features = np.load(args.filename).reshape([-1, 80, 1])
features = tf.constant(features)
input_length = 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:
if args.filename.endswith('.wav'):
transcript, _, _ = conformer.recognize_tflite(
signal, tf.constant(text_featurizer.blank, dtype=tf.int32), conformer.predict_net.get_initial_state())
print("Transcript:", tf.strings.unicode_encode(transcript, "UTF-8").numpy().decode("UTF-8"))
else:
encoded = conformer.encoder_inference(features)
hypothesis = conformer._perform_greedy(
encoded,
tf.shape(encoded)[0],
tf.constant(text_featurizer.blank, dtype=tf.int32),
conformer.predict_net.get_initial_state())
transcript = conformer.text_featurizer.indices2upoints(hypothesis.prediction)
print("Transcript:", tf.strings.unicode_encode(transcript, "UTF-8").numpy().decode("UTF-8"))
help="Whether to only use cpu")
args = parser.parse_args()
setup_devices([args.device], cpu=args.cpu)
from tensorflow_asr.configs.config import Config
from tensorflow_asr.featurizers.speech_featurizers import read_raw_audio
from tensorflow_asr.featurizers.speech_featurizers import TFSpeechFeaturizer
from tensorflow_asr.featurizers.text_featurizers import CharFeaturizer
from tensorflow_asr.models.conformer import Conformer
config = Config(args.config, learning=False)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
text_featurizer = CharFeaturizer(config.decoder_config)
# build model
conformer = Conformer(**config.model_config, vocabulary_size=text_featurizer.num_classes)
conformer._build(speech_featurizer.shape)
conformer.load_weights(args.saved, by_name=True)
conformer.summary(line_length=120)
conformer.add_featurizers(speech_featurizer, text_featurizer)
signal = read_raw_audio(args.filename)
predicted = tf.constant(args.blank, dtype=tf.int32)
states = tf.zeros([args.num_rnns, args.nstates, 1, args.statesize], dtype=tf.float32)
hyp, _, _ = conformer.recognize_tflite(signal, predicted, states)
print("".join([chr(u) for u in hyp]))
