Merge branch 'compute-vad' into 'master'

Compute vad

See merge request !3

bob/kaldi/__init__.py

 from .mfcc import mfcc
 from .mfcc import mfcc_from_path
+from .mfcc import compute_vad
 from .gmm import ubm_train
 from .gmm import ubm_full_train
 from .gmm import ubm_enroll

bob/kaldi/gmm.py

@@ -57,7 +57,7 @@ def ubm_train(feats, ubmname, num_threads=4, num_frames=500000,
     Returns
     -------
     str
-            A path to the the trained ubm model.
+            A text formatted trained Kaldi global DiagGMM model.
 
     """
 
@@ -66,6 +66,7 @@ def ubm_train(feats, ubmname, num_threads=4, num_frames=500000,
     binary3 = 'gmm-gselect'
     binary4 = 'gmm-global-acc-stats'
     binary5 = 'gmm-global-est'
+    binary6 = 'gmm-global-copy'
 
     # 1. Initialize a single diagonal GMM
     cmd1 = [binary1] # gmm-global-init-from-feats
@@ -175,16 +176,34 @@ def ubm_train(feats, ubmname, num_threads=4, num_frames=500000,
                             
                     os.unlink(inModel)
                     inModel = estfile.name
-                    
-    os.unlink(gselfile.name)
+   
+    # 6. Copy a single diagonal GMM as text string (for the BEAT platform)
+    ret = ""
+    with tempfile.NamedTemporaryFile(suffix='.txt') as txtfile, \
+         tempfile.NamedTemporaryFile(suffix='.log') as logfile:
+        cmd = [binary6] # gmm-global-copy
+        cmd += [
+            '--binary=false',
+            estfile.name,
+            txtfile.name,
+        ]
+        pipe = Popen(cmd, stdin=PIPE, stdout=PIPE, stderr=logfile)
+        pipe.communicate()
+        with open(logfile.name) as fp:
+            logtxt = fp.read()
+            logger.debug("%s", logtxt)
+        with open(txtfile.name, 'rt') as f:
+            ubmtxt = f.read()
+            ret = ubmtxt
+
     shutil.copyfile(estfile.name, ubmname)
-    shutil.copyfile(estfile.name, ubmname + '.dubm')
     os.unlink(estfile.name)
-    
-    return ubmname + '.dubm'
+    os.unlink(gselfile.name)
+            
+    return ret
 
 
-def ubm_full_train(feats, dubmname, num_gselect=20, num_iters=4,
+def ubm_full_train(feats, dubm, fubmfile, num_gselect=20, num_iters=4,
                    min_gaussian_weight=1.0e-04):
     """ Implements Kaldi egs/sre10/v1/train_full_ubm.sh
 
@@ -192,10 +211,10 @@ def ubm_full_train(feats, dubmname, num_gselect=20, num_iters=4,
     ----------
     feats : numpy.ndarray
             A 2D numpy ndarray object containing MFCCs.
-
-    dubmname : str
-            A path to the UBM model.
-
+    dubm : str
+            A text formatted trained Kaldi global DiagGMM model.
+    fubmfile : str
+            A path to the full covariance UBM model.
     num_gselect : :obj:`int`, optional
             Number of Gaussians to keep per frame.
     num_iters : :obj:`int`, optional
@@ -207,19 +226,22 @@ def ubm_full_train(feats, dubmname, num_gselect=20, num_iters=4,
     Returns
     -------
     str
-            A path to the the trained full covariance UBM model.
+            A path to the full covariance UBM model.
 
