>I have now applied the scripts I developed for those models to a

>combination of the english librispeech and voxforge corpora

Very impressive!

Thank you,

ken

--- (Edited on 10/31/2017 8:44 am [GMT-0400] by kmaclean) ---

I have done another auto-review round using the latest model. This time, I also upgraded to kaldi 5.2 and used that to train tdnn-chain models - with quite encouraging results:

%WER 2.48 [ 12525 / 504653, 737 ins, 2720 del, 9068 sub ] exp/nnet3_chain/tdnn_sp/decode_test/wer_10_0.0
%WER 3.03 [ 15269 / 504653, 948 ins, 3260 del, 11061 sub ] exp/nnet3_chain/tdnn_250/decode_test/wer_9_0.0

[bofh@donald py-kaldi-asr]$ python examples/chain_incremental.py 
tdnn_250 loading model...
tdnn_250 loading model... done, took 23.394126s.
tdnn_250 creating decoder...
tdnn_250 creating decoder... done, took 14.411979s.
decoding data/dw961.wav...
 0.087s:  4000 frames ( 0.250s) decoded.
 0.400s:  8000 frames ( 0.500s) decoded.
 0.742s: 12000 frames ( 0.750s) decoded.
 1.021s: 16000 frames ( 1.000s) decoded.
 1.263s: 20000 frames ( 1.250s) decoded.
 1.497s: 24000 frames ( 1.500s) decoded.
 1.714s: 28000 frames ( 1.750s) decoded.
 1.992s: 32000 frames ( 2.000s) decoded.
 2.370s: 36000 frames ( 2.250s) decoded.
 2.642s: 40000 frames ( 2.500s) decoded.
 2.873s: 44000 frames ( 2.750s) decoded.
 3.112s: 48000 frames ( 3.000s) decoded.
 3.333s: 52000 frames ( 3.250s) decoded.
 3.668s: 56000 frames ( 3.500s) decoded.
 3.876s: 60000 frames ( 3.750s) decoded.
 4.092s: 64000 frames ( 4.000s) decoded.
 4.305s: 68000 frames ( 4.250s) decoded.
 4.517s: 72000 frames ( 4.500s) decoded.
 4.951s: 74000 frames ( 4.625s) decoded.
*****************************************************************
** data/dw961.wav
** i cannot follow you she said 
** tdnn_250 likelihood: 1.99656772614
*****************************************************************
tdnn_250 decoding took     4.95s

--- (Edited on 11/29/2017 6:04 pm [GMT-0600] by guenter) ---

Thanks for your work. I've got a question though - the directory voxforge.cd_cont_6000 already contains means / mdef so I tried to use it directly with pocketshinx and the -hmm parameter and it seems to be working though loading means and variances takes quite some time (on a Raspberry). Any way I would be able to speed up the process or am I doing something completely wrong?

Michaela

--- (Edited on 5/5/2018 4:55 pm [GMT-0500] by ) ---

Hi Michaela,

currently we are quite focussed on kaldi - we do have models tuned for use on the rpi3. For now, this is work in progress but we are planning to do an "official" announcement soon. 

If you want to give it a try, here we have Raspbian packages of kaldi, the models and the python wrapper:

http://goofy.zamia.org/raspbian-ai/

once you have them installed, you can use a python script to try them (this one is adapted from the example scripts that come with https://github.com/gooofy/py-kaldi-asr ):

import sys
import os
import wave
import struct
import numpy as np

from time import time

from kaldiasr.nnet3 import KaldiNNet3OnlineModel, KaldiNNet3OnlineDecoder

# this is useful for benchmarking purposes
NUM_DECODER_RUNS = 1

MODELDIR    = '/opt/kaldi/model/kaldi-chain-voxforge-de'
MODEL       = 'tdnn_250'
WAVFILE     = 'data/gsp1.wav'

print '%s loading model...' % MODEL
time_start = time()
kaldi_model = KaldiNNet3OnlineModel (MODELDIR, MODEL, acoustic_scale=1.0, beam=7.0, frame_subsampling_factor=3)
print '%s loading model... done, took %fs.' % (MODEL, time()-time_start)

print '%s creating decoder...' % MODEL
time_start = time()
decoder = KaldiNNet3OnlineDecoder (kaldi_model)
print '%s creating decoder... done, took %fs.' % (MODEL, time()-time_start)

for i in range(NUM_DECODER_RUNS):

    time_start = time()

    print 'decoding %s...' % WAVFILE
    wavf = wave.open(WAVFILE, 'rb')

    # check format
    assert wavf.getnchannels()==1
    assert wavf.getsampwidth()==2

    # process file in 250ms chunks

    chunk_frames = 250 * wavf.getframerate() / 1000
    tot_frames   = wavf.getnframes()

    num_frames = 0
    while num_frames < tot_frames:

        finalize = False
        if (num_frames + chunk_frames) < tot_frames:
            nframes = chunk_frames
        else:
            nframes = tot_frames - num_frames
            finalize = True

        frames = wavf.readframes(nframes)
        num_frames += nframes
        samples = struct.unpack_from('<%dh' % nframes, frames)

        decoder.decode(wavf.getframerate(), np.array(samples, dtype=np.float32), finalize)

        s, l = decoder.get_decoded_string()

        print "%6.3fs: %5d frames (%6.3fs) decoded. %s" % (time()-time_start, num_frames, float(num_frames) / float(wavf.getframerate()), s)

    wavf.close()

    s, l = decoder.get_decoded_string()
    print
    print "*****************************************************************"
    print "**", WAVFILE
    print "**", s
    print "** %s likelihood:" % MODEL, l
    print "*****************************************************************"
    print
    print "%s decoding took %8.2fs" % (MODEL, time() - time_start )

--- (Edited on 5/8/2018 2:17 pm [GMT-0500] by guenter) ---