def _freq_feat_graph(feat_name, **kwargs):
winlen = kwargs.get('winlen')
winstep = kwargs.get('winstep')
feature_size = kwargs.get('feature_size')
sr = kwargs.get('sr') #pylint: disable=invalid-name
nfft = kwargs.get('nfft')
del nfft
assert feat_name in ('fbank', 'spec')
params = speech_ops.speech_params(sr=sr,
bins=feature_size,
add_delta_deltas=False,
audio_frame_length=winlen,
audio_frame_step=winstep)
graph = None
if feat_name == 'fbank':
# get session
if feat_name not in _global_sess:
graph = tf.Graph()
#pylint: disable=not-context-manager
with graph.as_default():
# fbank
filepath = tf.placeholder(dtype=tf.string,
shape=[],
name='wavpath')
waveforms, sample_rate = speech_ops.read_wav(filepath, params)
del sample_rate
fbank = speech_ops.extract_feature(waveforms, params)
# shape must be [T, D, C]
feat = tf.identity(fbank, name=feat_name)
elif feat_name == 'spec':
# magnitude spec
if feat_name not in _global_sess:
graph = tf.Graph()
#pylint: disable=not-context-manager
with graph.as_default():
filepath = tf.placeholder(dtype=tf.string,
shape=[],
name='wavpath')
waveforms, sample_rate = speech_ops.read_wav(filepath, params)
spec = py_x_ops.spectrum(
waveforms[:, 0],
tf.cast(sample_rate, tf.dtypes.float32),
output_type=1
) #output_type: 1, power spec; 2 log power spec
spec = tf.sqrt(spec)
# shape must be [T, D, C]
spec = tf.expand_dims(spec, -1)
feat = tf.identity(spec, name=feat_name)
else:
raise ValueError(f"Not support freq feat: {feat_name}.")
return graph, (_get_out_tensor_name('wavpath',
0), _get_out_tensor_name(feat_name, 0))
def extract_filterbank(*args, **kwargs):
''' tensorflow fbank feat '''
winlen = kwargs.get('winlen')
winstep = kwargs.get('winstep')
feature_size = kwargs.get('feature_size')
sr = kwargs.get('sr') #pylint: disable=invalid-name
nfft = kwargs.get('nfft')
dry_run = kwargs.get('dry_run')
del nfft
feat_name = 'fbank'
graph = None
op = None
# get session
if feat_name not in _global_sess:
graph = tf.Graph()
#pylint: disable=not-context-manager
with graph.as_default():
# fbank
params = speech_ops.speech_params(
sr=sr,
bins=feature_size,
add_delta_deltas=False,
audio_frame_length=winlen,
audio_frame_step=winstep)
filepath = tf.placeholder(dtype=tf.string, shape=[], name='wavpath')
waveforms, sample_rate = speech_ops.read_wav(filepath, params)
del sample_rate
fbank = speech_ops.extract_feature(waveforms, params)
fbank = tf.identity(fbank, name=feat_name)
sess = _get_session(_get_out_tensor_name(feat_name, 0), graph)
for wavpath in args:
savepath = os.path.splitext(wavpath)[0] + '.npy'
logging.debug('input: {}, output: {}'.format(wavpath, savepath))
feat = sess.run(feat_name + ":0", feed_dict={'wavpath:0': wavpath})
# save feat
if dry_run:
logging.info('save feat: path {} shape:{} dtype:{}'.format(
savepath, feat.shape, feat.dtype))
else:
np.save(savepath, feat)
def test_extract_feature(self):
''' test logfbank with delta, and cmvn '''
#pylint: disable=invalid-name
hp = tffeat.speech_params(sr=self.sr_true,
bins=40,
cmvn=False,
audio_desired_samples=1000,
add_delta_deltas=True)
with self.session():
wavfile = tf.constant(self.wavpath)
# read wav
audio, sample_rate = tffeat.read_wav(wavfile, hp)
del sample_rate
# fbank with delta delta and cmvn
feature = tffeat.extract_feature(audio, hp)
self.assertEqual(feature.eval().shape, (11, 40, 3))