     """
 
     binary1 = 'gmm-global-to-fgmm'
-    binary2 = 'fgmm-global-to-gmm'
+    # binary2 = 'fgmm-global-to-gmm'
     binary3 = 'subsample-feats'
     binary4 = 'gmm-gselect'
     binary5 = 'fgmm-global-acc-stats'
     binary6 = 'fgmm-global-est'
 
-    origdubm = dubmname
-    dubmname += '.dubm'
+    # Convert UBM string to a file
+    with tempfile.NamedTemporaryFile(
+                        delete=False, suffix='.dump') as dubmfile:
+        with open(dubmfile.name, 'wt') as fp:
+            fp.write(dubm)
 
     # 1. Init (diagonal GMM to full-cov. GMM)
     # gmm-global-to-fgmm $srcdir/final.dubm $dir/0.ubm || exit 1;
@@ -229,7 +251,7 @@ def ubm_full_train(feats, dubmname, num_gselect=20, num_iters=4,
     initfile, tempfile.NamedTemporaryFile(suffix='.log') as logfile:
         inModel = initfile.name
         cmd1 += [
-            dubmname,
+            dubmfile.name,
             inModel,
         ]
         pipe1 = Popen(cmd1, stdin=PIPE, stdout=PIPE, stderr=logfile)
@@ -243,20 +265,10 @@ def ubm_full_train(feats, dubmname, num_gselect=20, num_iters=4,
     # gmm-gselect --n=$num_gselect "fgmm-global-to-gmm $dir/0.ubm - \
     # |" "$feats" \
     #   "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
-    cmd2 = [binary2] # fgmm-global-to-gmm
-    with tempfile.NamedTemporaryFile(suffix='.dubm') as dubmfile, \
-         tempfile.NamedTemporaryFile(suffix='.ark') as arkfile, \
+    # cmd2 = [binary2] # fgmm-global-to-gmm
+    # with tempfile.NamedTemporaryFile(suffix='.dubm') as dubmfile, \
+    with tempfile.NamedTemporaryFile(suffix='.ark') as arkfile, \
          tempfile.NamedTemporaryFile(suffix='.gz') as gselfile:
-        cmd2 += [
-            inModel,
-            dubmfile.name,
-        ]
-        with tempfile.NamedTemporaryFile(suffix='.log') as logfile:
-            pipe2 = Popen(cmd2, stdin=PIPE, stdout=PIPE, stderr=logfile)
-            pipe2.communicate()
-            with open(logfile.name) as fp:
-                logtxt = fp.read()
-                logger.debug("%s", logtxt)
         # subsample-feats --n=$subsample ark:- ark:- |"
         cmd = [binary3] # subsample-feats
         cmd += [
@@ -337,36 +349,43 @@ def ubm_full_train(feats, dubmname, num_gselect=20, num_iters=4,
                         os.unlink(inModel)
                         inModel = estfile.name
                         
-    shutil.copyfile(estfile.name, origdubm + '.fubm')
+    shutil.copyfile(estfile.name, fubmfile)
     os.unlink(estfile.name)
+    os.unlink(dubmfile.name)
     
-    return origdubm + '.fubm'
+    return fubmfile # ToDo : covert to a text format
 
-def ubm_enroll(feats, ubm_file):
+def ubm_enroll(feats, ubm):
     """Performes MAP adaptation of GMM-UBM model.
 
     Parameters
     ----------
     feats : numpy.ndarray
         A 2D numpy ndarray object containing MFCCs.
-    ubm_file : str
-        A path to the Kaldi global GMM.
+    ubm : str
+        A text formatted Kaldi global DiagGMM.
 
 
     Returns
     -------
     str
-        A path to the enrolled GMM.
+        A text formatted Kaldi enrolled DiagGMM.
 
     """
 
     binary1 = 'gmm-global-acc-stats'
     binary2 = 'global-gmm-adapt-map'
+    binary3 = 'gmm-global-copy'
 
+    with tempfile.NamedTemporaryFile(
+                        delete=False, suffix='.dump') as ubmfile:
+        with open(ubmfile.name, 'wt') as fp:
+            fp.write(ubm)
+            
     # 1. Accumulate stats for training a diagonal-covariance GMM.
     cmd1 = [binary1] # gmm-global-acc-stats
     cmd1 += [
-        ubm_file,
+        ubmfile.name,
         'ark:-',
         '-',
     ]
@@ -375,7 +394,7 @@ def ubm_enroll(feats, ubm_file):
     estfile, tempfile.NamedTemporaryFile(suffix='.log') as logfile:
         cmd2 += [
             '--update-flags=m',
-            ubm_file,
+            ubmfile.name,
             '-',
             estfile.name,
         ]
@@ -392,9 +411,33 @@ def ubm_enroll(feats, ubm_file):
             logtxt = fp.read()
             logger.debug("%s", logtxt)
 
