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106 lines
3.3 KiB
106 lines
3.3 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import numpy as np
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from deepspeech.models.network import DeepSpeech2
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if __name__ == '__main__':
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batch_size = 2
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feat_dim = 161
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max_len = 100
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audio = np.random.randn(batch_size, feat_dim, max_len)
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audio_len = np.random.randint(100, size=batch_size, dtype='int32')
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audio_len[-1] = 100
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text = np.array([[1, 2], [1, 2]], dtype='int32')
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text_len = np.array([2] * batch_size, dtype='int32')
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place = paddle.CUDAPlace(0)
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audio = paddle.to_tensor(
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audio, dtype='float32', place=place, stop_gradient=True)
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audio_len = paddle.to_tensor(
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audio_len, dtype='int64', place=place, stop_gradient=True)
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text = paddle.to_tensor(
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text, dtype='int32', place=place, stop_gradient=True)
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text_len = paddle.to_tensor(
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text_len, dtype='int64', place=place, stop_gradient=True)
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print(audio.shape)
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print(audio_len.shape)
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print(text.shape)
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print(text_len.shape)
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print("-----------------")
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model = DeepSpeech2(
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feat_size=feat_dim,
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dict_size=10,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=False,
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share_rnn_weights=False, )
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logits, probs, logits_len = model(audio, text, audio_len, text_len)
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print('probs.shape', probs.shape)
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print("-----------------")
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model2 = DeepSpeech2(
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feat_size=feat_dim,
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dict_size=10,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=True,
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share_rnn_weights=False, )
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logits, probs, logits_len = model2(audio, text, audio_len, text_len)
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print('probs.shape', probs.shape)
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print("-----------------")
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model3 = DeepSpeech2(
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feat_size=feat_dim,
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dict_size=10,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=False,
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share_rnn_weights=True, )
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logits, probs, logits_len = model3(audio, text, audio_len, text_len)
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print('probs.shape', probs.shape)
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print("-----------------")
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model4 = DeepSpeech2(
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feat_size=feat_dim,
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dict_size=10,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=True,
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share_rnn_weights=True, )
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logits, probs, logits_len = model4(audio, text, audio_len, text_len)
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print('probs.shape', probs.shape)
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print("-----------------")
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model5 = DeepSpeech2(
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feat_size=feat_dim,
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dict_size=10,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=False,
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share_rnn_weights=False, )
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logits, probs, logits_len = model5(audio, text, audio_len, text_len)
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print('probs.shape', probs.shape)
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print("-----------------")
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