Implementing Kaldi Spectrogram

test/compliance/generate_test_stft_data.py

@@ -0,0 +1,80 @@
+import random
+
+# Path to the compute-spectrogram-feats executable.
+EXE_PATH = '/scratch/jamarshon/kaldi/src/featbin/compute-spectrogram-feats'
+
+# Path to the scp file. An example of its contents would be "my_id /scratch/jamarshon/audio/test/assets/kaldi_file.wav"
+# where the space separates an id from a wav file.
+SCP_PATH = 'scp:/scratch/jamarshon/downloads/a.scp'
+# The directory to which the stft features will be written to.
+OUTPUT_DIR = 'ark:/scratch/jamarshon/audio/test/assets/kaldi/'
+
+# The number of samples inside the input wave file read from `SCP_PATH`
+WAV_LEN = 20
+
+# How many output files should be generated.
+NUMBER_OF_OUTPUTS = 100
+
+WINDOWS = ['hamming', 'hanning', 'povey', 'rectangular', 'blackman']
+
+
+def generate_rand_boolean():
+    # Generates a random boolean ('true', 'false')
+    return 'true' if random.randint(0, 1) else 'false'
+
+
+def generate_rand_window_type():
+    # Generates a random window type
+    return WINDOWS[random.randint(0, len(WINDOWS) - 1)]
+
+
+def run():
+    for i in range(NUMBER_OF_OUTPUTS):
+        inputs = {
+            'blackman_coeff': '%.4f' % (random.random() * 5),
+            'dither': '0',
+            'energy_floor': '%.4f' % (random.random() * 5),
+            'frame_length': '%.4f' % (float(random.randint(2, WAV_LEN - 1)) / 16000 * 1000),
+            'frame_shift': '%.4f' % (float(random.randint(1, WAV_LEN - 1)) / 16000 * 1000),
+            'preemphasis_coefficient': '%.2f' % random.random(),
+            'raw_energy': generate_rand_boolean(),
+            'remove_dc_offset': generate_rand_boolean(),
+            'round_to_power_of_two': generate_rand_boolean(),
+            'snip_edges': generate_rand_boolean(),
+            'subtract_mean': generate_rand_boolean(),
+            'window_type': generate_rand_window_type()
+        }
+
+        fn = 'spec-' + ('-'.join(list(inputs.values())))
+
+        arg = [
+            EXE_PATH,
+            '--blackman-coeff=' + inputs['blackman_coeff'],
+            '--dither=' + inputs['dither'],
+            '--energy-floor=' + inputs['energy_floor'],
+            '--frame-length=' + inputs['frame_length'],
+            '--frame-shift=' + inputs['frame_shift'],
+            '--preemphasis-coefficient=' + inputs['preemphasis_coefficient'],
+            '--raw-energy=' + inputs['raw_energy'],
+            '--remove-dc-offset=' + inputs['remove_dc_offset'],
+            '--round-to-power-of-two=' + inputs['round_to_power_of_two'],
+            '--sample-frequency=16000',
+            '--snip-edges=' + inputs['snip_edges'],
+            '--subtract-mean=' + inputs['subtract_mean'],
+            '--window-type=' + inputs['window_type'],
+            SCP_PATH,
+            OUTPUT_DIR + fn + '.ark'
+        ]
+
+        print(fn)
+        print(inputs)
+        print(' '.join(arg))
+
+        try:
+            subprocess.call(arg)
+        except Exception:
+            pass
+
+
+if __name__ == '__main__':
+    run()

