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# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A demo script showing how to use the uisrnn package on toy data."""
import numpy as np
import uisrnn
SAVED_MODEL_NAME = 'saved_model.uisrnn'
def diarization_experiment(model_args, training_args, inference_args):
"""Experiment pipeline.
Load data --> train model --> test model --> output result
Args:
model_args: model configurations
training_args: training configurations
inference_args: inference configurations
"""
predicted_cluster_ids = []
test_record = []
train_data = np.load('./data/toy_training_data.npz', allow_pickle=True)
test_data = np.load('./data/toy_testing_data.npz', allow_pickle=True)
train_sequence = train_data['train_sequence']
train_cluster_id = train_data['train_cluster_id']
test_sequences = test_data['test_sequences'].tolist()
test_cluster_ids = test_data['test_cluster_ids'].tolist()
model = uisrnn.UISRNN(model_args)
# Training.
# If we have saved a mode previously, we can also skip training by
# calling:
# model.load(SAVED_MODEL_NAME)
model.fit(train_sequence, train_cluster_id, training_args)
model.save(SAVED_MODEL_NAME)
# Testing.
# You can also try uisrnn.parallel_predict to speed up with GPU.
# But that is a beta feature which is not thoroughly tested, so
# proceed with caution.
for (test_sequence, test_cluster_id) in zip(test_sequences, test_cluster_ids):
predicted_cluster_id = model.predict(test_sequence, inference_args)
predicted_cluster_ids.append(predicted_cluster_id)
accuracy = uisrnn.compute_sequence_match_accuracy(
test_cluster_id, predicted_cluster_id)
test_record.append((accuracy, len(test_cluster_id)))
print('Ground truth labels:')
print(test_cluster_id)
print('Predicted labels:')
print(predicted_cluster_id)
print('-' * 80)
output_string = uisrnn.output_result(model_args, training_args, test_record)
print('Finished diarization experiment')
print(output_string)
def main():
"""The main function."""
model_args, training_args, inference_args = uisrnn.parse_arguments()
diarization_experiment(model_args, training_args, inference_args)
if __name__ == '__main__':
main()
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