-    return estfile.name
 
-def gmm_score(feats, gmm_file, ubm_file):
+    # 3. Copy adapted diagonal GMM as text string (for the BEAT platform)
+    ret = ""
+    with tempfile.NamedTemporaryFile(suffix='.txt') as txtfile, \
+         tempfile.NamedTemporaryFile(suffix='.log') as logfile:
+        cmd = [binary3] # gmm-global-copy
+        cmd += [
+            '--binary=false',
+            estfile.name,
+            txtfile.name,
+        ]
+        pipe = Popen(cmd, stdin=PIPE, stdout=PIPE, stderr=logfile)
+        pipe.communicate()
+        with open(logfile.name) as fp:
+            logtxt = fp.read()
+            logger.debug("%s", logtxt)
+        with open(txtfile.name, 'rt') as f:
+            ubmtxt = f.read()
+
+            ret = ubmtxt
+    
+    os.unlink(ubmfile.name)
+    os.unlink(estfile.name)
+
+    return ret
+
+def gmm_score(feats, spkubm, ubm):
     """Print out per-frame log-likelihoods for input utterance.
 
     Parameters
@@ -402,10 +445,10 @@ def gmm_score(feats, gmm_file, ubm_file):
     feats : numpy.ndarray
         A 2D numpy ndarray object containing MFCCs.
 
-    gmm_file : str
-        A path to Kaldi adapted global GMM.
-    ubm_file : str
-        A path to Kaldi global GMM.
+    spkubm : str
+        A text formatted Kaldi adapted global DiagGMM.
+    ubm : str
+        A text formatted Kaldi global DiagGMM.
 
 
     Returns
@@ -416,10 +459,22 @@ def gmm_score(feats, gmm_file, ubm_file):
     """
 
     binary1 = 'gmm-global-get-frame-likes'
+
+    # Convert UBM string to a file
+    with tempfile.NamedTemporaryFile(
+                        delete=False, suffix='.dubm') as ubmfile:
+        with open(ubmfile.name, 'wt') as fp:
+            fp.write(ubm)
+
+    # Convert speaker UBM string to a file
+    with tempfile.NamedTemporaryFile(
+                        delete=False, suffix='.dubm') as spkubmfile:
+        with open(spkubmfile.name, 'wt') as fp:
+            fp.write(spkubm)            
     
     models = [
-        gmm_file,
-        ubm_file
+        spkubmfile.name,
+        ubmfile.name
     ]
     ret = [0, 0]
     # import ipdb; ipdb.set_trace()
@@ -449,6 +504,8 @@ def gmm_score(feats, gmm_file, ubm_file):
                 logtxt = fp.read()
                 logger.debug("%s", logtxt)
 
+    os.unlink(ubmfile.name)
+    os.unlink(spkubmfile.name)
     return ret[0] - ret[1]
 
 # def gmm_score_fast(feats, gmm_file, ubm_file):

bob/kaldi/ivector.py

@@ -16,18 +16,20 @@ import logging
 logger = logging.getLogger(__name__)
 
 
-def ivector_train(feats, projector_file, num_gselect=20, ivector_dim=600,
-                  use_weights=False, num_iters=5, min_post=0.025,
-                  num_samples_for_weights=3, posterior_scale=1.0):
+def ivector_train(feats, fubm, ivector_extractor, num_gselect=20,
+                  ivector_dim=600, use_weights=False, num_iters=5,
+                  min_post=0.025, num_samples_for_weights=3,
+                  posterior_scale=1.0):
     """Implements Kaldi egs/sre10/v1/train_ivector_extractor.sh
 
