You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
148 lines
5.3 KiB
148 lines
5.3 KiB
# 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 deepspeech.models.u2 import U2Model
|
|
from deepspeech.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()
|