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 _get_inputs(self, inputs):
inputs = [
input.array if isinstance(input, Frame) else input
for input in inputs
]
padded, lens = sequence_1d(inputs, return_len=True)
return padded, lens
def predict_lm(self, inputs, lm, beam_size: int = 100, **kwargs):
"""
Transcribe inputs using Beam Search + LM, will return list of strings.
This method will not able to utilise batch decoding, instead will do loop to decode for each elements.
Parameters
----------
input: List[np.array]
List[np.array] or List[malaya_speech.model.frame.Frame].
lm: ctc_decoders.Scorer
Returned from `malaya_speech.stt.language_model()`.
beam_size: int, optional (default=100)
beam size for beam decoder.
Returns
-------
result: List[str]
"""
try:
from ctc_decoders import ctc_beam_search_decoder
except:
raise ModuleNotFoundError(
'ctc_decoders not installed. Please install it by `pip install ctc-decoders` and try again.'
)
inputs = [
input.array if isinstance(input, Frame) else input
for input in inputs
]
padded, lens = sequence_1d(inputs, return_len=True)
logits, seq_lens = self._sess.run(
[self._softmax, self._seq_lens],
feed_dict={
self._X: padded,
self._X_len: lens
},
)
logits = np.transpose(logits, axes=(1, 0, 2))
results = []
for i in range(len(logits)):
d = ctc_beam_search_decoder(
logits[i][:seq_lens[i]],
self._vocab,
beam_size,
ext_scoring_func=lm,
**kwargs,
)
results.append(d[0][1])
return results
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)
r = self._execute(
inputs=[padded],
input_labels=['Placeholder'],
output_labels=['logits'],
)
return r['logits'][:, :, 0]
def predict(self,
inputs,
decoder: str = 'beam',
beam_size: int = 5,
**kwargs):
"""
Transcribe inputs, will return list of strings.
Parameters
----------
input: List[np.array]
List[np.array] or List[malaya_speech.model.frame.Frame].
decoder: str, optional (default='beam')
decoder mode, allowed values:
* ``'greedy'`` - greedy decoder.
* ``'beam'`` - beam decoder.
beam_size: int, optional (default=5)
beam size for beam decoder.
Returns
-------
result: List[str]
"""
decoder = self._check_decoder(decoder, beam_size)
inputs = [
input.array if isinstance(input, Frame) else input
for input in inputs
]
padded, lens = sequence_1d(inputs, return_len=True)
if decoder == 'greedy':
beam_size = 1
encoded_, padded_lens_ = self._sess.run(
[self._encoded, self._padded_lens],
feed_dict={
self._X_placeholder: padded,
self._X_len_placeholder: lens,
},
)
padded_lens_ = padded_lens_ // self._time_reduction_factor
s = self._sess.run(self._initial_states)
results = []
for i in range(len(encoded_)):
r = transducer_beam(
enc=encoded_[i],
total=padded_lens_[i],
initial_states=s,
encoded_placeholder=self._encoded_placeholder,
predicted_placeholder=self._predicted_placeholder,
states_placeholder=self._states_placeholder,
ytu=self._ytu,
new_states=self._new_states,
sess=self._sess,
beam_width=beam_size,
**kwargs,
)
results.append(subword_decode(self._vocab, r))
return results
def predict(self,
inputs,
decoder: str = 'beam',
beam_size: int = 100,
**kwargs):
"""
Transcribe inputs, will return list of strings.
Parameters
----------
input: List[np.array]
List[np.array] or List[malaya_speech.model.frame.Frame].
decoder: str, optional (default='beam')
decoder mode, allowed values:
* ``'greedy'`` - greedy decoder.
* ``'beam'`` - beam decoder.
beam_size: int, optional (default=100)
beam size for beam decoder.
Returns
-------
result: List[str]
"""
decoder = self._check_decoder(decoder, beam_size)
inputs = [
input.array if isinstance(input, Frame) else input
for input in inputs
]
padded, lens = sequence_1d(inputs, return_len=True)
if decoder == 'greedy':
beam_size = 1
if beam_size != self._beam_size:
self._beam_size = beam_size
self._decoded = tf.nn.ctc_beam_search_decoder(
self._logits,
self._seq_lens,
beam_width=self._beam_size,
top_paths=1,
merge_repeated=True,
**kwargs,
)[0][0]
r = self._sess.run(self._decoded,
feed_dict={
self._X: padded,
self._X_len: lens
})
decoded = np.zeros(r.dense_shape, dtype=np.int32)
for i in range(r.values.shape[0]):
decoded[r.indices[i][0], r.indices[i][1]] = r.values[i]
results = []
for i in range(len(decoded)):
results.append(
char_decode(decoded[i],
lookup=self._vocab).replace('<PAD>', ''))
return results