def evaluate(hyper_params): if hyper_params["test_dataset_dirs"] is None: logging.fatal("Setting test_dataset_dirs in config file is mandatory for evaluation mode") return # Load the test set data data_processor = dataprocessor.DataProcessor(hyper_params["test_dataset_dirs"]) test_set = data_processor.get_dataset() logging.info("Using %d size of test set", len(test_set)) if len(test_set) == 0: logging.fatal("No files in test set during an evaluation mode") return with tf.Session() as sess: # create model model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"], 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/") wer, cer = model.evaluate_full(sess, test_set, hyper_params["max_input_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) print("Resulting WER : {0:.3g} %".format(wer)) print("Resulting CER : {0:.3g} %".format(cer)) return
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 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 evaluate(hyper_params): if hyper_params["test_dataset_dirs"] is None: logging.fatal("Setting test_dataset_dirs in config file is mandatory for evaluation mode") return # Load the test set data data_processor = dataprocessor.DataProcessor(hyper_params["test_dataset_dirs"]) test_set = data_processor.get_dataset() logging.info("Using %d size of test set", len(test_set)) if len(test_set) == 0: logging.fatal("No files in test set during an evaluation mode") return with tf.Session() as sess: # create model model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"], 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"]) wer, cer = model.evaluate_full(sess, test_set, hyper_params["max_input_seq_length"], hyper_params["signal_processing"]) print("Resulting WER : {0:.3g} %".format(wer)) print("Resulting CER : {0:.3g} %".format(cer)) return
def build_acoustic_training_rnn(is_chief, is_ditributed, sess, hyper_params, prog_params, train_set, test_set): model = AcousticModel( hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"], 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"]) # Create a Dataset from the train_set and the test_set train_dataset = model.build_dataset(train_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) train_dataset = train_dataset.shuffle(10, reshuffle_each_iteration=True) v_iterator = None if test_set is []: t_iterator = model.add_dataset_input(train_dataset) else: test_dataset = model.build_dataset( test_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) # Build the input stream from the different datasets t_iterator, v_iterator = model.add_datasets_input( train_dataset, test_dataset) # Create the model #tensorboard_dir model.create_training_rnn(is_chief, is_ditributed, hyper_params["dropout_input_keep_prob"], hyper_params["dropout_output_keep_prob"], hyper_params["grad_clip"], hyper_params["learning_rate"], hyper_params["lr_decay_factor"], use_iterator=True) model.add_tensorboard(sess, prog_params["train_dir"], prog_params["timeline"]) sv = None if is_ditributed: init_op = tf.global_variables_initializer() sv = tf.train.Supervisor(is_chief=is_chief, logdir=prog_params["train_dir"], init_op=init_op, recovery_wait_secs=1, summary_op=None, global_step=model.global_step) model.supervisor = sv else: model.initialize(sess) model.restore(sess, prog_params["train_dir"]) # Override the learning rate if given on the command line if prog_params["learn_rate"] is not None: model.set_learning_rate(sess, prog_params["learn_rate"]) return sv, model, t_iterator, v_iterator
def build_acoustic_training_rnn(sess, hyper_params, prog_params, train_set, test_set): model = AcousticModel( hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"], 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"]) # Create a Dataset from the train_set and the test_set train_dataset = model.build_dataset(train_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) v_iterator = None if test_set is []: t_iterator = model.add_dataset_input(train_dataset) sess.run(t_iterator.initializer) else: test_dataset = model.build_dataset( test_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) # Build the input stream from the different datasets t_iterator, v_iterator = model.add_datasets_input( train_dataset, test_dataset) sess.run(t_iterator.initializer) sess.run(v_iterator.initializer) # Create the model model.create_training_rnn(hyper_params["dropout_input_keep_prob"], hyper_params["dropout_output_keep_prob"], hyper_params["grad_clip"], hyper_params["learning_rate"], hyper_params["lr_decay_factor"], use_iterator=True) model.add_tensorboard(sess, hyper_params["tensorboard_dir"], prog_params["tb_name"], prog_params["timeline"]) model.initialize(sess) model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/") # Override the learning rate if given on the command line if prog_params["learn_rate"] is not None: model.set_learning_rate(sess, prog_params["learn_rate"]) return model, t_iterator, v_iterator
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])
def build_acoustic_training_rnn(sess, hyper_params, prog_params, train_set, test_set): model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"], 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"]) # Create a Dataset from the train_set and the test_set train_dataset = model.build_dataset(train_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) v_iterator = None if test_set is []: t_iterator = model.add_dataset_input(train_dataset) sess.run(t_iterator.initializer) else: test_dataset = model.build_dataset(test_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) # Build the input stream from the different datasets t_iterator, v_iterator = model.add_datasets_input(train_dataset, test_dataset) sess.run(t_iterator.initializer) sess.run(v_iterator.initializer) # Create the model model.create_training_rnn(hyper_params["dropout_input_keep_prob"], hyper_params["dropout_output_keep_prob"], hyper_params["grad_clip"], hyper_params["learning_rate"], hyper_params["lr_decay_factor"], use_iterator=True) model.add_tensorboard(sess, hyper_params["tensorboard_dir"], prog_params["tb_name"], prog_params["timeline"]) model.initialize(sess) model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/") # Override the learning rate if given on the command line if prog_params["learn_rate"] is not None: model.set_learning_rate(sess, prog_params["learn_rate"]) return model, t_iterator, v_iterator