Merge pull request #10 from mmcauliffe/autovot-support

Autovot support

conch/analysis/autovot.py

@@ -0,0 +1,87 @@
+from .functions import BaseAnalysisFunction
+import wave
+import subprocess
+import textgrid 
+import os
+import tempfile
+
+
+def is_autovot_friendly_file(sound_file):
+    rate = subprocess.run(["soxi", "-r", sound_file], encoding="UTF-8", stdout=subprocess.PIPE).stdout
+    if int(rate) != 16000:
+        return False
+
+    channels = subprocess.run(["soxi", "-c", sound_file], encoding="UTF-8", stdout=subprocess.PIPE).stdout
+    if int(channels) != 1:
+        return False
+    return True
+
+def resample_for_autovot(soundfile, tmpdir):
+    output_file = os.path.join(tmpdir, "sound_file.wav")
+    subprocess.call(["sox", soundfile, "-c", "1", "-r", "16000", output_file])
+    return output_file
+
+
+class MeasureVOTPretrained(object):
+    def __init__(self, classifier_to_use=None, min_vot_length=15, max_vot_length=250, window_max=30, window_min=30, debug=False):
+        if classifier_to_use is None:
+            raise ValueError("There must be a classifier to run AutoVOT")
+        else:
+            self.classifier_to_use = classifier_to_use
+        self.min_vot_length = min_vot_length
+        self.max_vot_length = max_vot_length
+        self.debug = debug
+        self.window_max = window_max
+        self.window_min = window_min
+
+    def __call__(self, segment):
+        file_path = os.path.expanduser(segment["file_path"])
+        begin = segment["begin"]
+        end = segment["end"]
+        vot_marks = sorted(segment["vot_marks"], key=lambda x: x[0])
+        grid = textgrid.TextGrid(maxTime=end)
+        vot_tier = textgrid.IntervalTier(name='vot', maxTime=end)
+        for vot_begin, vot_end, *extra_data in vot_marks:
+            vot_tier.add(vot_begin, vot_end, 'vot')
+        grid.append(vot_tier)
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            grid_path = "{}/file.TextGrid".format(tmpdirname)
+            csv_path = "{}/file.csv".format(tmpdirname)
+            wav_filenames = "{}/wavs.txt".format(tmpdirname)
+            textgrid_filenames = "{}/textgrids.txt".format(tmpdirname)
+
+            if not is_autovot_friendly_file(file_path):
+                file_path = resample_for_autovot(file_path, tmpdirname)
+
+            with open(wav_filenames, 'w') as f:
+                f.write("{}\n".format(file_path))
+
+            with open(textgrid_filenames, 'w') as f:
+                f.write("{}\n".format(grid_path))
+
+            grid.write(grid_path)
+
+            if self.debug:
+                grid.write('/tmp/textgrid_from_conch.csv')
+                with open('/tmp/alt_wordlist.txt', 'w') as f:
+                    f.write("{}\n".format('/tmp/textgrid_from_conch.csv'))
+                subprocess.run(["auto_vot_decode.py", wav_filenames, '/tmp/alt_wordlist.txt', self.classifier_to_use, '--vot_tier', 'vot', '--vot_mark', 'vot', "--min_vot_length", str(self.min_vot_length), "--max_vot_length", str(self.max_vot_length), "--window_max", str(self.window_max), "--window_min", str(self.window_min)])
+            subprocess.run(["auto_vot_decode.py", wav_filenames, textgrid_filenames, self.classifier_to_use, '--vot_tier', 'vot', '--vot_mark', 'vot', '--csv_file', csv_path, "--min_vot_length", str(self.min_vot_length), "--max_vot_length", str(self.max_vot_length), "--window_max", str(self.window_max), "--window_min", str(self.window_min)])
+
+            return_list = []
+            with open(csv_path, "r") as f: 
+                f.readline()
+                for l, (b, e, *extra_data) in zip(f, vot_marks):
+                    _, time, vot, confidence = l.split(',')
+                    if "neg 0\n" ==  confidence:
+                        confidence = 0
+                    return_list.append((float(time), float(vot), float(confidence), *extra_data))
+            return return_list
+
+class AutoVOTAnalysisFunction(BaseAnalysisFunction):
+    def __init__(self, classifier_to_use=None, min_vot_length=15, max_vot_length=250, window_max=30, window_min=30, debug=False, arguments=None):
+        super(AutoVOTAnalysisFunction, self).__init__()
+        self._function = MeasureVOTPretrained(classifier_to_use=classifier_to_use, min_vot_length=min_vot_length, max_vot_length=max_vot_length, window_max=window_max, window_min=window_min, debug=debug)
+        self.requires_file = True
+        self.uses_segments = True
+        self.requires_segment_as_arg = True

conch/analysis/functions.py

@@ -15,6 +15,7 @@ class BaseAnalysisFunction(object):
     def __init__(self):
         self._function = print
         self.requires_file = False
+        self.requires_segment_as_arg = False
         self.uses_segments = False
         self.arguments = []
 