     Parameters
     ----------
     feats : numpy.ndarray
         A 2D numpy ndarray object containing MFCCs.
-
-    projector_file : str
-        A path to global GMM file
+    fubm : str
+        A path to full-diagonal UBM file
+    ivector_extractor : str
+        A path to the ivector extractor
 
     num_gselect : :obj:`int`, optional
         Number of Gaussians to keep per frame.
@@ -61,8 +63,6 @@ def ivector_train(feats, projector_file, num_gselect=20, ivector_dim=600,
     binary6 = 'ivector-extractor-acc-stats'
     binary7 = 'ivector-extractor-est'
 
-    fgmm_model = projector_file + '.fubm'
-
     # 1. Create Kaldi training data structure
     # ToDo: implement Bob's function for that
     with tempfile.NamedTemporaryFile(delete=False, suffix='.ark') as arkfile:
@@ -79,7 +79,7 @@ def ivector_train(feats, projector_file, num_gselect=20, ivector_dim=600,
     with tempfile.NamedTemporaryFile(delete=False, suffix='.dubm') as \
     dubmfile, tempfile.NamedTemporaryFile(suffix='.log') as logfile:
         cmd1 += [
-            fgmm_model,
+            fubm,
             dubmfile.name,
         ]
         pipe1 = Popen(cmd1, stdin=PIPE, stdout=PIPE, stderr=logfile)
@@ -94,7 +94,7 @@ def ivector_train(feats, projector_file, num_gselect=20, ivector_dim=600,
         cmd2 += [
             '--ivector-dim=' + str(ivector_dim),
             '--use-weights=' + str(use_weights).lower(),
-            fgmm_model,
+            fubm,
             iefile.name,
         ]
         pipe2 = Popen(cmd2, stdin=PIPE, stdout=PIPE, stderr=logfile)
@@ -130,7 +130,7 @@ def ivector_train(feats, projector_file, num_gselect=20, ivector_dim=600,
         cmd4 = [binary4] # fgmm-global-gselect-to-post
         cmd4 += [
             '--min-post=' + str(min_post),
-            fgmm_model,
+            fubm,
             'ark:' + arkfile.name,
             'ark:' + gselfile.name,
             'ark:-',
@@ -201,21 +201,23 @@ def ivector_train(feats, projector_file, num_gselect=20, ivector_dim=600,
                     os.unlink(inModel)
                     inModel = estfile.name
 
-    shutil.copyfile(inModel, projector_file + '.ie')
+    shutil.copyfile(inModel, ivector_extractor)
     os.unlink(inModel)
 
-    return projector_file + '.ie'
+    return ivector_extractor # ToDo: covert to the string
 
 
-def ivector_extract(feats, projector_file, num_gselect=20, min_post=0.025,
-                    posterior_scale=1.0):
+def ivector_extract(feats, fubm, ivector_extractor, num_gselect=20,
+                    min_post=0.025, posterior_scale=1.0):
     """Implements Kaldi egs/sre10/v1/extract_ivectors.sh
 
     Parameters
     ----------
     feats : numpy.ndarray
         A 2D numpy ndarray object containing MFCCs.
-    projector_file : str
+    fubm : str
+        A path to full-diagonal UBM file
+    ivector_extractor : str
         A path to global GMM file.
     num_gselect : :obj:`int`, optional
         Number of Gaussians to keep per frame.
@@ -241,14 +243,13 @@ def ivector_extract(feats, projector_file, num_gselect=20, min_post=0.025,
     # import ipdb; ipdb.set_trace()
     # ivector-extract --verbose=2 $srcdir/final.ie "$feats" ark,s,cs:- \
     # ark,scp,t:$dir/ivector.JOB.ark,$dir/ivector.JOB.scp || exit 1;
-    fgmm_model = projector_file + '.fubm'
 
