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148 lines
5.4 KiB
148 lines
5.4 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 unittest
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import numpy as np
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import paddle
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from yacs.config import CfgNode as CN
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from paddlespeech.s2t.models.u2 import U2Model
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from paddlespeech.s2t.utils.layer_tools import summary
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class TestU2Model(unittest.TestCase):
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def setUp(self):
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paddle.set_device('cpu')
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self.batch_size = 2
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self.feat_dim = 83
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self.max_len = 64
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self.vocab_size = 4239
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#(B, T, D)
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audio = np.random.randn(self.batch_size, self.max_len, self.feat_dim)
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audio_len = np.random.randint(self.max_len, size=self.batch_size)
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audio_len[-1] = self.max_len
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#(B, U)
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text = np.array([[1, 2], [1, 2]])
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text_len = np.array([2] * self.batch_size)
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self.audio = paddle.to_tensor(audio, dtype='float32')
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self.audio_len = paddle.to_tensor(audio_len, dtype='int64')
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self.text = paddle.to_tensor(text, dtype='int32')
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self.text_len = paddle.to_tensor(text_len, dtype='int64')
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def test_transformer(self):
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conf_str = """
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# network architecture
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# encoder related
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encoder: transformer
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder architecture type
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normalize_before: true
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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"""
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cfg = CN().load_cfg(conf_str)
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cfg.input_dim = self.feat_dim
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cfg.output_dim = self.vocab_size
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cfg.cmvn_file = None
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cfg.cmvn_file_type = 'npz'
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cfg.freeze()
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model = U2Model(cfg)
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summary(model, None)
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total_loss, attention_loss, ctc_loss = model(self.audio, self.audio_len,
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self.text, self.text_len)
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self.assertEqual(total_loss.numel(), 1)
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self.assertEqual(attention_loss.numel(), 1)
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self.assertEqual(ctc_loss.numel(), 1)
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def test_conformer(self):
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conf_str = """
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# network architecture
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# encoder related
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encoder: conformer
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
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normalize_before: true
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cnn_module_kernel: 15
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use_cnn_module: True
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activation_type: 'swish'
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pos_enc_layer_type: 'rel_pos'
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selfattention_layer_type: 'rel_selfattn'
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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"""
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cfg = CN().load_cfg(conf_str)
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cfg.input_dim = self.feat_dim
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cfg.output_dim = self.vocab_size
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cfg.cmvn_file = None
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cfg.cmvn_file_type = 'npz'
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cfg.freeze()
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model = U2Model(cfg)
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summary(model, None)
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total_loss, attention_loss, ctc_loss = model(self.audio, self.audio_len,
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self.text, self.text_len)
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self.assertEqual(total_loss.numel(), 1)
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self.assertEqual(attention_loss.numel(), 1)
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self.assertEqual(ctc_loss.numel(), 1)
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if __name__ == '__main__':
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unittest.main()
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