experiments/STT-tensorflow
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PiperOrigin-RevId: 245449476
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Jared Duke 2019-04-26 10:56:56 -07:00 committed by TensorFlower Gardener
parent 64e5f6fd50
commit 032bda0929
9 changed files with 17 additions and 130 deletions
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1
tensorflow/examples/speech_commands/BUILD
View File

@ -48,7 +48,6 @@ py_library(
srcs_version = "PY2AND3",
deps = [
"//tensorflow:tensorflow_py",
"//tensorflow/lite/experimental/microfrontend:audio_microfrontend_py",
"//third_party/py/numpy",
"@six_archive//:six",
],

38
tensorflow/examples/speech_commands/freeze.py
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@ -49,14 +49,6 @@ import input_data
import models
from tensorflow.python.framework import graph_util
# If it's available, load the specialized feature generator. If this doesn't
# work, try building with bazel instead of running the Python script directly.
# bazel run tensorflow/examples/speech_commands:freeze_graph
try:
from tensorflow.lite.experimental.microfrontend.python.ops import audio_microfrontend_op as frontend_op # pylint:disable=g-import-not-at-top
except ImportError:
frontend_op = None
FLAGS = None
@ -78,7 +70,7 @@ def create_inference_graph(wanted_words, sample_rate, clip_duration_ms,
feature_bin_count: Number of frequency bands to analyze.
model_architecture: Name of the kind of model to generate.
preprocess: How the spectrogram is processed to produce features, for
example 'mfcc', 'average', or 'micro'.
example 'mfcc' or 'average'.
Raises:
Exception: If the preprocessing mode isn't recognized.
@ -114,33 +106,9 @@ def create_inference_graph(wanted_words, sample_rate, clip_duration_ms,
spectrogram,
sample_rate,
dct_coefficient_count=model_settings['fingerprint_width'])
elif preprocess == 'micro':
if not frontend_op:
raise Exception(
'Micro frontend op is currently not available when running TensorFlow'
' directly from Python, you need to build and run through Bazel, for'
' example'
' `bazel run tensorflow/examples/speech_commands:freeze_graph`'
)
sample_rate = model_settings['sample_rate']
window_size_ms = (model_settings['window_size_samples'] *
1000) / sample_rate
window_step_ms = (model_settings['window_stride_samples'] *
1000) / sample_rate
int16_input = tf.cast(
tf.multiply(decoded_sample_data.audio, 32767), tf.int16)
micro_frontend = frontend_op.audio_microfrontend(
int16_input,
sample_rate=sample_rate,
window_size=window_size_ms,
window_step=window_step_ms,
num_channels=model_settings['fingerprint_width'],
out_scale=1,
out_type=tf.float32)
fingerprint_input = tf.multiply(micro_frontend, (10.0 / 256.0))
else:
raise Exception('Unknown preprocess mode "%s" (should be "mfcc",'
' "average", or "micro")' % (preprocess))
raise Exception('Unknown preprocess mode "%s" (should be "mfcc" or'
' "average")' % (preprocess))
fingerprint_size = model_settings['fingerprint_size']
reshaped_input = tf.reshape(fingerprint_input, [-1, fingerprint_size])

18
tensorflow/examples/speech_commands/freeze_test.py
View File

@ -65,24 +65,6 @@ class FreezeTest(test.TestCase):
ops = [node.op for node in sess.graph_def.node]
self.assertEqual(0, ops.count('Mfcc'))
@test_util.run_deprecated_v1
def testCreateInferenceGraphWithMicro(self):
with self.cached_session() as sess:
freeze.create_inference_graph(
wanted_words='a,b,c,d',
sample_rate=16000,
clip_duration_ms=1000.0,
clip_stride_ms=30.0,
window_size_ms=30.0,
window_stride_ms=10.0,
feature_bin_count=40,
model_architecture='conv',
preprocess='micro')
self.assertIsNotNone(sess.graph.get_tensor_by_name('wav_data:0'))
self.assertIsNotNone(
sess.graph.get_tensor_by_name('decoded_sample_data:0'))
self.assertIsNotNone(sess.graph.get_tensor_by_name('labels_softmax:0'))
@test_util.run_deprecated_v1
def testFeatureBinCount(self):
with self.cached_session() as sess:

