# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # 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 # # http://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. import paddle import numpy as np import unittest from deepspeech.models.u2 import U2TransformerModel from deepspeech.models.u2 import U2ConformerModel class TestU2Model(unittest.TestCase): def setUp(self): batch_size = 2 feat_dim = 161 max_len = 100 audio = np.random.randn(batch_size, feat_dim, max_len) audio_len = np.random.randint(100, size=batch_size, dtype='int32') audio_len[-1] = 100 text = np.array([[1, 2], [1, 2]], dtype='int32') text_len = np.array([2] * batch_size, dtype='int32') self.audio = paddle.to_tensor(audio, dtype='float32') self.audio_len = paddle.to_tensor(audio_len, dtype='int64') self.text = paddle.to_tensor(text, dtype='int32') self.text_len = paddle.to_tensor(text_len, dtype='int64') print(audio.shape) print(audio_len.shape) print(text.shape) print(text_len.shape) print("-----------------") def test_ds2_1(self): model = DeepSpeech2Model( feat_size=feat_dim, dict_size=10, num_conv_layers=2, num_rnn_layers=3, rnn_size=1024, use_gru=False, share_rnn_weights=False, ) logits, probs, logits_len = model(self.audio, self.audio_len, self.text, self.text_len) print('probs.shape', probs.shape) print("-----------------") if __name__ == '__main__': unittest.main()