Revise parameters for Kaldi fbank compatibility test

Test parameters for Kaldi fbank were generated using this script, but this does not necessarily generate values in valid range, so we need to revise them.

First, we need to test the parity of default values.

$ compute-fbank-feats --help

Create Mel-filter bank (FBANK) feature files.
Usage:  compute-fbank-feats [options...] <wav-rspecifier> <feats-wspecifier>

Options:
  --allow-downsample          : If true, allow the input waveform to have a higher frequency than the specified --sample-frequency (and we'll downsample). (bool, default = false)
  --allow-upsample            : If true, allow the input waveform to have a lower frequency than the specified --sample-frequency (and we'll upsample). (bool, default = false)
  --blackman-coeff            : Constant coefficient for generalized Blackman window. (float, default = 0.42)
  --channel                   : Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (int, default = -1)
  --debug-mel                 : Print out debugging information for mel bin computation (bool, default = false)
  --dither                    : Dithering constant (0.0 means no dither). If you turn this off, you should set the --energy-floor option, e.g. to 1.0 or 0.1 (float, default = 1)
  --energy-floor              : Floor on energy (absolute, not relative) in FBANK computation. Only makes a difference if --use-energy=true; only necessary if --dither=0.0.  Suggested values: 0.1 or 1.0 (float, default = 0)
  --frame-length              : Frame length in milliseconds (float, default = 25)
  --frame-shift               : Frame shift in milliseconds (float, default = 10)
  --high-freq                 : High cutoff frequency for mel bins (if <= 0, offset from Nyquist) (float, default = 0)
  --htk-compat                : If true, put energy last.  Warning: not sufficient to get HTK compatible features (need to change other parameters). (bool, default = false)
  --low-freq                  : Low cutoff frequency for mel bins (float, default = 20)
  --max-feature-vectors       : Memory optimization. If larger than 0, periodically remove feature vectors so that only this number of the latest feature vectors is retained. (int, default = -1)
  --min-duration              : Minimum duration of segments to process (in seconds). (float, default = 0)
  --num-mel-bins              : Number of triangular mel-frequency bins (int, default = 23)
  --output-format             : Format of the output files [kaldi, htk] (string, default = "kaldi")
  --preemphasis-coefficient   : Coefficient for use in signal preemphasis (float, default = 0.97)
  --raw-energy                : If true, compute energy before preemphasis and windowing (bool, default = true)
  --remove-dc-offset          : Subtract mean from waveform on each frame (bool, default = true)
  --round-to-power-of-two     : If true, round window size to power of two by zero-padding input to FFT. (bool, default = true)
  --sample-frequency          : Waveform data sample frequency (must match the waveform file, if specified there) (float, default = 16000)
  --snip-edges                : If true, end effects will be handled by outputting only frames that completely fit in the file, and the number of frames depends on the frame-length.  If false, the number of frames depends only on the frame-shift, and we reflect the data at the ends. (bool, default = true)
  --subtract-mean             : Subtract mean of each feature file [CMS]; not recommended to do it this way.  (bool, default = false)
  --use-energy                : Add an extra dimension with energy to the FBANK output. (bool, default = false)
  --use-log-fbank             : If true, produce log-filterbank, else produce linear. (bool, default = true)
  --use-power                 : If true, use power, else use magnitude. (bool, default = true)
  --utt2spk                   : Utterance to speaker-id map (if doing VTLN and you have warps per speaker) (string, default = "")
  --vtln-high                 : High inflection point in piecewise linear VTLN warping function (if negative, offset from high-mel-freq (float, default = -500)
  --vtln-low                  : Low inflection point in piecewise linear VTLN warping function (float, default = 100)
  --vtln-map                  : Map from utterance or speaker-id to vtln warp factor (rspecifier) (string, default = "")
  --vtln-warp                 : Vtln warp factor (only applicable if vtln-map not specified) (float, default = 1)
  --window-type               : Type of window ("hamming"|"hanning"|"povey"|"rectangular"|"sine"|"blackmann") (string, default = "povey")
  --write-utt2dur             : Wspecifier to write duration of each utterance in seconds, e.g. 'ark,t:utt2dur'. (string, default = "")

Standard options:
  --config                    : Configuration file to read (this option may be repeated) (string, default = "")
  --help                      : Print out usage message (bool, default = false)
  --print-args                : Print the command line arguments (to stderr) (bool, default = true)
  --verbose                   : Verbose level (higher->more logging) (int, default = 0)

Second, we should cover values found in Kaldi examples. such as

• Switchboard
• WSJ
• Fisher Switchboard
• CSJ
• CHiME-1,2,3, CHiME-4 1ch, 2ch, 6ch, CHiME-5, CHiME-6 Track1, Track2
• Callhome
• librispeech
• TIMIT

Then we can also add perturbation

• --frame-length and --frame-shift: integer value around 10 - 50 [ms] if perturbing
• --preemphasis-coefficient: less than 1.0, around 0.90 - 0.99
• --low-freq: around 20 - 50 (and less than --high-freq)

See also https://github.com/pytorch/audio/pull/672

[engineerchuan]

@mthrok since this is so related to https://github.com/pytorch/audio/issues/689, I would like to work on this as well.

[mthrok]

Do you mean adding an equivalent implementation of compute-kaldi-pitch-feats to torchaudio?

Yes, exactly. That would be very cool, but it should not be a very high priority though.

We found that the pitch feature always improved the performance for several tonal languages (e.g., Chinese), and did not degrade the performance for the other languages. So, espnet1 decided to use log Mel filterbank + pitch features as default. However, the pitch feature extraction is rather complicated, and we had some difficulties in making this pitch feature extraction fully written by torch functions. So, espnet2 decided to only use log Mel filterbank features, instead. We still observe a slight degradation of the ASR performance, but that can be mitigated by some tuning. We’re now moving to espnet2 so we don’t need it in the long term, but probably it is quite beneficial for the short term or people keep to use espnet1.

I see. I created the issue https://github.com/pytorch/audio/issues/686 to track this.