This repo contains an implementation of
- FDLP-spectrogram from the paper Radically Old Way of Computing Spectra: Applications in End-to-End ASR (https://arxiv.org/abs/2103.14129)
- Modulation Vectors from the paper M-vectors: Sub-band Based Energy Modulation Features for Multi-stream Automatic Speech Recognition (https://ieeexplore.ieee.org/abstract/document/8682710)
The implementation allows fast batch computation of FDLP-spectrogram that can even be used on the fly for DNN training.
To compute FDLP spectrogram
from fdlp import FDLP
fdlp = FDLP()
# speech (batch x signal length) : padded speech signals formed into a batch
# lens (batch) : lengths of each padded speech siganl in the batch
# set lens=None if you are computing features one utterance at a time and not as a batch
feats, olens = fdlp.extract_feats(speech, lens)The fdlp class takes the following important parameters which are set to reasonable default values.
n_filters: int = 80, # Number of filters
coeff_num: int = 100, # Number of modulation coefficients to compute
coeff_range: str = '1,100', # Range of modulation coefficients to preserve
order: int = 150, # Order of FDLP model
fduration: float = 1.5, # Duration of window in seconds
frate: int = 100, # Frame rate
overlap_fraction: float = 0.25, # Overlap fraction in Overlap-Add
srate: int = 16000 # Sample rate of the speech signal# Kaldi-like features
make-fdlp kaldi wav.scp "ark:| copy-feats ark:- ark,scp:/path/to/srotage/make_fdlp.ark,data/feats.scp" [data/utt2num_frames]
For more info type:
make-fdlp kaldi --help
The performance of an e2e ASR with these features can be found in https://arxiv.org/abs/2103.14129 and is summarized below
| Data set | mel-spectrogram | FDLP-spectrogram |
|---|---|---|
| WSJ (test_eval92) | 5.1 | 4.8 |
| REVERB (et_real_1ch / et_real_1ch_wpe / et_real_8ch_beamformit) | 23.2 / 20.7 / 9.2 | 19.4 / 18.0 / 7.2 |
| CHIME4 (et05_real_isolated_1ch_track / et05_real_beamformit_2mics / et05_real_beamformit_5mics) | 23.7 / 20.4 / 16.8 | 23.4 / 19.5 / 15.8 |
from fdlp import FDLP
fdlp = FDLP(lfr=10, return_mvector=True)
# speech (batch x signal length) : padded speech signals formed into a batch
# lens (batch) : lengths of each padded speech siganl in the batch
feats, olens = fdlp.extract_feats(speech, lens)The fdlp class takes the following important parameters for M-vector computation.
n_filters: int = 80, # Number of filters
coeff_num: int = 100, # Number of modulation coefficients to compute
coeff_range: str = '1,100', # Range of modulation coefficients to preserve
order: int = 150, # Order of FDLP model
fduration: float = 1.5, # Duration of window in seconds
frate: int = 100, # Frame rate
lfr: int = 10, # M-vectors are computed at this frame-rate and then interpolated to frate
overlap_fraction: float = 0.25, # Overlap fraction in Overlap-Add
srate: int = 16000 # Sample rate of the speech signalResults with these features for Kaldi TDNN models for REVERB data set can be found in Modulation Vectors as Robust Feature Representation for ASR in Domain Mismatched Conditions (https://www.isca-speech.org/archive_v0/Interspeech_2019/pdfs/2723.pdf)
paper: https://arxiv.org/pdf/2203.13216.pdf
This work modifies the conventional FDLP model. The M-vectors computed using complex FDLP exactly corresponds to the modulation spectrum of speech in different frequency sub-bands.
from fdlp.src.fdlp import fdlp
fdlp = fdlp(lfr=10, return_mvector=True, complex_mvectors=True)
# speech (batch x signal length) : padded speech signals formed into a batch
# lens (batch) : lengths of each padded speech siganl in the batch
feats, olens = fdlp.extract_feats(speech, lens)To install the latest, unreleased version, do:
pip install git+https://github.com/sadhusamik/fdlp_spectrogram
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