diffsptk 1.0.1

Speech signal processing modules for machine learning

diffsptk

diffsptk is a differentiable version of SPTK based on the PyTorch framework.

Requirements

• Python 3.8+
• PyTorch 1.11.0+

Documentation

See this page for a reference manual.

Installation

The latest stable release can be installed through PyPI by running

pip install diffsptk

The development release can be installed from the master branch:

pip install git+https://github.com/sp-nitech/diffsptk.git@master

Examples

Mel-cepstral analysis and synthesis

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 and MFCC extraction

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)

Subband decomposition

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

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

@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},
}