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222 lines
8.8 KiB
222 lines
8.8 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 deepspeech.models.ds2_online import DeepSpeech2ModelOnline
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class TestDeepSpeech2ModelOnline(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 = 161
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max_len = 210
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# (B, T, D)
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audio = np.random.randn(self.batch_size, max_len, self.feat_dim)
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audio_len = np.random.randint(max_len, size=self.batch_size)
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audio_len[-1] = 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_ds2_1(self):
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model = DeepSpeech2ModelOnline(
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feat_size=self.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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num_fc_layers=2,
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fc_layers_size_list=[512, 256],
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use_gru=False)
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loss = model(self.audio, self.audio_len, self.text, self.text_len)
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self.assertEqual(loss.numel(), 1)
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def test_ds2_2(self):
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model = DeepSpeech2ModelOnline(
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feat_size=self.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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num_fc_layers=2,
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fc_layers_size_list=[512, 256],
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use_gru=True)
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loss = model(self.audio, self.audio_len, self.text, self.text_len)
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self.assertEqual(loss.numel(), 1)
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def test_ds2_3(self):
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model = DeepSpeech2ModelOnline(
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feat_size=self.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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num_fc_layers=2,
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fc_layers_size_list=[512, 256],
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use_gru=False)
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loss = model(self.audio, self.audio_len, self.text, self.text_len)
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self.assertEqual(loss.numel(), 1)
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def test_ds2_4(self):
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model = DeepSpeech2ModelOnline(
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feat_size=self.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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num_fc_layers=2,
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fc_layers_size_list=[512, 256],
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use_gru=True)
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loss = model(self.audio, self.audio_len, self.text, self.text_len)
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self.assertEqual(loss.numel(), 1)
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def test_ds2_5(self):
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model = DeepSpeech2ModelOnline(
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feat_size=self.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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num_fc_layers=2,
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fc_layers_size_list=[512, 256],
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use_gru=False)
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loss = model(self.audio, self.audio_len, self.text, self.text_len)
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self.assertEqual(loss.numel(), 1)
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def split_into_chunk(self, x, x_lens, decoder_chunk_size, subsampling_rate,
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receptive_field_length):
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chunk_size = (decoder_chunk_size - 1
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) * subsampling_rate + receptive_field_length
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chunk_stride = subsampling_rate * decoder_chunk_size
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max_len = x.shape[1]
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assert (chunk_size <= max_len)
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x_chunk_list = []
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x_chunk_lens_list = []
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padding_len = chunk_stride - (max_len - chunk_size) % chunk_stride
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padding = paddle.zeros((x.shape[0], padding_len, x.shape[2]))
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padded_x = paddle.concat([x, padding], axis=1)
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num_chunk = (max_len + padding_len - chunk_size) / chunk_stride + 1
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num_chunk = int(num_chunk)
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for i in range(0, num_chunk):
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start = i * chunk_stride
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end = start + chunk_size
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x_chunk = padded_x[:, start:end, :]
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x_len_left = paddle.where(x_lens - i * chunk_stride < 0,
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paddle.zeros_like(x_lens),
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x_lens - i * chunk_stride)
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x_chunk_len_tmp = paddle.ones_like(x_lens) * chunk_size
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x_chunk_lens = paddle.where(x_len_left < x_chunk_len_tmp,
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x_len_left, x_chunk_len_tmp)
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x_chunk_list.append(x_chunk)
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x_chunk_lens_list.append(x_chunk_lens)
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return x_chunk_list, x_chunk_lens_list
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def test_ds2_6(self):
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model = DeepSpeech2ModelOnline(
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feat_size=self.feat_dim,
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dict_size=10,
