def testSaveWavFile(self):
tmp_dir = self.get_temp_dir()
file_path = os.path.join(tmp_dir, "load_test.wav")
save_data = np.zeros([16000, 1])
input_data.save_wav_file(file_path, save_data, 16000)
loaded_data = input_data.load_wav_file(file_path)
self.assertIsNotNone(loaded_data)
self.assertEqual(16000, len(loaded_data))
def main(_):
words_list = input_data.prepare_words_list(FLAGS.wanted_words.split(','))
model_settings = models.prepare_model_settings(
len(words_list), FLAGS.sample_rate, FLAGS.clip_duration_ms,
FLAGS.window_size_ms, FLAGS.window_stride_ms, FLAGS.dct_coefficient_count)
audio_processor = input_data.AudioProcessor(
'', FLAGS.data_dir, FLAGS.silence_percentage, 10,
FLAGS.wanted_words.split(','), FLAGS.validation_percentage,
FLAGS.testing_percentage, model_settings)
output_audio_sample_count = FLAGS.sample_rate * FLAGS.test_duration_seconds
output_audio = np.zeros((output_audio_sample_count,), dtype=np.float32)
# Set up background audio.
background_crossover_ms = 500
background_segment_duration_ms = (
FLAGS.clip_duration_ms + background_crossover_ms)
background_segment_duration_samples = int(
(background_segment_duration_ms * FLAGS.sample_rate) / 1000)
background_segment_stride_samples = int(
(FLAGS.clip_duration_ms * FLAGS.sample_rate) / 1000)
background_ramp_samples = int(
((background_crossover_ms / 2) * FLAGS.sample_rate) / 1000)
# Mix the background audio into the main track.
how_many_backgrounds = int(
math.ceil(output_audio_sample_count / background_segment_stride_samples))
for i in range(how_many_backgrounds):
output_offset = int(i * background_segment_stride_samples)
background_index = np.random.randint(len(audio_processor.background_data))
background_samples = audio_processor.background_data[background_index]
background_offset = np.random.randint(
0, len(background_samples) - model_settings['desired_samples'])
background_volume = np.random.uniform(0, FLAGS.background_volume)
mix_in_audio_sample(output_audio, output_offset, background_samples,
background_offset, background_segment_duration_samples,
background_volume, background_ramp_samples,
background_ramp_samples)
# Mix the words into the main track, noting their labels and positions.
output_labels = []
word_stride_ms = FLAGS.clip_duration_ms + FLAGS.word_gap_ms
word_stride_samples = int((word_stride_ms * FLAGS.sample_rate) / 1000)
clip_duration_samples = int(
(FLAGS.clip_duration_ms * FLAGS.sample_rate) / 1000)
word_gap_samples = int((FLAGS.word_gap_ms * FLAGS.sample_rate) / 1000)
how_many_words = int(
math.floor(output_audio_sample_count / word_stride_samples))
all_test_data, all_test_labels = audio_processor.get_unprocessed_data(
-1, model_settings, 'testing')
for i in range(how_many_words):
output_offset = (
int(i * word_stride_samples) + np.random.randint(word_gap_samples))
output_offset_ms = (output_offset * 1000) / FLAGS.sample_rate
is_unknown = np.random.randint(100) < FLAGS.unknown_percentage
if is_unknown:
wanted_label = input_data.UNKNOWN_WORD_LABEL
else:
wanted_label = words_list[2 + np.random.randint(len(words_list) - 2)]
test_data_start = np.random.randint(len(all_test_data))
found_sample_data = None
index_lookup = np.arange(len(all_test_data), dtype=np.int32)
np.random.shuffle(index_lookup)
for test_data_offset in range(len(all_test_data)):
test_data_index = index_lookup[(
test_data_start + test_data_offset) % len(all_test_data)]
current_label = all_test_labels[test_data_index]
if current_label == wanted_label:
found_sample_data = all_test_data[test_data_index]
break
mix_in_audio_sample(output_audio, output_offset, found_sample_data, 0,
clip_duration_samples, 1.0, 500, 500)
output_labels.append({'label': wanted_label, 'time': output_offset_ms})
input_data.save_wav_file(FLAGS.output_audio_file, output_audio,
FLAGS.sample_rate)
tf.logging.info('Saved streaming test wav to %s', FLAGS.output_audio_file)
with open(FLAGS.output_labels_file, 'w') as f:
for output_label in output_labels:
f.write('%s, %f\n' % (output_label['label'], output_label['time']))
tf.logging.info('Saved streaming test labels to %s', FLAGS.output_labels_file)
def main(_):
words_list = input_data.prepare_words_list(FLAGS.wanted_words.split(','))
model_settings = models.prepare_model_settings(
len(words_list), FLAGS.sample_rate, FLAGS.clip_duration_ms,
FLAGS.window_size_ms, FLAGS.window_stride_ms, FLAGS.feature_bin_count,
'mfcc')
audio_processor = input_data.AudioProcessor(
'', FLAGS.data_dir, FLAGS.silence_percentage, 10,
FLAGS.wanted_words.split(','), FLAGS.validation_percentage,
FLAGS.testing_percentage, model_settings, FLAGS.data_dir)
output_audio_sample_count = FLAGS.sample_rate * FLAGS.test_duration_seconds
output_audio = np.zeros((output_audio_sample_count,), dtype=np.float32)
