def record_and_write(audio_processor, hyper_params):
import pyaudio
_CHUNK = hyper_params["max_input_seq_length"]
_SR = 22050
p = pyaudio.PyAudio()
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1,
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
hyper_params["char_map_length"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/")
# Create stream of listening
stream = p.open(format=pyaudio.paInt16, channels=1, rate=_SR, input=True, frames_per_buffer=_CHUNK)
print("NOW RECORDING...")
while True:
data = stream.read(_CHUNK)
data = np.fromstring(data)
feat_vec, original_feat_vec_length = audio_processor.process_signal(data, _SR)
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
predictions = model.process_input(sess, feat_vec, [original_feat_vec_length])
result = [dataprocessor.DataProcessor.get_labels_str(hyper_params["char_map"], prediction)
for prediction in predictions]
print(result, end="")
def process_file(audio_processor, hyper_params, file):
feat_vec, original_feat_vec_length = audio_processor.process_audio_file(file)
if original_feat_vec_length > hyper_params["max_input_seq_length"]:
logging.warning("File too long")
return
elif original_feat_vec_length < hyper_params["max_input_seq_length"]:
# Pad the feat_vec with zeros
pad_length = hyper_params["max_input_seq_length"] - original_feat_vec_length
padding = np.zeros((pad_length, hyper_params["input_dim"]), dtype=np.float)
feat_vec = np.concatenate((feat_vec, padding), 0)
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1,
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
hyper_params["char_map_length"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/")
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
predictions = model.process_input(sess, feat_vec, [original_feat_vec_length])
transcribed_text = [dataprocessor.DataProcessor.get_labels_str(hyper_params["char_map"], prediction)
for prediction in predictions]
print(transcribed_text[0])
def record_and_write(audio_processor, hyper_params):
import pyaudio
_CHUNK = hyper_params["max_input_seq_length"]
_SR = 22050
p = pyaudio.PyAudio()
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1,
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
language=hyper_params["language"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"])
# Create stream of listening
stream = p.open(format=pyaudio.paInt16, channels=1, rate=_SR, input=True, frames_per_buffer=_CHUNK)
print("NOW RECORDING...")
while True:
data = stream.read(_CHUNK)
data = np.fromstring(data)
feat_vec, original_feat_vec_length = audio_processor.process_signal(data, _SR)
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
result = model.process_input(sess, feat_vec, [original_feat_vec_length])
print(result, end="")
def process_file(audio_processor, hyper_params, file):
feat_vec, original_feat_vec_length = audio_processor.process_audio_file(file)
if original_feat_vec_length > hyper_params["max_input_seq_length"]:
logging.warning("File too long")
return
elif original_feat_vec_length < hyper_params["max_input_seq_length"]:
# Pad the feat_vec with zeros
pad_length = hyper_params["max_input_seq_length"] - original_feat_vec_length
padding = np.zeros((pad_length, hyper_params["input_dim"]), dtype=np.float)
feat_vec = np.concatenate((feat_vec, padding), 0)
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1,
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
hyper_params["char_map_length"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/")
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
predictions = model.process_input(sess, feat_vec, [original_feat_vec_length])
transcribed_text = [dataprocessor.DataProcessor.get_labels_str(hyper_params["char_map"], prediction)
for prediction in predictions]
print(transcribed_text[0])
def process_file(audio_processor, hyper_params, file):
feat_vec, original_feat_vec_length = audio_processor.process_audio_file(file)
if original_feat_vec_length > hyper_params["max_input_seq_length"]:
logging.warning("File too long")
return
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1,
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
language=hyper_params["language"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"])
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
transcribed_text = model.process_input(sess, feat_vec, [original_feat_vec_length])
print(transcribed_text[0])