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num_conv_layers=2,
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num_rnn_layers=1,
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rnn_size=1024,
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num_fc_layers=2,
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fc_layers_size_list=[512, 256],
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use_gru=True)
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model.eval()
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paddle.device.set_device("cpu")
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de_ch_size = 9
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audio_chunk_list, audio_chunk_lens_list = self.split_into_chunk(
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self.audio, self.audio_len, de_ch_size,
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model.encoder.conv.subsampling_rate,
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model.encoder.conv.receptive_field_length)
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eouts_prefix = None
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eouts_lens_prefix = None
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chunk_state_list = [None] * model.encoder.num_rnn_layers
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for i, audio_chunk in enumerate(audio_chunk_list):
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audio_chunk_lens = audio_chunk_lens_list[i]
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probs_pre_chunks, eouts_prefix, eouts_lens_prefix, chunk_state_list = model.decode_prob_by_chunk(
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audio_chunk, audio_chunk_lens, eouts_prefix, eouts_lens_prefix,
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chunk_state_list)
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# print (i, probs_pre_chunks.shape)
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probs, eouts, eouts_lens, final_state_list = model.decode_prob(
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self.audio, self.audio_len)
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decode_max_len = probs.shape[1]
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probs_pre_chunks = probs_pre_chunks[:, :decode_max_len, :]
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self.assertEqual(paddle.allclose(probs, probs_pre_chunks), True)
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def test_ds2_7(self):
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model = DeepSpeech2ModelOnline(
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feat_size=self.feat_dim,
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dict_size=10,
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num_conv_layers=2,
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num_rnn_layers=1,
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rnn_size=1024,
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num_fc_layers=2,
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fc_layers_size_list=[512, 256],
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use_gru=True)
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model.eval()
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paddle.device.set_device("cpu")
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de_ch_size = 9
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probs, eouts, eouts_lens, final_state_list = model.decode_prob(
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self.audio, self.audio_len)
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probs_by_chk, eouts_by_chk, eouts_lens_by_chk, final_state_list_by_chk = model.decode_prob_chunk_by_chunk(
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self.audio, self.audio_len, de_ch_size)
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decode_max_len = probs.shape[1]
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probs_by_chk = probs_by_chk[:, :decode_max_len, :]
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eouts_by_chk = eouts_by_chk[:, :decode_max_len, :]
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self.assertEqual(
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paddle.sum(
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paddle.abs(paddle.subtract(eouts_lens, eouts_lens_by_chk))), 0)
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self.assertEqual(
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paddle.sum(paddle.abs(paddle.subtract(eouts, eouts_by_chk))), 0)
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self.assertEqual(
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paddle.sum(
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paddle.abs(paddle.subtract(probs, probs_by_chk))).numpy(), 0)
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self.assertEqual(paddle.allclose(eouts_by_chk, eouts), True)
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self.assertEqual(paddle.allclose(probs_by_chk, probs), True)
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"""
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print ("conv_x", conv_x)
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print ("conv_x_by_chk", conv_x_by_chk)
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print ("final_state_list", final_state_list)
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#print ("final_state_list_by_chk", final_state_list_by_chk)
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print (paddle.sum(paddle.abs(paddle.subtract(eouts[:,:de_ch_size,:], eouts_by_chk[:,:de_ch_size,:]))))
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print (paddle.allclose(eouts[:,:de_ch_size,:], eouts_by_chk[:,:de_ch_size,:]))
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print (paddle.sum(paddle.abs(paddle.subtract(eouts[:,de_ch_size:de_ch_size*2,:], eouts_by_chk[:,de_ch_size:de_ch_size*2,:]))))
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print (paddle.allclose(eouts[:,de_ch_size:de_ch_size*2,:], eouts_by_chk[:,de_ch_size:de_ch_size*2,:]))
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print (paddle.sum(paddle.abs(paddle.subtract(eouts[:,de_ch_size*2:de_ch_size*3,:], eouts_by_chk[:,de_ch_size*2:de_ch_size*3,:]))))
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print (paddle.allclose(eouts[:,de_ch_size*2:de_ch_size*3,:], eouts_by_chk[:,de_ch_size*2:de_ch_size*3,:]))
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print (paddle.sum(paddle.abs(paddle.subtract(eouts, eouts_by_chk))))
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print (paddle.sum(paddle.abs(paddle.subtract(eouts, eouts_by_chk))))
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print (paddle.allclose(eouts[:,:,:], eouts_by_chk[:,:,:]))
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"""
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if __name__ == '__main__':
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unittest.main()
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