def predict(self, inputs):
"""
Change Mel to Waveform.
Parameters
----------
inputs: List[np.array]
List[np.array] or List[malaya_speech.model.frame.Frame].
Returns
-------
result: List
"""
inputs = [
input.array if isinstance(input, Frame) else input
for input in inputs
]
padded, lens = sequence_1d(inputs, return_len=True)
if self._output_nodes:
r = self._sess.run(
self._output_nodes['logits'],
feed_dict={self._input_nodes['Placeholder']: padded},
)
else:
inputs = to_tf(self._eager_g.inputs, [padded])
r = self._eager_g(**inputs)[0]
return r[:, :, 0]
def predict(self, string, **kwargs):
"""
Change string to Mel.
Parameters
----------
string: str
Returns
-------
result: Dict[string, decoder-output, postnet-output, alignment]
"""
t, ids = self._normalizer.normalize(string, **kwargs)
if self._output_nodes:
r = self._sess.run(
self._output_nodes,
feed_dict={
self._input_nodes['Placeholder']: [ids],
self._input_nodes['Placeholder_1']: [len(ids)],
},
)
return {
'string': t,
'ids': ids,
'decoder-output': r['decoder_output'][0],
'postnet-output': r['post_mel_outputs'][0],
'alignment': r['alignment_histories'][0],
}
else:
inputs = to_tf(
self._eager_g.inputs,
[np.array([ids]), np.array([len(ids)])])
r = self._eager_g(**inputs)
return r
def predict(self, input):
"""
Enhance inputs, will return waveform.
Parameters
----------
input: np.array
np.array or malaya_speech.model.frame.Frame.
Returns
-------
result: np.array
"""
if isinstance(input, Frame):
input = input.array
if self._output_nodes:
r = self._sess.run(
self._output_nodes['logits'],
feed_dict={self._input_nodes['Placeholder']: input},
)
else:
inputs = to_tf(self._eager_g.inputs, [input])
r = self._eager_g(**inputs)[0].numpy()
return r
def vectorize(self, inputs):
"""
Vectorize inputs.
Parameters
----------
inputs: List[np.array]
List[np.array] or List[malaya_speech.model.frame.Frame].
Returns
-------
result: np.array
returned [B, D].
"""
inputs = [
input.array if isinstance(input, Frame) else input
for input in inputs
]
inputs = [self._vectorizer(input) for input in inputs]
inputs, lengths = padding_sequence_nd(inputs, dim=0, return_len=True)
if self._output_nodes:
r = self._sess.run(
self._output_nodes['logits'],
feed_dict={
self._input_nodes['Placeholder']: inputs,
self._input_nodes['Placeholder_1']: lengths,
},
)
else:
inputs = to_tf(self._eager_g.inputs, [inputs, lengths])
r = self._eager_g(**inputs)[0].numpy()
return r
def predict(self, input):
"""
Enhance inputs, will return waveform.
Parameters
----------
input: np.array
np.array or malaya_speech.model.frame.Frame.
Returns
-------
result: Dict
"""
if isinstance(input, Frame):
input = input.array
if self._output_nodes:
r = self._sess.run(
self._output_nodes,
feed_dict={self._input_nodes['Placeholder']: input},
)
else:
inputs = to_tf(self._eager_g.inputs, [input])
r = self._eager_g(**inputs)
r = {f'logits_{no}': i.numpy() for no, i in enumerate(r)}
results = {}
for no, instrument in enumerate(self._instruments):
results[instrument] = r[f'logits_{no}']
return results
def predict(self, inputs):
"""
Enhance inputs, will return melspectrogram.
Parameters
----------
inputs: List[np.array]
Returns
-------
result: List
"""
inputs = [
input.array if isinstance(input, Frame) else input
for input in inputs
]
mels = [featurization.scale_mel(s).T for s in inputs]
x, lens = padding_sequence_nd(mels, maxlen=256, dim=0, return_len=True)
if self._output_nodes:
l = self._sess.run(
self._output_nodes['logits'],
feed_dict={self._input_nodes['Placeholder']: x},
)
else:
inputs = to_tf(self._eager_g.inputs, [x])
l = self._eager_g(**inputs)[0].numpy()
results = []
for index in range(len(x)):
results.append(
featurization.unscale_mel(x[index, :lens[index]].T +
l[index, :lens[index], :, 0].T))
return results
def predict(self, original_audio, target_audio):
"""
Change original voice audio to follow targeted voice.
Parameters
----------
original_audio: np.array or malaya_speech.model.frame.Frame
target_audio: np.array or malaya_speech.model.frame.Frame
Returns
-------
result: Dict[decoder-output, postnet-output]
"""
original_audio = (input.array if isinstance(original_audio, Frame) else
original_audio)
target_audio = (input.array
if isinstance(target_audio, Frame) else target_audio)
original_mel = self._waveform_to_mel(original_audio)
target_mel = self._waveform_to_mel(target_audio)
original_v = self._speaker_vector([original_audio])[0] * 30 - 3.5
target_v = self._speaker_vector([target_audio])[0] * 30 - 3.5
if self._output_nodes:
r = self._sess.run(
self._output_nodes,
feed_dict={
self._input_nodes['mel']: [original_mel],
self._input_nodes['ori_vector']: [original_v],
self._input_nodes['target_vector']: [target_v],
self._input_nodes['mel_lengths']: [len(original_mel)],
},
)
r = {
'decoder-output': r['mel_before'][0],
'postnet-output': r['mel_after'][0],
}
else:
inputs = to_tf(
self._eager_g.inputs,
[
np.array([original_mel]),
np.array([original_v]),
np.array([target_v]),
np.array([len(original_mel)]),
],
)
r = self._eager_g(**inputs)
r = {
'decoder-output': r[0][0].numpy(),
'postnet-output': r[1][0].numpy(),
}
return r
def predict_proba(self, inputs):
"""
Predict inputs, will return probability.
Parameters
----------
inputs: List[np.array]
List[np.array] or List[malaya_speech.model.frame.Frame].
Returns
-------
result: np.array
returned [B, D].
"""
inputs = [
input.array if isinstance(input, Frame) else input
for input in inputs
]
inputs = [self._vectorizer(input, **self._extra) for input in inputs]
if self.__model__ == 'deep-speaker':
dim = 0
else:
dim = 1
inputs = padding_sequence_nd(inputs, dim=dim)
inputs = np.expand_dims(inputs, -1)
if self._output_nodes:
r = self._sess.run(
self._output_nodes['logits'],
feed_dict={self._input_nodes['Placeholder']: inputs},
)
else:
inputs = to_tf(self._eager_g.inputs, [inputs])
r = self._eager_g(**inputs)[0].numpy()
return r