diffsptk

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

Requirements

• Python 3.8+
• PyTorch 1.10.0+

Documentation

See this page for a reference manual.

Installation

The latest stable release can be installed through PyPI by running

pip install diffsptk

Alternatively,

git clone https://github.com/sp-nitech/diffsptk.git
pip install -e diffsptk

Examples

Mel-cepstral analysis

import diffsptk
import torch

# Generate waveform.
x = torch.randn(100)

# Compute STFT of x.
stft = diffsptk.STFT(frame_length=12, frame_period=10, fft_length=16)
X = stft(x)

# Estimate 4-th order mel-cepstrum of x.
mcep = diffsptk.MelCepstralAnalysis(cep_order=4, fft_length=16, alpha=0.1, n_iter=1)
mc = mcep(X)

Mel-spectrogram extraction

import diffsptk
import torch

# Generate waveform.
x = torch.randn(100)

# Compute STFT of x.
stft = diffsptk.STFT(frame_length=12, frame_period=10, fft_length=32)
X = stft(x)

# Apply 4 mel-filter banks to the STFT.
fbank = diffsptk.MelFilterBankAnalysis(n_channel=4, fft_length=32, sample_rate=8000, floor=1e-1)
Y = fbank(X)

Subband decomposition

import diffsptk
import torch

K = 4   # Number of subbands.
M = 40  # Order of filter.

# Generate waveform.
x = torch.randn(100)

# 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))

# Compute error between two signals.
error = torch.abs(x_hat - x).sum()

Status

~~module~~ will not be implemented in this repository.

• [x] acorr
• [ ] ~~acr2csm~~
• [ ] ~~aeq~~ (torch.allclose)
• [ ] ~~amgcep~~
• [ ] ~~average~~ (torch.mean)
• [x] b2mc
• [ ] ~~bcp~~ (torch.split)
• [ ] ~~bcut~~
• [x] c2acr
• [x] c2mpir
• [x] c2ndps
• [x] cdist
• [ ] ~~clip~~ (torch.clip)
• [ ] ~~csm2acr~~
• [x] dct
• [x] decimate
• [x] delay
• [x] delta
• [x] dequantize
• [x] df2
• [x] dfs
• [ ] ~~dmp~~
• [ ] ~~dtw~~
• [ ] ~~dtw_merge~~
• [ ] ~~entropy~~ (torch.special.entr)
• [ ] ~~excite~~
• [ ] ~~extract~~
• [x] fbank
• [ ] ~~fd~~
• [ ] ~~fdrw~~
• [ ] ~~fft~~ (torch.fft.fft)
• [ ] ~~fft2~~ (torch.fft.fft2)
• [x] fftcep
• [ ] ~~fftr~~ (torch.fft.rfft)
• [ ] ~~fftr2~~ (torch.fft.rfft2)
• [x] frame
• [x] freqt
• [ ] ~~glogsp~~
• [ ] ~~gmm~~
• [ ] ~~gmmp~~
• [x] gnorm
• [ ] ~~gpolezero~~
• [ ] ~~grlogsp~~
• [x] grpdelay
• [ ] ~~gseries~~
• [ ] ~~gspecgram~~
• [ ] ~~gwave~~
• [ ] ~~histogram~~ (torch.histogram)
• [ ] ~~huffman~~
• [ ] ~~huffman_decode~~
• [ ] ~~huffman_encode~~
• [x] idct
• [ ] ~~ifft~~ (torch.fft.ifft)
• [ ] ~~ifft2~~ (torch.fft.ifft2)
• [x] ignorm
• [ ] imglsadf (will be appeared)
• [x] impulse
• [x] imsvq
• [x] interpolate
• [x] ipqmf
• [x] iulaw
• [x] lar2par
• [ ] ~~lbg~~
• [x] levdur
• [x] linear_intpl
• [x] lpc
• [ ] ~~lpc2c~~
• [ ] ~~lpc2lsp~~
• [x] lpc2par
• [x] lpccheck
• [ ] ~~lsp2lpc~~
• [ ] ~~lspcheck~~
• [ ] ~~lspdf~~
• [ ] ~~ltcdf~~
• [x] mc2b
• [x] mcpf
• [ ] ~~median~~ (torch.median)
• [ ] ~~merge~~ (torch.cat)
• [x] mfcc
• [x] mgc2mgc
• [x] mgc2sp
• [x] mgcep
• [ ] mglsadf (will be appeared)
• [ ] ~~mglsp2sp~~
• [ ] ~~minmax~~
• [x] mlpg (support only unit variance)
• [ ] ~~mlsacheck~~
• [x] mpir2c
• [ ] ~~mseq~~
• [ ] ~~msvq~~
• [ ] ~~nan~~ (torch.isnan)
• [x] ndps2c
• [x] norm0
• [ ] ~~nrand~~ (torch.randn)
• [x] par2lar
• [x] par2lpc
• [x] pca
• [ ] ~~pcas~~
• [x] phase
• [x] pitch
• [ ] ~~pitch_mark~~
• [ ] ~~poledf~~
• [x] pqmf
• [x] quantize
• [x] ramp
• [ ] ~~reverse~~ (torch.flip)
• [ ] ~~rlevdur~~
• [x] rmse
• [ ] ~~root_pol~~
• [x] sin
• [x] smcep
• [x] snr
• [x] sopr
• [x] spec
• [x] step
• [ ] ~~swab~~
• [ ] ~~symmetrize~~
• [ ] ~~train~~
• [ ] ~~transpose~~ (torch.transpose)
• [x] ulaw
• [ ] ~~vc~~
• [ ] ~~vopr~~
• [ ] ~~vstat~~ (torch.var_mean)
• [ ] ~~vsum~~ (torch.sum)
• [x] window
• [ ] ~~x2x~~
• [x] zcross
• [x] zerodf

License

This software is released under the Apache License 2.0.