@@ -39,6 +40,8 @@ def __call__(self, segment):
         elif isinstance(segment, str) and not self.requires_file:
             signal, sr = librosa.load(safe_path(segment))
             return self._function(signal, sr, *self.arguments)
+        elif isinstance(segment, FileSegment) and self.requires_segment_as_arg:
+            return self._function(segment, *self.arguments)
         elif isinstance(segment, FileSegment) and self.requires_file and not self.uses_segments:
             beg, end = segment.begin, segment.end
             padding = segment['padding']

conch/analysis/segments.py

@@ -44,6 +44,8 @@ def __eq__(self, other):
             return False
         if self.channel != other.channel:
             return False
+        if self.properties != other.properties:
+            return False
         return True
 
     def __lt__(self, other):

tests/conftest.py

@@ -37,6 +37,9 @@ def praat_script_test_dir(test_dir):
 def soundfiles_dir(test_dir):
     return os.path.join(test_dir, 'soundfiles')
 
+@pytest.fixture(scope='session')
+def autovot_dir(test_dir):
+    return os.path.join(test_dir, 'autovot')
 
 @pytest.fixture(scope='session')
 def tts_dir(test_dir):
@@ -62,7 +65,6 @@ def y_path(soundfiles_dir):
 def acoustic_corpus_path(soundfiles_dir):
     return os.path.join(soundfiles_dir, 'acoustic_corpus.wav')
 
-
 @pytest.fixture(scope='session')
 def call_back():
     def function(*args):
@@ -87,6 +89,22 @@ def base_filenames(soundfiles_dir):
                  if x.endswith('.wav')]
     return filenames
 
+@pytest.fixture(scope='session')
+def autovot_markings(test_dir):
+    vot_markings = []
+    with open(os.path.join(test_dir, "vot_marks"), "r") as f:
+        for x in f:
+            vots = x.split(' ')
+            vot_markings.append((float(vots[0]), float(vots[1])))
+    return vot_markings
+
+@pytest.fixture(scope='session')
+def classifier_path(test_dir):
+    return os.path.join(test_dir, "vot_model", "sotc_voiceless.classifier")
+
+@pytest.fixture(scope='session')
+def autovot_correct_times():
+     return [(1.593, 0.056, 180.344), (1.828, 0.008, 126.073), (1.909, 0.071, 90.8671), (2.041, 0.005, 45.6481), (2.687, 0.016, 212.67), (2.859, 0.005, 22.646), (2.951, 0.005, 78.2495), (3.351, 0.052, 84.7406), (5.574, 0.02, 96.0191), (6.212, 0.01, 72.1773), (6.736, 0.02, 114.721), (7.02, 0.029, 224.901), (9.255, 0.032, 123.367), (9.498, 0.017, 92.7151), (11.424, 0.056, 85.1062), (13.144, 0.012, 191.111), (13.55, 0.012, 59.8446), (25.125, 0.014, 165.632)]
 
 @pytest.fixture(scope='session')
 def praatpath():
@@ -114,7 +132,6 @@ def formants_func():
                                 window_length=0.025)
     return func
 
-
 @pytest.fixture(scope='session')
 def pitch_func():
     func = PitchTrackFunction(min_pitch=50, max_pitch=500, time_step=0.01)

tests/data/vot_marks

@@ -0,0 +1,18 @@
+1.50346 1.65870
+1.85687 1.90566
+1.90566 1.98664
+2.06736 2.14425
+2.65964 2.70424
+2.78255 2.85937
+2.93352 2.95891
+3.32791 3.38926
+5.53054 5.60142
+6.18190 6.23417
+6.76380 6.79755
+6.89678 7.06540
+9.21705 9.32077
+9.44277 9.50559
+11.44430 11.50088
+13.10701 13.22000
+13.51863 13.55111
+25.09728 25.14633

tests/data/vot_model/sotc_voiceless.classifier.neg

@@ -0,0 +1 @@
+59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

tests/data/vot_model/sotc_voiceless.classifier.pos

@@ -0,0 +1 @@
+77 -5.19517 0.974839 8.37886 4.43399 -0.291111 -1.03706 -0.851605 -2.99899 -0.988911 0.238412 -6.02394 7.69937 1.91178 12.1159 17.234 3.06435 -4.58158 -6.52092 0.704001 -9.8874 -4.83043 -1.23498 4.27573 -2.88435 -4.91017 -3.34528 -1.34791 11.5056 28.1743 1.4589 0.965129 -11.9287 0.431235 0.497913 -4.02346 -7.29419 -1.02066 12.9648 0.510423 0.892902 -2.05145 -1.73262 -8.13197 14.6691 5.57997 0.225761 5.1901 -7.44362 21.9976 0.278337 1.93388 -36.4286 10.1835 -3.78564 -4.12507 -17.2654 -1.25228 -18.5517 -51.1295 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

tests/test_analysis_autovot.py

@@ -0,0 +1,20 @@
+from conch.analysis.autovot import AutoVOTAnalysisFunction
+import librosa
+from statistics import mean
+import wave
+import pytest
+from conch.analysis.segments import SegmentMapping
+from conch import analyze_segments
+
+
+def test_autovot(acoustic_corpus_path, autovot_markings, classifier_path, autovot_correct_times):
+    mapping = SegmentMapping()
+    with wave.open(acoustic_corpus_path, 'r') as f: 
+        length = f.getnframes() / float(f.getframerate())
+    mapping.add_file_segment(acoustic_corpus_path, 0, length, channel=0, vot_marks=autovot_markings)
+    func = AutoVOTAnalysisFunction(classifier_to_use=classifier_path, window_min=-30, window_max=30, min_vot_length=5, max_vot_length=100)
+    output = analyze_segments(mapping, func, multiprocessing=False)
+    output = output[mapping[0]]
+    for o, truth in zip(output, autovot_correct_times):
+        assert o == truth
+