     # Initialize the i-vector extractor using the FGMM input
     cmd1 = [binary1] # fgmm-global-to-gmm
     with tempfile.NamedTemporaryFile(delete=False, suffix='.dubm') as \
     dubmfile, tempfile.NamedTemporaryFile(suffix='.log') as logfile:
         cmd1 += [
-            fgmm_model,
+            fubm,
             dubmfile.name,
         ]
         pipe1 = Popen(cmd1, stdin=PIPE, stdout=PIPE, stderr=logfile)
@@ -277,7 +278,7 @@ def ivector_extract(feats, projector_file, num_gselect=20, min_post=0.025,
         cmd2 = [binary3] # fgmm-global-gselect-to-post
         cmd2 += [
             '--min-post=' + str(min_post),
-            fgmm_model,
+            fubm,
             'ark:-',
             'ark,s,cs:' + gselfile.name,
             'ark:-',
@@ -302,7 +303,7 @@ def ivector_extract(feats, projector_file, num_gselect=20, min_post=0.025,
 
         cmd4 = [binary5] # ivector-extract
         cmd4 += [
-            projector_file + '.ie',
+            ivector_extractor,
             'ark:-',
             'ark,s,cs:' + postfile.name,
             'ark:-',

bob/kaldi/mfcc.py

@@ -10,6 +10,7 @@ import numpy as np
 from . import io
 from subprocess import PIPE, Popen
 from os.path import isfile
+import tempfile
 import logging
 logger = logging.getLogger(__name__)
 
@@ -18,7 +19,7 @@ def mfcc(data, rate=8000, preemphasis_coefficient=0.97, raw_energy=True,
          frame_length=25, frame_shift=10, num_ceps=13, num_mel_bins=23,
          cepstral_lifter=22, low_freq=20, high_freq=0, dither=1.0,
          snip_edges=True, normalization=True):
-    """Computes the MFCCs for a given input signal
+    """Computes the MFCCs for given speech samples.
 
     Parameters
     ----------
@@ -66,7 +67,7 @@ def mfcc(data, rate=8000, preemphasis_coefficient=0.97, raw_energy=True,
         32-bit floats).
 
     """
-
+    
     binary1 = 'compute-mfcc-feats'
     cmd1 = [binary1]
     binary2 = 'add-deltas'
@@ -116,11 +117,9 @@ def mfcc(data, rate=8000, preemphasis_coefficient=0.97, raw_energy=True,
         io.write_wav(pipe1.stdin, data, rate)
         pipe1.stdin.close()
 
-        # read ark from pipe3.stdout
         ret = [mat for name, mat in io.read_mat_ark(pipe3.stdout)][0]
         return ret
 
-
 def mfcc_from_path(filename, channel=0, preemphasis_coefficient=0.97,
                    raw_energy=True, frame_length=25, frame_shift=10,
                    num_ceps=13, num_mel_bins=23, cepstral_lifter=22,
@@ -222,68 +221,69 @@ def mfcc_from_path(filename, channel=0, preemphasis_coefficient=0.97,
         ret = [mat for name, mat in io.read_mat_ark(pipe3.stdout)][0]
         return ret
 
-# def compute_vad(feats, vad_energy_mean_scale=0.5, vad_energy_threshold=5,
-# vad_frames_context=0, vad_proportion_threshold=0.6):
-#   """Computes speech/non-speech segments given a Kaldi feature matrix
-
-#   Parameters:
-
-# feats (matrix): A 2-D numpy array, with log-energy being in the first
-# component of each feature vector
-
-
-#   Returns:
-
-#     A list of speech segments as a int32 numpy array with start and end times
+def compute_vad(samples, rate, vad_energy_mean_scale=0.5, vad_energy_th=5,
+    vad_frames_context=0, vad_proportion_th=0.6):
+    """Performs Voice Activity Detection on a Kaldi feature matrix
 