# Set up background audio.
background_crossover_ms = 500
background_segment_duration_ms = (
FLAGS.clip_duration_ms + background_crossover_ms)
background_segment_duration_samples = int(
(background_segment_duration_ms * FLAGS.sample_rate) / 1000)
background_segment_stride_samples = int(
(FLAGS.clip_duration_ms * FLAGS.sample_rate) / 1000)
background_ramp_samples = int(
((background_crossover_ms / 2) * FLAGS.sample_rate) / 1000)
# Mix the background audio into the main track.
how_many_backgrounds = int(
math.ceil(output_audio_sample_count / background_segment_stride_samples))
for i in range(how_many_backgrounds):
output_offset = int(i * background_segment_stride_samples)
background_index = np.random.randint(len(audio_processor.background_data))
background_samples = audio_processor.background_data[background_index]
background_offset = np.random.randint(
0, len(background_samples) - model_settings['desired_samples'])
background_volume = np.random.uniform(0, FLAGS.background_volume)
mix_in_audio_sample(output_audio, output_offset, background_samples,
background_offset, background_segment_duration_samples,
background_volume, background_ramp_samples,
background_ramp_samples)
# Mix the words into the main track, noting their labels and positions.
output_labels = []
word_stride_ms = FLAGS.clip_duration_ms + FLAGS.word_gap_ms
word_stride_samples = int((word_stride_ms * FLAGS.sample_rate) / 1000)
clip_duration_samples = int(
(FLAGS.clip_duration_ms * FLAGS.sample_rate) / 1000)
word_gap_samples = int((FLAGS.word_gap_ms * FLAGS.sample_rate) / 1000)
how_many_words = int(
math.floor(output_audio_sample_count / word_stride_samples))
all_test_data, all_test_labels = audio_processor.get_unprocessed_data(
-1, model_settings, 'testing')
for i in range(how_many_words):
output_offset = (
int(i * word_stride_samples) + np.random.randint(word_gap_samples))
output_offset_ms = (output_offset * 1000) / FLAGS.sample_rate
is_unknown = np.random.randint(100) < FLAGS.unknown_percentage
if is_unknown:
wanted_label = input_data.UNKNOWN_WORD_LABEL
else:
wanted_label = words_list[2 + np.random.randint(len(words_list) - 2)]
test_data_start = np.random.randint(len(all_test_data))
found_sample_data = None
index_lookup = np.arange(len(all_test_data), dtype=np.int32)
np.random.shuffle(index_lookup)
for test_data_offset in range(len(all_test_data)):
test_data_index = index_lookup[(
test_data_start + test_data_offset) % len(all_test_data)]
current_label = all_test_labels[test_data_index]
if current_label == wanted_label:
found_sample_data = all_test_data[test_data_index]
break
mix_in_audio_sample(output_audio, output_offset, found_sample_data, 0,
clip_duration_samples, 1.0, 500, 500)
output_labels.append({'label': wanted_label, 'time': output_offset_ms})
input_data.save_wav_file(FLAGS.output_audio_file, output_audio,
FLAGS.sample_rate)
tf.logging.info('Saved streaming test wav to %s', FLAGS.output_audio_file)
with open(FLAGS.output_labels_file, 'w') as f:
for output_label in output_labels:
f.write('%s, %f\n' % (output_label['label'], output_label['time']))
tf.logging.info('Saved streaming test labels to %s', FLAGS.output_labels_file)