def recognize_beam_tflite(
self,
signal,
):
"""
Function to convert to tflite using beam search decoding
Args:
signal: tf.Tensor with shape [None] indicating a single audio signal
Return:
transcript: tf.Tensor of Unicode Code Points with shape [None] and dtype tf.int32
"""
features = self.speech_featurizer.tf_extract(signal)
features = tf.expand_dims(features, axis=0)
input_length = shape_util.shape_list(features)[1]
input_length = math_util.get_reduced_length(input_length,
self.time_reduction_factor)
input_length = tf.expand_dims(input_length, axis=0)
logits = self.encoder(features, training=False)
logits = self.decoder(logits, training=False)
probs = tf.nn.softmax(logits)
decoded = tf.keras.backend.ctc_decode(
y_pred=probs,
input_length=input_length,
greedy=False,
beam_width=self.text_featurizer.decoder_config.beam_width,
)
decoded = tf.cast(decoded[0][0][0], dtype=tf.int32)
transcript = self.text_featurizer.indices2upoints(decoded)
return transcript
def call(
self,
inputs,
training=False,
**kwargs,
):
enc = self.encoder([inputs["inputs"], inputs["inputs_length"]], training=training, **kwargs)
pred = self.predict_net([inputs["predictions"], inputs["predictions_length"]], training=training, **kwargs)
logits = self.joint_net([enc, pred], training=training, **kwargs)
return data_util.create_logits(
logits=logits, logits_length=math_util.get_reduced_length(inputs["inputs_length"], self.time_reduction_factor)
)
def call(
self,
inputs,
training=False,
**kwargs,
):
logits = self.encoder(inputs["inputs"], training=training, **kwargs)
logits = self.decoder(logits, training=training, **kwargs)
return data_util.create_logits(
logits=logits,
logits_length=math_util.get_reduced_length(
inputs["inputs_length"], self.time_reduction_factor),
)
def recognize(
self,
inputs: Dict[str, tf.Tensor],
):
"""
RNN Transducer Greedy decoding
Args:
features (tf.Tensor): a batch of padded extracted features
Returns:
tf.Tensor: a batch of decoded transcripts
"""
encoded = self.encoder([inputs["inputs"], inputs["inputs_length"]], training=False)
encoded_length = math_util.get_reduced_length(inputs["inputs_length"], self.time_reduction_factor)
return self._perform_greedy_batch(encoded=encoded, encoded_length=encoded_length)
def recognize(
self,
inputs: Dict[str, tf.Tensor],
):
"""
RNN Transducer Greedy decoding
Args:
features (tf.Tensor): a batch of padded extracted features
Returns:
tf.Tensor: a batch of decoded transcripts
"""
batch_size, _, _, _ = shape_util.shape_list(inputs["inputs"])
encoded, _ = self.encoder.recognize(inputs["inputs"], self.encoder.get_initial_state(batch_size))
encoded_length = math_util.get_reduced_length(inputs["inputs_length"], self.time_reduction_factor)
return self._perform_greedy_batch(encoded=encoded, encoded_length=encoded_length)
def recognize_beam(
self,
inputs: Dict[str, tf.Tensor],
lm: bool = False,
):
"""
RNN Transducer Beam Search
Args:
features (tf.Tensor): a batch of padded extracted features
lm (bool, optional): whether to use language model. Defaults to False.
Returns:
tf.Tensor: a batch of decoded transcripts
"""
encoded = self.encoder([inputs["inputs"], inputs["inputs_length"]], training=False)
encoded_length = math_util.get_reduced_length(inputs["inputs_length"], self.time_reduction_factor)
return self._perform_beam_search_batch(encoded=encoded, encoded_length=encoded_length, lm=lm)
def call(
self,
inputs,
training=False,
**kwargs,
):
features, input_length = inputs
outputs = features
for conv in self.convs:
outputs = conv(outputs, training=training)
outputs = self.last_conv(outputs, training=training)
input_length = math_util.get_reduced_length(input_length,
self.last_conv.strides)
outputs = self.se([outputs, input_length], training=training)
if self.residual is not None:
res = self.residual(features, training=training)
outputs = tf.add(outputs, res)
outputs = self.activation(outputs)
return outputs, input_length
args.subwords)
else:
text_featurizer = CharFeaturizer(config.decoder_config)
text_featurizer.decoder_config.beam_width = args.beam_width
# build model
conformer = Conformer(**config.model_config,
vocabulary_size=text_featurizer.num_classes)
conformer.make(speech_featurizer.shape)
conformer.load_weights(args.saved, by_name=True, skip_mismatch=True)
conformer.summary(line_length=120)
conformer.add_featurizers(speech_featurizer, text_featurizer)
signal = read_raw_audio(args.filename)
features = speech_featurizer.tf_extract(signal)
input_length = math_util.get_reduced_length(
tf.shape(features)[0], conformer.time_reduction_factor)
if args.beam_width:
transcript = conformer.recognize_beam(features[None, ...],
input_length[None, ...])
print("Transcript:", transcript[0].numpy().decode("UTF-8"))
elif args.timestamp:
transcript, stime, etime, _, _ = conformer.recognize_tflite_with_timestamp(
signal, tf.constant(text_featurizer.blank, dtype=tf.int32),
conformer.predict_net.get_initial_state())
print("Transcript:", transcript)
print("Start time:", stime)
print("End time:", etime)
else:
transcript, _, _ = conformer.recognize_tflite(
signal, tf.constant(text_featurizer.blank, dtype=tf.int32),