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263 lines
9.7 KiB
263 lines
9.7 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 os
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import pickle
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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 paddle import inference
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from paddlespeech.s2t.models.ds2 import DeepSpeech2InferModel
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from paddlespeech.s2t.models.ds2 import DeepSpeech2Model
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class TestDeepSpeech2Model(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 = DeepSpeech2Model(
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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 = DeepSpeech2Model(
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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 = DeepSpeech2Model(
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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 = DeepSpeech2Model(
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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 = DeepSpeech2Model(
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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_6(self):
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model = DeepSpeech2Model(
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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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rnn_direction='bidirect',
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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_7(self):
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use_gru = False
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model = DeepSpeech2Model(
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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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rnn_direction='forward',
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num_fc_layers=2,
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fc_layers_size_list=[512, 256],
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use_gru=use_gru)
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model.eval()
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paddle.device.set_device("cpu")
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de_ch_size = 8
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eouts, eouts_lens, final_state_h_box, final_state_c_box = model.encoder(
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self.audio, self.audio_len)
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eouts_by_chk_list, eouts_lens_by_chk_list, final_state_h_box_chk, final_state_c_box_chk = model.encoder.forward_chunk_by_chunk(
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self.audio, self.audio_len, de_ch_size)
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eouts_by_chk = paddle.concat(eouts_by_chk_list, axis=1)
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eouts_lens_by_chk = paddle.add_n(eouts_lens_by_chk_list)
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decode_max_len = eouts.shape[1]
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eouts_by_chk = eouts_by_chk[:, :decode_max_len, :]
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self.assertEqual(paddle.allclose(eouts_by_chk, eouts), True)
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self.assertEqual(
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paddle.allclose(final_state_h_box, final_state_h_box_chk), True)
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if use_gru is False:
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self.assertEqual(
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paddle.allclose(final_state_c_box, final_state_c_box_chk), True)
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def test_ds2_8(self):
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use_gru = True
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model = DeepSpeech2Model(
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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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rnn_direction='forward',
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num_fc_layers=2,
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fc_layers_size_list=[512, 256],
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use_gru=use_gru)
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model.eval()
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paddle.device.set_device("cpu")
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de_ch_size = 8
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eouts, eouts_lens, final_state_h_box, final_state_c_box = model.encoder(
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self.audio, self.audio_len)
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eouts_by_chk_list, eouts_lens_by_chk_list, final_state_h_box_chk, final_state_c_box_chk = model.encoder.forward_chunk_by_chunk(
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self.audio, self.audio_len, de_ch_size)
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eouts_by_chk = paddle.concat(eouts_by_chk_list, axis=1)
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eouts_lens_by_chk = paddle.add_n(eouts_lens_by_chk_list)
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decode_max_len = eouts.shape[1]
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eouts_by_chk = eouts_by_chk[:, :decode_max_len, :]
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self.assertEqual(paddle.allclose(eouts_by_chk, eouts), True)
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self.assertEqual(
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paddle.allclose(final_state_h_box, final_state_h_box_chk), True)
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if use_gru is False:
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self.assertEqual(
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paddle.allclose(final_state_c_box, final_state_c_box_chk), True)
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class TestDeepSpeech2StaticModelOnline(unittest.TestCase):
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def setUp(self):
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export_prefix = "exp/deepspeech2_online/checkpoints/test_export"
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if not os.path.exists(os.path.dirname(export_prefix)):
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os.makedirs(os.path.dirname(export_prefix), mode=0o755)
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infer_model = DeepSpeech2InferModel(
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feat_size=161,
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dict_size=4233,
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num_conv_layers=2,
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num_rnn_layers=5,
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rnn_size=1024,
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num_fc_layers=0,
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fc_layers_size_list=[-1],
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use_gru=False)
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static_model = infer_model.export()
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paddle.jit.save(static_model, export_prefix)
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with open("test_data/static_ds2online_inputs.pickle", "rb") as f:
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self.data_dict = pickle.load(f)
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self.setup_model(export_prefix)
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def setup_model(self, export_prefix):
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deepspeech_config = inference.Config(export_prefix + ".pdmodel",
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export_prefix + ".pdiparams")
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if ('CUDA_VISIBLE_DEVICES' in os.environ.keys() and
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os.environ['CUDA_VISIBLE_DEVICES'].strip() != ''):
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deepspeech_config.enable_use_gpu(100, 0)
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deepspeech_config.enable_memory_optim()
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deepspeech_predictor = inference.create_predictor(deepspeech_config)
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self.predictor = deepspeech_predictor
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def test_unit(self):
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input_names = self.predictor.get_input_names()
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audio_handle = self.predictor.get_input_handle(input_names[0])
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audio_len_handle = self.predictor.get_input_handle(input_names[1])
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h_box_handle = self.predictor.get_input_handle(input_names[2])
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c_box_handle = self.predictor.get_input_handle(input_names[3])
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x_chunk = self.data_dict["audio_chunk"]
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x_chunk_lens = self.data_dict["audio_chunk_lens"]
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chunk_state_h_box = self.data_dict["chunk_state_h_box"]
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chunk_state_c_box = self.data_dict["chunk_state_c_bos"]
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audio_handle.reshape(x_chunk.shape)
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audio_handle.copy_from_cpu(x_chunk)
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audio_len_handle.reshape(x_chunk_lens.shape)
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audio_len_handle.copy_from_cpu(x_chunk_lens)
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h_box_handle.reshape(chunk_state_h_box.shape)
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h_box_handle.copy_from_cpu(chunk_state_h_box)
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c_box_handle.reshape(chunk_state_c_box.shape)
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c_box_handle.copy_from_cpu(chunk_state_c_box)
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output_names = self.predictor.get_output_names()
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output_handle = self.predictor.get_output_handle(output_names[0])
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output_lens_handle = self.predictor.get_output_handle(output_names[1])
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output_state_h_handle = self.predictor.get_output_handle(
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output_names[2])
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output_state_c_handle = self.predictor.get_output_handle(
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output_names[3])
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self.predictor.run()
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output_chunk_probs = output_handle.copy_to_cpu()
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output_chunk_lens = output_lens_handle.copy_to_cpu()
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chunk_state_h_box = output_state_h_handle.copy_to_cpu()
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chunk_state_c_box = output_state_c_handle.copy_to_cpu()
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return True
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if __name__ == '__main__':
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unittest.main()
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