# Extract pitch of x.
pitch = diffsptk.Pitch(frame_period=fp, sample_rate=sr, f_min=80, f_max=180)
p = pitch(x)
# Generate excitation signal.
excite = diffsptk.ExcitationGeneration(frame_period=fp)
e = excite(p)
n = diffsptk.nrand(x.size(0) - 1)
# Synthesize waveform.
x_voiced = mlsa(e, mc)
x_unvoiced = mlsa(n, mc)
# Output analysis-synthesis result.
diffsptk.write("voiced.wav", x_voiced, sr)
diffsptk.write("unvoiced.wav", x_unvoiced, sr)
```
### Mel-spectrogram, MFCC, and PLP extraction
```python
import diffsptk
# Set analysis condition.
fl = 400
fp = 80
n_fft = 512
n_channel = 80
M = 12
# Read waveform.
x, sr = diffsptk.read("assets/data.wav")
# Compute STFT amplitude of x.
stft = diffsptk.STFT(frame_length=fl, frame_period=fp, fft_length=n_fft)
X = stft(x)
# Extract mel-spectrogram.
fbank = diffsptk.MelFilterBankAnalysis(
n_channel=n_channel,
fft_length=n_fft,
sample_rate=sr,
)
Y = fbank(X)
print(Y.shape)
# Extract MFCC.
mfcc = diffsptk.MFCC(
mfcc_order=M,
n_channel=n_channel,
fft_length=n_fft,
sample_rate=sr,
)
Y = mfcc(X)
print(Y.shape)
# Extract PLP.
plp = diffsptk.PLP(
plp_order=M,
n_channel=n_channel,
fft_length=n_fft,
sample_rate=sr,
)
Y = plp(X)
print(Y.shape)
```
### Subband decomposition
```python
import diffsptk
K = 4 # Number of subbands.
M = 40 # Order of filter.
# Read waveform.
x, sr = diffsptk.read("assets/data.wav")
# Decompose x.
pqmf = diffsptk.PQMF(K, M)
decimate = diffsptk.Decimation(K)
y = decimate(pqmf(x), dim=-1)
# Reconstruct x.
interpolate = diffsptk.Interpolation(K)
ipqmf = diffsptk.IPQMF(K, M)
x_hat = ipqmf(interpolate(K * y, dim=-1)).reshape(-1)
# Write reconstructed waveform.
diffsptk.write("reconst.wav", x_hat, sr)
# Compute error.
error = (x_hat - x).abs().sum()
print(error)
```
### Vector quantization
```python
import diffsptk
K = 2 # Codebook size.
M = 4 # Order of vector.
# Prepare input.
x = diffsptk.nrand(M)
# Quantize x.
vq = diffsptk.VectorQuantization(M, K)
x_hat, indices, commitment_loss = vq(x)
# Compute error.
error = (x_hat - x).abs().sum()
print(error)
```
License
-------
This software is released under the Apache License 2.0.
Reference
---------
```bibtex
@InProceedings{sp-nitech2023sptk,
author = {Takenori Yoshimura and Takato Fujimoto and Keiichiro Oura and Keiichi Tokuda},
title = {{SPTK4}: An open-source software toolkit for speech signal processing},
booktitle = {12th ISCASpeech Synthesis Workshop (SSW 2023)},
pages = {211--217},
year = {2023},
}
```
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"description": "diffsptk\n========\n*diffsptk* is a differentiable version of [SPTK](https://github.com/sp-nitech/SPTK) based on the PyTorch framework.\n\n[](https://sp-nitech.github.io/diffsptk/latest/)\n[](https://sp-nitech.github.io/diffsptk/1.2.1/)\n[](https://pepy.tech/project/diffsptk)\n[](https://pypi.python.org/pypi/diffsptk)\n[](https://pypi.python.org/pypi/diffsptk)\n[](https://pypi.python.org/pypi/diffsptk)\n[](https://app.codecov.io/gh/sp-nitech/diffsptk)\n[](https://github.com/sp-nitech/diffsptk/blob/master/LICENSE)\n[](https://github.com/sp-nitech/diffsptk/actions)\n[](https://github.com/psf/black)\n\n\nRequirements\n------------\n- Python 3.8+\n- PyTorch 1.11.0+\n\n\nDocumentation\n-------------\n- See [this page](https://sp-nitech.github.io/diffsptk/latest/) for a reference manual.\n- Our [paper](https://www.isca-speech.org/archive/ssw_2023/yoshimura23_ssw.html) is available on the ISCA Archive.