46
tensorflow/examples/speech_commands/input_data.py
View File

@ -37,13 +37,6 @@ from tensorflow.python.ops import io_ops
from tensorflow.python.platform import gfile
from tensorflow.python.util import compat
# If it's available, load the specialized feature generator. If this doesn't
# work, try building with bazel instead of running the Python script directly.
try:
from tensorflow.lite.experimental.microfrontend.python.ops import audio_microfrontend_op as frontend_op # pylint:disable=g-import-not-at-top
except ImportError:
frontend_op = None
MAX_NUM_WAVS_PER_CLASS = 2**27 - 1 # ~134M
SILENCE_LABEL = '_silence_'
SILENCE_INDEX = 0
@ -176,12 +169,9 @@ def get_features_range(model_settings):
elif model_settings['preprocess'] == 'mfcc':
features_min = -247.0
features_max = 30.0
elif model_settings['preprocess'] == 'micro':
features_min = 0.0
features_max = 26.0
else:
raise Exception('Unknown preprocess mode "%s" (should be "mfcc",'
' "average", or "micro")' % (model_settings['preprocess']))
raise Exception('Unknown preprocess mode "%s" (should be "mfcc" or'
' "average")' % (model_settings['preprocess']))
return features_min, features_max
@ -387,7 +377,6 @@ class AudioProcessor(object):
Raises:
ValueError: If the preprocessing mode isn't recognized.
Exception: If the preprocessor wasn't compiled in.
"""
with tf.get_default_graph().name_scope('data'):
desired_samples = model_settings['desired_samples']
@ -453,36 +442,9 @@ class AudioProcessor(object):
dct_coefficient_count=model_settings['fingerprint_width'])
tf.summary.image(
'mfcc', tf.expand_dims(self.output_, -1), max_outputs=1)
elif model_settings['preprocess'] == 'micro':
if not frontend_op:
raise Exception(
'Micro frontend op is currently not available when running'
' TensorFlow directly from Python, you need to build and run'
' through Bazel'
)
sample_rate = model_settings['sample_rate']
window_size_ms = (model_settings['window_size_samples'] *
1000) / sample_rate
window_step_ms = (model_settings['window_stride_samples'] *
1000) / sample_rate
int16_input = tf.cast(tf.multiply(background_clamp, 32768), tf.int16)
micro_frontend = frontend_op.audio_microfrontend(
int16_input,
sample_rate=sample_rate,
window_size=window_size_ms,
window_step=window_step_ms,
num_channels=model_settings['fingerprint_width'],
out_scale=1,
out_type=tf.float32)
self.output_ = tf.multiply(micro_frontend, (10.0 / 256.0))
tf.summary.image(
'micro',
tf.expand_dims(tf.expand_dims(self.output_, -1), 0),
max_outputs=1)
else:
raise ValueError(
'Unknown preprocess mode "%s" (should be "mfcc", '
' "average", or "micro")' % (model_settings['preprocess']))
raise ValueError('Unknown preprocess mode "%s" (should be "mfcc" or'
' "average")' % (model_settings['preprocess']))
# Merge all the summaries and write them out to /tmp/retrain_logs (by
# default)

4
tensorflow/examples/speech_commands/input_data_test.py
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@ -202,10 +202,6 @@ class InputDataTest(test.TestCase):
def testGetDataMfcc(self):
self._runGetDataTest("mfcc", 30)
@test_util.run_deprecated_v1
def testGetDataMicro(self):
self._runGetDataTest("micro", 20)
@test_util.run_deprecated_v1
def testGetUnprocessedData(self):
tmp_dir = self.get_temp_dir()

7
tensorflow/examples/speech_commands/models.py
View File

@ -71,12 +71,9 @@ def prepare_model_settings(label_count, sample_rate, clip_duration_ms,
elif preprocess == 'mfcc':
average_window_width = -1
fingerprint_width = feature_bin_count
elif preprocess == 'micro':
average_window_width = -1
fingerprint_width = feature_bin_count
else:
raise ValueError('Unknown preprocess mode "%s" (should be "mfcc",'
' "average", or "micro")' % (preprocess))
raise ValueError('Unknown preprocess mode "%s" (should be "mfcc" or'
' "average")' % (preprocess))
fingerprint_size = fingerprint_width * spectrogram_length
return {
'desired_samples': desired_samples,

