def _MakeLogMelFromTensorflowBuiltin(tf_wav_bytes):
sample_rate, audio = audio_lib.DecodeWav(tf_wav_bytes)
static_sample_rate = 16000
with tf.control_dependencies(
[tf.assert_equal(sample_rate, static_sample_rate)]):
log_mel = audio_lib.AudioToMfcc(static_sample_rate, audio, 25, 25, 40)
return log_mel
def testDecodeWav(self):
with open(
test_helper.test_src_dir_path('tools/testdata/gan_or_vae.wav'),
'rb') as f:
wav = f.read()
with self.session() as sess:
sample_rate, audio = sess.run(audio_lib.DecodeWav(wav))
self.assertEqual(24000, sample_rate)
self.assertEqual(75900, len(audio))
def testAudioToMfcc(self):
with open(
test_helper.test_src_dir_path('tools/testdata/gan_or_vae.wav'),
'rb') as f:
wav = f.read()
sample_rate, audio = audio_lib.DecodeWav(wav)
static_sample_rate = 24000
mfcc = audio_lib.AudioToMfcc(static_sample_rate, audio, 32, 25, 40)
with self.session() as sess:
audio_sample_rate, mfcc = sess.run([sample_rate, mfcc])
assert audio_sample_rate == static_sample_rate
self.assertAllEqual(mfcc.shape, [1, 126, 40])
def _MakeLogMel(tf_wav_bytes):
sample_rate, audio = audio_lib.DecodeWav(tf_wav_bytes)
audio *= 32768
# Remove channel dimension, since we have a single channel.
audio = tf.squeeze(audio, axis=1)
# TODO(drpng): make batches.
audio = tf.expand_dims(audio, axis=0)
static_sample_rate = 16000
mel_frontend = _CreateAsrFrontend()
with tf.control_dependencies(
[tf.assert_equal(sample_rate, static_sample_rate)]):
log_mel, _ = mel_frontend.FPropDefaultTheta(audio)
return log_mel
def _InferenceSubgraph_Default(self):
"""Constructs graph for offline inference.
Returns:
(fetches, feeds) where both fetches and feeds are dictionaries. Each
dictionary consists of keys corresponding to tensor names, and values
corresponding to a tensor in the graph which should be input/read from.
"""
p = self.params
with tf.name_scope('default'):
# TODO(laurenzo): Once the migration to integrated frontends is complete,
# this model should be upgraded to use the MelAsrFrontend in its
# params vs relying on pre-computed feature generation and the inference
# special casing.
wav_bytes = tf.placeholder(dtype=tf.string, name='wav')
frontend = self.frontend if p.frontend else None
if not frontend:
# No custom frontend. Instantiate the default.
frontend_p = asr_frontend.MelAsrFrontend.Params()
frontend = frontend_p.cls(frontend_p)
# Decode the wave bytes and use the explicit frontend.
unused_sample_rate, audio = audio_lib.DecodeWav(wav_bytes)
audio *= 32768
# Remove channel dimension, since we have a single channel.
audio = tf.squeeze(audio, axis=1)
# Add batch.
audio = tf.expand_dims(audio, axis=0)
input_batch_src = py_utils.NestedMap(src_inputs=audio,
paddings=tf.zeros_like(audio))
input_batch_src = frontend.FPropDefaultTheta(input_batch_src)
# Undo default stacking, if specified.
input_batch_src.src_inputs = tf.reshape(
input_batch_src.src_inputs, [1, -1, p.input.frame_size, 1])
input_batch_src.paddings = tf.zeros(
shape=[1, tf.shape(input_batch_src.src_inputs)[1]])
encoder_outputs = self.encoder.FPropDefaultTheta(input_batch_src)
decoder_outputs = self.decoder.BeamSearchDecode(encoder_outputs)
topk = self._GetTopK(decoder_outputs)
feeds = {'wav': wav_bytes}
fetches = {
'hypotheses': topk.decoded,
'scores': topk.scores,
'src_frames': input_batch_src.src_inputs,
'encoder_frames': encoder_outputs.encoded
}
return fetches, feeds
