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96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. 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 pytest
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from omegaconf import DictConfig
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from nemo.collections.asr.models.classification_models import EncDecRegressionModel
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@pytest.fixture()
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def speech_regression_model():
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preprocessor = {'cls': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor', 'params': dict({})}
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encoder = {
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'cls': 'nemo.collections.asr.modules.ConvASREncoder',
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'params': {
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'feat_in': 64,
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'activation': 'relu',
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'conv_mask': True,
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'jasper': [
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{
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'filters': 32,
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'repeat': 1,
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'kernel': [1],
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'stride': [1],
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'dilation': [1],
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'dropout': 0.0,
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'residual': False,
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'separable': True,
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'se': True,
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'se_context_size': -1,
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}
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],
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},
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}
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decoder = {
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'cls': 'nemo.collections.asr.modules.conv_asr.ConvASRDecoderClassification',
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'params': {'feat_in': 32, 'return_logits': True, 'num_classes': 1},
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}
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modelConfig = DictConfig(
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{
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'preprocessor': DictConfig(preprocessor),
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'encoder': DictConfig(encoder),
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'decoder': DictConfig(decoder),
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'labels': None,
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'is_regression_task': True,
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}
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)
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model = EncDecRegressionModel(cfg=modelConfig)
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return model
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class TestEncDecRegressionModel:
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@pytest.mark.unit
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def test_constructor(self, speech_regression_model):
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asr_model = speech_regression_model.train()
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conv_cnt = (64 * 32 * 1 + 32) + (64 * 1 * 1 + 32) # separable kernel + bias + pointwise kernel + bias
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bn_cnt = (4 * 32) * 2 # 2 * moving averages
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dec_cnt = 32 * 1 + 1 # fc + bias
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param_count = conv_cnt + bn_cnt + dec_cnt
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assert asr_model.num_weights == param_count
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# Check to/from config_dict:
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confdict = asr_model.to_config_dict()
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instance2 = EncDecRegressionModel.from_config_dict(confdict)
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assert isinstance(instance2, EncDecRegressionModel)
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@pytest.mark.unit
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def test_transcription(self, speech_regression_model, test_data_dir):
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audio_filenames = ['an22-flrp-b.wav', 'an90-fbbh-b.wav']
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audio_paths = [os.path.join(test_data_dir, "asr", "train", "an4", "wav", fp) for fp in audio_filenames]
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model = speech_regression_model.eval()
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# Test Top 1 classification transcription
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results = model.transcribe(audio_paths, batch_size=2)
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assert len(results) == 2
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