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PaddleSpeech/tests/unit/asr/u2_model_test.py

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