2
tensorflow/examples/speech_commands/train.py
View File

@ -446,7 +446,7 @@ if __name__ == '__main__':
'--preprocess',
type=str,
default='mfcc',
help='Spectrogram processing mode. Can be "mfcc", "average", or "micro"')
help='Spectrogram processing mode. Can be "mfcc" or "average"')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

15
tensorflow/examples/speech_commands/wav_to_features.py
View File

@ -56,7 +56,7 @@ def wav_to_features(sample_rate, clip_duration_ms, window_size_ms,
window_stride_ms: How far to move in time between spectogram timeslices.
feature_bin_count: How many bins to use for the feature fingerprint.
quantize: Whether to train the model for eight-bit deployment.
preprocess: Spectrogram processing mode; "mfcc", "average" or "micro".
preprocess: Spectrogram processing mode. Can be "mfcc" or "average".
input_wav: Path to the audio WAV file to read.
output_c_file: Where to save the generated C source file.
"""
@ -86,15 +86,14 @@ def wav_to_features(sample_rate, clip_duration_ms, window_size_ms,
f.write(' * --window_stride_ms=%d \\\n' % window_stride_ms)
f.write(' * --feature_bin_count=%d \\\n' % feature_bin_count)
if quantize:
f.write(' * --quantize=1 \\\n')
f.write(' * --quantize \\\n')
f.write(' * --preprocess="%s" \\\n' % preprocess)
f.write(' * --input_wav="%s" \\\n' % input_wav)
f.write(' * --output_c_file="%s" \\\n' % output_c_file)
f.write(' */\n\n')
f.write('const int g_%s_width = %d;\n' %
(variable_base, model_settings['fingerprint_width']))
f.write('const int g_%s_height = %d;\n' %
(variable_base, model_settings['spectrogram_length']))
f.write('const int g_%s_width = %d;\n' % (variable_base, features.shape[2]))
f.write(
'const int g_%s_height = %d;\n' % (variable_base, features.shape[1]))
if quantize:
features_min, features_max = input_data.get_features_range(model_settings)
f.write('const unsigned char g_%s_data[] = {' % variable_base)
@ -109,7 +108,7 @@ def wav_to_features(sample_rate, clip_duration_ms, window_size_ms,
quantized_value = 255
if i == 0:
f.write('\n ')
f.write('%d, ' % (quantized_value))
f.write('%d, ' % quantized_value)
i = (i + 1) % 10
else:
f.write('const float g_%s_data[] = {\n' % variable_base)
@ -169,7 +168,7 @@ if __name__ == '__main__':
'--preprocess',
type=str,
default='mfcc',
help='Spectrogram processing mode. Can be "mfcc", "average", or "micro"')
help='Spectrogram processing mode. Can be "mfcc" or "average"')
parser.add_argument(
'--input_wav',
type=str,

16
tensorflow/examples/speech_commands/wav_to_features_test.py
View File

@ -66,22 +66,6 @@ class WavToFeaturesTest(test.TestCase):
content = f.read()
self.assertTrue(b"const unsigned char g_input_data" in content)
@test_util.run_deprecated_v1
def testWavToFeaturesMicro(self):
tmp_dir = self.get_temp_dir()
wav_dir = os.path.join(tmp_dir, "wavs")
os.mkdir(wav_dir)
self._saveWavFolders(wav_dir, ["a", "b", "c"], 100)
input_file_path = os.path.join(tmp_dir, "input.wav")
output_file_path = os.path.join(tmp_dir, "output.c")
wav_data = self._getWavData()
self._saveTestWavFile(input_file_path, wav_data)
wav_to_features.wav_to_features(16000, 1000, 10, 10, 40, True, "micro",
input_file_path, output_file_path)
with open(output_file_path, "rb") as f:
content = f.read()
self.assertIn(b"const unsigned char g_input_data", content)
if __name__ == "__main__":
test.main()