test/compliance/test_kaldi.py

@@ -0,0 +1,140 @@
+import math
+import os
+import test.common_utils
+import torch
+import torchaudio
+import torchaudio.compliance.kaldi as kaldi
+import unittest
+
+
+def extract_window(window, wave, f, frame_length, frame_shift, snip_edges):
+    # just a copy of ExtractWindow from feature-window.cc in python
+    def first_sample_of_frame(frame, window_size, window_shift, snip_edges):
+        if snip_edges:
+            return frame * window_shift
+        else:
+            midpoint_of_frame = frame * window_shift + window_shift // 2
+            beginning_of_frame = midpoint_of_frame - window_size // 2
+            return beginning_of_frame
+
+    sample_offset = 0
+    num_samples = sample_offset + wave.size(0)
+    start_sample = first_sample_of_frame(f, frame_length, frame_shift, snip_edges)
+    end_sample = start_sample + frame_length
+
+    if snip_edges:
+        assert(start_sample >= sample_offset and end_sample <= num_samples)
+    else:
+        assert(sample_offset == 0 or start_sample >= sample_offset)
+
+    wave_start = start_sample - sample_offset
+    wave_end = wave_start + frame_length
+    if wave_start >= 0 and wave_end <= wave.size(0):
+        window[f, :] = wave[wave_start:(wave_start + frame_length)]
+    else:
+        wave_dim = wave.size(0)
+        for s in range(frame_length):
+            s_in_wave = s + wave_start
+            while s_in_wave < 0 or s_in_wave >= wave_dim:
+                if s_in_wave < 0:
+                    s_in_wave = - s_in_wave - 1
+                else:
+                    s_in_wave = 2 * wave_dim - 1 - s_in_wave
+            window[f, s] = wave[s_in_wave]
+
+
+class Test_Kaldi(unittest.TestCase):
+    test_dirpath, test_dir = test.common_utils.create_temp_assets_dir()
+    test_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file.wav')
+
+    def _test_get_strided_helper(self, num_samples, window_size, window_shift, snip_edges):
+        waveform = torch.arange(num_samples).float()
+        output = kaldi._get_strided(waveform, window_size, window_shift, snip_edges)
+
+        # from NumFrames in feature-window.cc
+        n = window_size
+        if snip_edges:
+            m = 0 if num_samples < window_size else 1 + (num_samples - window_size) // window_shift
+        else:
+            m = (num_samples + (window_shift // 2)) // window_shift
+
+        self.assertTrue(output.dim() == 2)
+        self.assertTrue(output.shape[0] == m and output.shape[1] == n)
+
+        window = torch.empty((m, window_size))
+
+        for r in range(m):
+            extract_window(window, waveform, r, window_size, window_shift, snip_edges)
+        self.assertTrue(torch.allclose(window, output))
+
+    def test_get_strided(self):
+        # generate any combination where 0 < window_size <= num_samples and
+        # 0 < window_shift.
+        for num_samples in range(1, 20):
+            for window_size in range(1, num_samples + 1):
+                for window_shift in range(1, 2 * num_samples + 1):
+                    for snip_edges in range(0, 2):
+                        self._test_get_strided_helper(num_samples, window_size, window_shift, snip_edges)
+
+    def _create_data_set(self):
+        # used to generate the dataset to test on. this is not used in testing (offline procedure)
+        test_dirpath = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+        test_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file.wav')
+        sr = 16000
+        x = torch.arange(0, 20).float()
+        # between [-6,6]
+        y = torch.cos(2 * math.pi * x) + 3 * torch.sin(math.pi * x) + 2 * torch.cos(x)
+        # between [-2^30, 2^30]
+        y = (y / 6 * (1 << 30)).long()
+        # clear the last 16 bits because they aren't used anyways
+        y = ((y >> 16) << 16).float()
+        torchaudio.save(test_filepath, y, sr)
+        sound, sample_rate = torchaudio.load(test_filepath, normalization=False)
+        print(y >> 16)
+        self.assertTrue(sample_rate == sr)
+        self.assertTrue(torch.allclose(y, sound))
+
+    def test_spectrogram(self):
+        sound, sample_rate = torchaudio.load_wav(self.test_filepath)
+        kaldi_output_dir = os.path.join(self.test_dirpath, 'assets', 'kaldi')
+        files = list(filter(lambda x: x.startswith('spec'), os.listdir(kaldi_output_dir)))
+        print('Results:', len(files))
+
+        for f in files:
+            print(f)
+            kaldi_output_path = os.path.join(kaldi_output_dir, f)
+            kaldi_output_dict = {k: v for k, v in torchaudio.kaldi_io.read_mat_ark(kaldi_output_path)}
+
+            assert len(kaldi_output_dict) == 1 and 'my_id' in kaldi_output_dict, 'invalid test kaldi ark file'
+            kaldi_output = kaldi_output_dict['my_id']
+
+            args = f.split('-')
+            args[-1] = os.path.splitext(args[-1])[0]
+            assert len(args) == 13, 'invalid test kaldi file name'
+
+            spec_output = kaldi.spectrogram(
+                sound,
+                blackman_coeff=float(args[1]),
+                dither=float(args[2]),
+                energy_floor=float(args[3]),
+                frame_length=float(args[4]),
+                frame_shift=float(args[5]),
+                preemphasis_coefficient=float(args[6]),
+                raw_energy=args[7] == 'true',
+                remove_dc_offset=args[8] == 'true',
+                round_to_power_of_two=args[9] == 'true',
+                snip_edges=args[10] == 'true',
+                subtract_mean=args[11] == 'true',
+                window_type=args[12])
+
+            error = spec_output - kaldi_output
+            mse = error.pow(2).sum() / spec_output.numel()
+            max_error = torch.max(error.abs())
+
+            print('mse:', mse.item(), 'max_error:', max_error.item())
+            self.assertTrue(spec_output.shape, kaldi_output.shape)
+            self.assertTrue(torch.allclose(spec_output, kaldi_output, atol=1e-3, rtol=0))
+
+
+if __name__ == '__main__':
+    unittest.main()

test/test_dataloader.py

@@ -14,7 +14,7 @@ class TORCHAUDIODS(Dataset):
 
     def __init__(self):
         self.asset_dirpath = os.path.join(self.test_dirpath, "assets")
-        sound_files = list(filter(lambda x: '.wav' in x or '.mp3' in x, os.listdir(self.asset_dirpath)))
+        sound_files = ["sinewave.wav", "steam-train-whistle-daniel_simon.mp3"]
         self.data = [os.path.join(self.asset_dirpath, fn) for fn in sound_files]
         self.si, self.ei = torchaudio.info(os.path.join(self.asset_dirpath, "sinewave.wav"))
         self.si.precision = 16

torchaudio/__init__.py

@@ -4,7 +4,7 @@
 import torch
 import _torch_sox
 
-from torchaudio import transforms, datasets, kaldi_io, sox_effects, legacy
+from torchaudio import transforms, datasets, kaldi_io, sox_effects, legacy, compliance
 
 
 def check_input(src):
@@ -92,6 +92,14 @@ def load(filepath,
     return out, sample_rate
 
 
+def load_wav(filepath, **kwargs):
+    """ Loads a wave file. It assumes that the wav file uses 16 bit per sample that needs normalization by shifting
+    the input right by 16 bits.
+    """
+    kwargs['normalization'] = 1 << 16
+    return load(filepath, **kwargs)
+
+
 def save(filepath, src, sample_rate, precision=16, channels_first=True):
     """Convenience function for `save_encinfo`.
 

torchaudio/compliance/__init__.py

@@ -0,0 +1 @@
+from . import kaldi