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PaddleSpeech/tests/unit/asr/deepspeech2_online_model_te...

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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.
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import os
import pickle
import unittest
import numpy as np
import paddle
from paddle import inference
from paddlespeech.s2t.models.ds2_online import DeepSpeech2InferModelOnline
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from paddlespeech.s2t.models.ds2_online import DeepSpeech2ModelOnline
class TestDeepSpeech2ModelOnline(unittest.TestCase):
def setUp(self):
paddle.set_device('cpu')
self.batch_size = 2
self.feat_dim = 161
max_len = 210
# (B, T, D)
audio = np.random.randn(self.batch_size, max_len, self.feat_dim)
audio_len = np.random.randint(max_len, size=self.batch_size)
audio_len[-1] = 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_ds2_1(self):
model = DeepSpeech2ModelOnline(
feat_size=self.feat_dim,
dict_size=10,
num_conv_layers=2,
num_rnn_layers=3,
rnn_size=1024,
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=False)
loss = model(self.audio, self.audio_len, self.text, self.text_len)
self.assertEqual(loss.numel(), 1)
def test_ds2_2(self):
model = DeepSpeech2ModelOnline(
feat_size=self.feat_dim,
dict_size=10,
num_conv_layers=2,
num_rnn_layers=3,
rnn_size=1024,
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=True)
loss = model(self.audio, self.audio_len, self.text, self.text_len)
self.assertEqual(loss.numel(), 1)
def test_ds2_3(self):
model = DeepSpeech2ModelOnline(
feat_size=self.feat_dim,
dict_size=10,
num_conv_layers=2,
num_rnn_layers=3,
rnn_size=1024,
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=False)
loss = model(self.audio, self.audio_len, self.text, self.text_len)
self.assertEqual(loss.numel(), 1)
def test_ds2_4(self):
model = DeepSpeech2ModelOnline(
feat_size=self.feat_dim,
dict_size=10,
num_conv_layers=2,
num_rnn_layers=3,
rnn_size=1024,
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=True)
loss = model(self.audio, self.audio_len, self.text, self.text_len)
self.assertEqual(loss.numel(), 1)
def test_ds2_5(self):
model = DeepSpeech2ModelOnline(
feat_size=self.feat_dim,
dict_size=10,
num_conv_layers=2,
num_rnn_layers=3,
rnn_size=1024,
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=False)
loss = model(self.audio, self.audio_len, self.text, self.text_len)
self.assertEqual(loss.numel(), 1)
def test_ds2_6(self):
model = DeepSpeech2ModelOnline(
feat_size=self.feat_dim,
dict_size=10,
num_conv_layers=2,
num_rnn_layers=3,
rnn_size=1024,
rnn_direction='bidirect',
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=False)
loss = model(self.audio, self.audio_len, self.text, self.text_len)
self.assertEqual(loss.numel(), 1)
def test_ds2_7(self):
use_gru = False
model = DeepSpeech2ModelOnline(
feat_size=self.feat_dim,
dict_size=10,
num_conv_layers=2,
num_rnn_layers=1,
rnn_size=1024,
rnn_direction='forward',
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=use_gru)
model.eval()
paddle.device.set_device("cpu")
de_ch_size = 8
eouts, eouts_lens, final_state_h_box, final_state_c_box = model.encoder(
self.audio, self.audio_len)
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(
self.audio, self.audio_len, de_ch_size)
eouts_by_chk = paddle.concat(eouts_by_chk_list, axis=1)
eouts_lens_by_chk = paddle.add_n(eouts_lens_by_chk_list)
decode_max_len = eouts.shape[1]
eouts_by_chk = eouts_by_chk[:, :decode_max_len, :]
self.assertEqual(paddle.allclose(eouts_by_chk, eouts), True)
self.assertEqual(
paddle.allclose(final_state_h_box, final_state_h_box_chk), True)
if use_gru is False:
self.assertEqual(
paddle.allclose(final_state_c_box, final_state_c_box_chk), True)
def test_ds2_8(self):
use_gru = True
model = DeepSpeech2ModelOnline(
