# Fix random seeds and number of threads
np.random.seed(42)
tf.random.set_seed(42)
tf.config.threading.set_inter_op_parallelism_threads(args.threads)
tf.config.threading.set_intra_op_parallelism_threads(args.threads)
# Create logdir name
args.logdir = os.path.join(
"logs", "{}-{}-{}".format(
os.path.basename(__file__),
datetime.datetime.now().strftime("%Y-%m-%d_%H%M%S"), ",".join(
("{}={}".format(re.sub("(.)[^_]*_?", r"\1", key), value)
for key, value in sorted(vars(args).items())))))
# Load the data
timit = TimitMFCC()
# Create the network and train
network = Network(args)
for epoch in range(args.epochs):
network.train_epoch(timit.train, args)
network.evaluate(timit.dev, "dev", args)
print(f"Epoch {epoch+1} done.")
# Generate test set annotations, but to allow parallel execution, create it
# in in args.logdir if it exists.
out_path = "speech_recognition_test.txt"
if os.path.isdir(args.logdir):
out_path = os.path.join(args.logdir, out_path)
with open(out_path, "w", encoding="utf-8") as out_file:
for sentence in network.predict(timit.test, args):
a[i - 1][j - 1] + (x[i - 1] != y[j - 1]))
return a[-1][-1]
if __name__ == "__main__":
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument("predictions",
type=str,
help="Path to predicted output.")
parser.add_argument("dataset",
type=str,
help="Which dataset to evaluate ('dev', 'test').")
args = parser.parse_args([] if "__file__" not in globals() else None)
gold = getattr(TimitMFCC(), args.dataset).data["letters"]
with open(args.predictions, "r", encoding="utf-8") as predictions_file:
predictions = [line.rstrip("\n") for line in predictions_file]
if len(predictions) < len(gold):
raise RuntimeError(
"The predictions are shorter than gold data: {} vs {}.".format(
len(predictions), len(gold)))
score = 0
for i in range(len(gold)):
gold_sentence = [TimitMFCC.LETTERS[letter] for letter in gold[i]]
predicted_sentence = predictions[i].split(" ")
score += edit_distance(gold_sentence,
predicted_sentence) / len(gold_sentence)