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from examples.speech_recognition.models.vggtransformer import ( |
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TransformerDecoder, |
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VGGTransformerEncoder, |
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VGGTransformerModel, |
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vggtransformer_1, |
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vggtransformer_2, |
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vggtransformer_base, |
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) |
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from .asr_test_base import ( |
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DEFAULT_TEST_VOCAB_SIZE, |
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TestFairseqDecoderBase, |
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TestFairseqEncoderBase, |
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TestFairseqEncoderDecoderModelBase, |
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get_dummy_dictionary, |
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get_dummy_encoder_output, |
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get_dummy_input, |
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) |
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class VGGTransformerModelTest_mid(TestFairseqEncoderDecoderModelBase): |
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def setUp(self): |
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def override_config(args): |
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""" |
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vggtrasformer_1 use 14 layers of transformer, |
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for testing purpose, it is too expensive. For fast turn-around |
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test, reduce the number of layers to 3. |
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""" |
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args.transformer_enc_config = ( |
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"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3" |
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) |
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super().setUp() |
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extra_args_setter = [vggtransformer_1, override_config] |
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self.setUpModel(VGGTransformerModel, extra_args_setter) |
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self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) |
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class VGGTransformerModelTest_big(TestFairseqEncoderDecoderModelBase): |
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def setUp(self): |
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def override_config(args): |
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""" |
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vggtrasformer_2 use 16 layers of transformer, |
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for testing purpose, it is too expensive. For fast turn-around |
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test, reduce the number of layers to 3. |
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""" |
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args.transformer_enc_config = ( |
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"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3" |
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) |
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super().setUp() |
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extra_args_setter = [vggtransformer_2, override_config] |
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self.setUpModel(VGGTransformerModel, extra_args_setter) |
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self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) |
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class VGGTransformerModelTest_base(TestFairseqEncoderDecoderModelBase): |
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def setUp(self): |
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def override_config(args): |
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""" |
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vggtrasformer_base use 12 layers of transformer, |
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for testing purpose, it is too expensive. For fast turn-around |
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test, reduce the number of layers to 3. |
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""" |
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args.transformer_enc_config = ( |
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"((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 3" |
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) |
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super().setUp() |
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extra_args_setter = [vggtransformer_base, override_config] |
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self.setUpModel(VGGTransformerModel, extra_args_setter) |
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self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) |
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class VGGTransformerEncoderTest(TestFairseqEncoderBase): |
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def setUp(self): |
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super().setUp() |
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self.setUpInput(get_dummy_input(T=50, D=80, B=5)) |
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def test_forward(self): |
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print("1. test standard vggtransformer") |
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self.setUpEncoder(VGGTransformerEncoder(input_feat_per_channel=80)) |
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super().test_forward() |
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print("2. test vggtransformer with limited right context") |
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self.setUpEncoder( |
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VGGTransformerEncoder( |
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input_feat_per_channel=80, transformer_context=(-1, 5) |
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) |
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) |
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super().test_forward() |
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print("3. test vggtransformer with limited left context") |
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self.setUpEncoder( |
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VGGTransformerEncoder( |
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input_feat_per_channel=80, transformer_context=(5, -1) |
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) |
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) |
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super().test_forward() |
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print("4. test vggtransformer with limited right context and sampling") |
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self.setUpEncoder( |
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VGGTransformerEncoder( |
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input_feat_per_channel=80, |
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transformer_context=(-1, 12), |
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transformer_sampling=(2, 2), |
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) |
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) |
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super().test_forward() |
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print("5. test vggtransformer with windowed context and sampling") |
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self.setUpEncoder( |
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VGGTransformerEncoder( |
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input_feat_per_channel=80, |
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transformer_context=(12, 12), |
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transformer_sampling=(2, 2), |
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) |
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) |
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class TransformerDecoderTest(TestFairseqDecoderBase): |
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def setUp(self): |
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super().setUp() |
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dict = get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE) |
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decoder = TransformerDecoder(dict) |
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dummy_encoder_output = get_dummy_encoder_output(encoder_out_shape=(50, 5, 256)) |
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self.setUpDecoder(decoder) |
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self.setUpInput(dummy_encoder_output) |
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self.setUpPrevOutputTokens() |
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