feat_size=self.feat_dim,
dict_size=10,
num_conv_layers=2,
num_rnn_layers=1,
rnn_size=1024,
rnn_direction='forward',
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=use_gru)
model.eval()
paddle.device.set_device("cpu")
de_ch_size = 8
eouts, eouts_lens, final_state_h_box, final_state_c_box = model.encoder(
self.audio, self.audio_len)
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(
self.audio, self.audio_len, de_ch_size)
eouts_by_chk = paddle.concat(eouts_by_chk_list, axis=1)
eouts_lens_by_chk = paddle.add_n(eouts_lens_by_chk_list)
decode_max_len = eouts.shape[1]
eouts_by_chk = eouts_by_chk[:, :decode_max_len, :]
self.assertEqual(paddle.allclose(eouts_by_chk, eouts), True)
self.assertEqual(
paddle.allclose(final_state_h_box, final_state_h_box_chk), True)
if use_gru is False:
self.assertEqual(
paddle.allclose(final_state_c_box, final_state_c_box_chk), True)
class TestDeepSpeech2StaticModelOnline(unittest.TestCase):
def setUp(self):
export_prefix = "exp/deepspeech2_online/checkpoints/test_export"
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if not os.path.exists(os.path.dirname(export_prefix)):
os.makedirs(os.path.dirname(export_prefix), mode=0o755)
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infer_model = DeepSpeech2InferModelOnline(
feat_size=161,
dict_size=4233,
num_conv_layers=2,
num_rnn_layers=5,
rnn_size=1024,
num_fc_layers=0,
fc_layers_size_list=[-1],
use_gru=False)
static_model = infer_model.export()
paddle.jit.save(static_model, export_prefix)
with open("test_data/static_ds2online_inputs.pickle", "rb") as f:
self.data_dict = pickle.load(f)
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self.setup_model(export_prefix)
def setup_model(self, export_prefix):
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deepspeech_config = inference.Config(export_prefix + ".pdmodel",
export_prefix + ".pdiparams")
if ('CUDA_VISIBLE_DEVICES' in os.environ.keys() and
os.environ['CUDA_VISIBLE_DEVICES'].strip() != ''):
deepspeech_config.enable_use_gpu(100, 0)
deepspeech_config.enable_memory_optim()
deepspeech_predictor = inference.create_predictor(deepspeech_config)
self.predictor = deepspeech_predictor
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def test_unit(self):
input_names = self.predictor.get_input_names()
audio_handle = self.predictor.get_input_handle(input_names[0])
audio_len_handle = self.predictor.get_input_handle(input_names[1])
h_box_handle = self.predictor.get_input_handle(input_names[2])
c_box_handle = self.predictor.get_input_handle(input_names[3])
x_chunk = self.data_dict["audio_chunk"]
x_chunk_lens = self.data_dict["audio_chunk_lens"]
chunk_state_h_box = self.data_dict["chunk_state_h_box"]
chunk_state_c_box = self.data_dict["chunk_state_c_bos"]
audio_handle.reshape(x_chunk.shape)
audio_handle.copy_from_cpu(x_chunk)
audio_len_handle.reshape(x_chunk_lens.shape)
audio_len_handle.copy_from_cpu(x_chunk_lens)
h_box_handle.reshape(chunk_state_h_box.shape)
h_box_handle.copy_from_cpu(chunk_state_h_box)
c_box_handle.reshape(chunk_state_c_box.shape)
c_box_handle.copy_from_cpu(chunk_state_c_box)
output_names = self.predictor.get_output_names()
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output_handle = self.predictor.get_output_handle(output_names[0])
output_lens_handle = self.predictor.get_output_handle(output_names[1])
output_state_h_handle = self.predictor.get_output_handle(
output_names[2])
output_state_c_handle = self.predictor.get_output_handle(
output_names[3])
self.predictor.run()
output_chunk_probs = output_handle.copy_to_cpu()
output_chunk_lens = output_lens_handle.copy_to_cpu()
chunk_state_h_box = output_state_h_handle.copy_to_cpu()
chunk_state_c_box = output_state_c_handle.copy_to_cpu()
return True
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if __name__ == '__main__':
unittest.main()