def test_audio_preprocessors(self):
batch_size = 2
dl = nemo_asr.AudioToSpeechLabelDataLayer(
# featurizer_config=self.featurizer_config,
manifest_filepath=self.manifest_filepath,
labels=self.labels,
batch_size=batch_size,
# placement=DeviceType.GPU,
drop_last=False,
shuffle=False,
)
installed_torchaudio = True
try:
import torchaudio
except ModuleNotFoundError:
installed_torchaudio = False
with self.assertRaises(ModuleNotFoundError):
to_spectrogram = nemo_asr.AudioToSpectrogramPreprocessor(n_fft=400, window=None)
with self.assertRaises(ModuleNotFoundError):
to_mfcc = nemo_asr.AudioToMFCCPreprocessor(n_mfcc=15)
if installed_torchaudio:
to_spectrogram = nemo_asr.AudioToSpectrogramPreprocessor(n_fft=400, window=None)
to_mfcc = nemo_asr.AudioToMFCCPreprocessor(n_mfcc=15)
time_stretch_augment = nemo_asr.TimeStretchAugmentation(
self.featurizer_config['sample_rate'], probability=1.0, min_speed_rate=0.9, max_speed_rate=1.1
)
to_melspec = nemo_asr.AudioToMelSpectrogramPreprocessor(features=50)
for batch in dl.data_iterator:
input_signals, seq_lengths, _, _ = batch
input_signals = input_signals.to(to_melspec._device)
seq_lengths = seq_lengths.to(to_melspec._device)
melspec = to_melspec.forward(input_signals, seq_lengths)
if installed_torchaudio:
spec = to_spectrogram.forward(input_signals, seq_lengths)
mfcc = to_mfcc.forward(input_signals, seq_lengths)
ts_input_signals = time_stretch_augment.forward(input_signals, seq_lengths)
# Check that number of features is what we expect
self.assertTrue(melspec[0].shape[1] == 50)
if installed_torchaudio:
self.assertTrue(spec[0].shape[1] == 201) # n_fft // 2 + 1 bins
self.assertTrue(mfcc[0].shape[1] == 15)
timesteps = ts_input_signals[0].shape[1]
self.assertTrue(timesteps <= int(1.15 * self.featurizer_config['sample_rate']))
self.assertTrue(timesteps >= int(0.85 * self.featurizer_config['sample_rate']))
def test_audio_preprocessors(self):
batch_size = 5
dl = nemo_asr.AudioToTextDataLayer(
# featurizer_config=self.featurizer_config,
manifest_filepath=self.manifest_filepath,
labels=self.labels,
batch_size=batch_size,
# placement=DeviceType.GPU,
drop_last=True,
shuffle=False,
)
installed_torchaudio = True
try:
import torchaudio
except ModuleNotFoundError:
installed_torchaudio = False
with self.assertRaises(ModuleNotFoundError):
to_spectrogram = nemo_asr.AudioToSpectrogramPreprocessor(
n_fft=400, window=None)
with self.assertRaises(ModuleNotFoundError):
to_mfcc = nemo_asr.AudioToMFCCPreprocessor(n_mfcc=15)
if installed_torchaudio:
to_spectrogram = nemo_asr.AudioToSpectrogramPreprocessor(
n_fft=400, window=None)
to_mfcc = nemo_asr.AudioToMFCCPreprocessor(n_mfcc=15)
to_melspec = nemo_asr.AudioToMelSpectrogramPreprocessor(features=50)
for batch in dl.data_iterator:
input_signals, seq_lengths, _, _ = batch
input_signals = input_signals.to(to_melspec._device)
seq_lengths = seq_lengths.to(to_melspec._device)
melspec = to_melspec.forward(input_signals, seq_lengths)
if installed_torchaudio:
spec = to_spectrogram.forward(input_signals, seq_lengths)
mfcc = to_mfcc.forward(input_signals, seq_lengths)
# Check that number of features is what we expect
self.assertTrue(melspec[0].shape[1] == 50)
if installed_torchaudio:
self.assertTrue(spec[0].shape[1] == 201) # n_fft // 2 + 1 bins
self.assertTrue(mfcc[0].shape[1] == 15)