-#   Raises:
-
-#     RuntimeError: if any problem was detected during the conversion.
-
-#     IOError: if the binary to be executed does not exist
+    Parameters
+    ----------
+    feats : numpy.ndarray
+        A 2-D numpy array, with log-energy being in the first
+        component of each feature vector
+    rate : float
+        The sampling rate of the input signal in ``samples``.
+    vad_energy_mean_scale: :obj:`float`, optional
+        If this is set to s, to get the actual threshold we let m be the mean
+        log-energy of the file, and use s*m + vad-energy-th
+    vad_energy_th: :obj:`float`, optional
+        Constant term in energy threshold for MFCC0 for VAD.
+    vad_frames_context: :obj:`int`, optional
+        Number of frames of context on each side of central frame,
+        in window for which energy is monitored 
+    vad_proportion_th: :obj:`float`, optional
+        Parameter controlling the proportion of frames within the window that
+        need to have more energy than the threshold
 
-#   """
+    Returns
+    -------
 
-#   name = 'abc'
-#   binary1 = utils.kaldi_path(['src', 'ivectorbin', 'compute-vad'])
-#   cmd1 = [binary1]
+    numpy.ndarray
+        The labels [1/0] of voiced features (1D array of floats).
+    """
 
-#   # compute features into the ark file
-#   cmd1 += [
-#       '--vad-energy-mean-scale=' + str(vad_energy_mean_scale),
-#       '--vad-energy-threshold=' + str(vad_energy_threshold),
-#       '--vad-frames-context=' + str(vad_frames_context),
-#       '--vad-proportion-threshold=' + str(vad_proportion_threshold),
-#       'ark:-',
-#       'ark:-',
-#       ]
+    binary1 = 'compute-mfcc-feats'
+    cmd1 = [binary1]
+    binary2 = 'compute-vad'
+    cmd2 = [binary2]
 
-#   with tempfile.NamedTemporaryFile(suffix='.seg') as segfile:
-#     binary2 = utils.kaldi_path(
-# ['src', 'ivectorbin', 'create-split-from-vad'])
-#     cmd2 = [binary2]
+    cmd1 += [
+        '--sample-frequency=' + str(rate),
+        'ark:-',
+        'ark:-',
+    ]
+    cmd2 += [
+        '--vad-energy-mean-scale=' + str(vad_energy_mean_scale),
+        '--vad-energy-threshold=' + str(vad_energy_th),
+        '--vad-frames-context=' + str(vad_frames_context),
+        '--vad-proportion-threshold=' + str(vad_proportion_th),
+        'ark:-',
+        'ark:-',
+    ]
 
-#     cmd2 += [
-#       'ark:-',
-#       segfile.name,
-#     ]
+    samples /= np.max(np.abs(samples), axis=0)  # normalize to [-1,1]
 
-#     with open(os.devnull, "w") as fnull:
-#       # pipe1 numpy matrix -> compute-vad
-#       pipe1 = Popen(cmd1, stdout=PIPE, stdin=PIPE, stderr=fnull)
-#       pipe2 = Popen(cmd2, stdout=PIPE, stdin=pipe1.stdout, stderr=fnull)
+    with tempfile.NamedTemporaryFile(suffix='.log') as logfile:
+        pipe1 = Popen(cmd1, stdin=PIPE, stdout=PIPE, stderr=logfile)
+        pipe2 = Popen(cmd2, stdin=pipe1.stdout, stdout=PIPE, stderr=logfile)
 
-#       # write ark file into pipe.stdin
-#       io.write_mat(pipe1.stdin, feats, key='abc')
-#       pipe1.stdin.close()
+        pipe1.stdin.write(b'abc ')
+        io.write_wav(pipe1.stdin, samples, rate)
+        pipe1.stdin.close()
 
-#       # wait for piped execution to finish
-#       pipe2.communicate()
+        with open(logfile.name) as fp:
+            logtxt = fp.read()
+            logger.debug("%s", logtxt)
 
-#       # segfile should have the segmented output. read the file
-#       segs = []
-#       with open(segfile.name) as fp:
-#         for l in fp.readlines():
-#           start, end = l.split()[2:]
-#           segs.append([start, end])
-#       return np.array(segs, dtype='int32')
+        # read ark from pipe2.stdout
+        ret = [mat for name, mat in io.read_vec_flt_ark(pipe2.stdout)][0]
+        return ret

bob/kaldi/test/data/sample16k-vad.txt

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