\n\n\nInstallation\n------------\nThe latest stable release can be installed through PyPI by running\n```sh\npip install diffsptk\n```\nThe development release can be installed from the master branch:\n```sh\npip install git+https://github.com/sp-nitech/diffsptk.git@master\n```\n\n\nExamples\n--------\n### Mel-cepstral analysis and synthesis\n```python\nimport diffsptk\n\n# Set analysis condition.\nfl = 400\nfp = 80\nn_fft = 512\nM = 24\n\n# Read waveform.\nx, sr = diffsptk.read(\"assets/data.wav\")\n\n# Compute STFT amplitude of x.\nstft = diffsptk.STFT(frame_length=fl, frame_period=fp, fft_length=n_fft)\nX = stft(x)\n\n# Estimate mel-cepstrum of x.\nalpha = diffsptk.get_alpha(sr)\nmcep = diffsptk.MelCepstralAnalysis(cep_order=M, fft_length=n_fft, alpha=alpha, n_iter=10)\nmc = mcep(X)\n\n# Reconstruct x.\nmlsa = diffsptk.MLSA(filter_order=M, frame_period=fp, alpha=alpha, taylor_order=30)\nx_hat = mlsa(mlsa(x, -mc), mc)\n\n# Write reconstructed waveform.\ndiffsptk.write(\"reconst.wav\", x_hat, sr)\n\n# Compute error.\nerror = (x_hat - x).abs().sum()\nprint(error)\n\n# Extract pitch of x.\npitch = diffsptk.Pitch(frame_period=fp, sample_rate=sr, f_min=80, f_max=180)\np = pitch(x)\n\n# Generate excitation signal.\nexcite = diffsptk.ExcitationGeneration(frame_period=fp)\ne = excite(p)\nn = diffsptk.nrand(x.size(0) - 1)\n\n# Synthesize waveform.\nx_voiced = mlsa(e, mc)\nx_unvoiced = mlsa(n, mc)\n\n# Output analysis-synthesis result.\ndiffsptk.write(\"voiced.wav\", x_voiced, sr)\ndiffsptk.write(\"unvoiced.wav\", x_unvoiced, sr)\n```\n\n### Mel-spectrogram, MFCC, and PLP extraction\n```python\nimport diffsptk\n\n# Set analysis condition.\nfl = 400\nfp = 80\nn_fft = 512\nn_channel = 80\nM = 12\n\n# Read waveform.\nx, sr = diffsptk.read(\"assets/data.wav\")\n\n# Compute STFT amplitude of x.\nstft = diffsptk.STFT(frame_length=fl, frame_period=fp, fft_length=n_fft)\nX = stft(x)\n\n# Extract mel-spectrogram.\nfbank = diffsptk.MelFilterBankAnalysis(\n n_channel=n_channel,\n fft_length=n_fft,\n sample_rate=sr,\n)\nY = fbank(X)\nprint(Y.shape)\n\n# Extract MFCC.\nmfcc = diffsptk.MFCC(\n mfcc_order=M,\n n_channel=n_channel,\n fft_length=n_fft,\n sample_rate=sr,\n)\nY = mfcc(X)\nprint(Y.shape)\n\n# Extract PLP.\nplp = diffsptk.PLP(\n plp_order=M,\n n_channel=n_channel,\n fft_length=n_fft,\n sample_rate=sr,\n)\nY = plp(X)\nprint(Y.shape)\n```\n\n### Subband decomposition\n```python\nimport diffsptk\n\nK = 4 # Number of subbands.\nM = 40 # Order of filter.\n\n# Read waveform.\nx, sr = diffsptk.read(\"assets/data.wav\")\n\n# Decompose x.\npqmf = diffsptk.PQMF(K, M)\ndecimate = diffsptk.Decimation(K)\ny = decimate(pqmf(x), dim=-1)\n\n# Reconstruct x.\ninterpolate = diffsptk.Interpolation(K)\nipqmf = diffsptk.IPQMF(K, M)\nx_hat = ipqmf(interpolate(K * y, dim=-1)).reshape(-1)\n\n# Write reconstructed waveform.\ndiffsptk.write(\"reconst.wav\", x_hat, sr)\n\n# Compute error.\nerror = (x_hat - x).abs().sum()\nprint(error)\n```\n\n### Vector quantization\n```python\nimport diffsptk\n\nK = 2 # Codebook size.\nM = 4 # Order of vector.\n\n# Prepare input.\nx = diffsptk.nrand(M)\n\n# Quantize x.\nvq = diffsptk.VectorQuantization(M, K)\nx_hat, indices, commitment_loss = vq(x)\n\n# Compute error.\nerror = (x_hat - x).abs().sum()\nprint(error)\n```\n\n\nLicense\n-------\nThis software is released under the Apache License 2.0.\n\n\nReference\n---------\n```bibtex\n@InProceedings{sp-nitech2023sptk,\n author = {Takenori Yoshimura and Takato Fujimoto and Keiichiro Oura and Keiichi Tokuda},\n title = {{SPTK4}: An open-source software toolkit for speech signal processing},\n booktitle = {12th ISCASpeech Synthesis Workshop (SSW 2023)},\n pages = {211--217},\n year = {2023},\n}\n```\n",
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