tests/collections/asr/test_asr_regression_model.py

# Copyright (c) 2021, NVIDIA CORPORATION.  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.

import os

import pytest
from omegaconf import DictConfig

from nemo.collections.asr.models.classification_models import EncDecRegressionModel


@pytest.fixture()
def speech_regression_model():
    preprocessor = {'cls': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor', 'params': dict({})}
    encoder = {
        'cls': 'nemo.collections.asr.modules.ConvASREncoder',
        'params': {
            'feat_in': 64,
            'activation': 'relu',
            'conv_mask': True,
            'jasper': [
                {
                    'filters': 32,
                    'repeat': 1,
                    'kernel': [1],
                    'stride': [1],
                    'dilation': [1],
                    'dropout': 0.0,
                    'residual': False,
                    'separable': True,
                    'se': True,
                    'se_context_size': -1,
                }
            ],
        },
    }

    decoder = {
        'cls': 'nemo.collections.asr.modules.conv_asr.ConvASRDecoderClassification',
        'params': {'feat_in': 32, 'return_logits': True, 'num_classes': 1},
    }

    modelConfig = DictConfig(
        {
            'preprocessor': DictConfig(preprocessor),
            'encoder': DictConfig(encoder),
            'decoder': DictConfig(decoder),
            'labels': None,
            'is_regression_task': True,
        }
    )
    model = EncDecRegressionModel(cfg=modelConfig)
    return model


class TestEncDecRegressionModel:
    @pytest.mark.unit
    def test_constructor(self, speech_regression_model):
        asr_model = speech_regression_model.train()

        conv_cnt = (64 * 32 * 1 + 32) + (64 * 1 * 1 + 32)  # separable kernel + bias + pointwise kernel + bias
        bn_cnt = (4 * 32) * 2  # 2 * moving averages
        dec_cnt = 32 * 1 + 1  # fc + bias

        param_count = conv_cnt + bn_cnt + dec_cnt
        assert asr_model.num_weights == param_count

        # Check to/from config_dict:
        confdict = asr_model.to_config_dict()
        instance2 = EncDecRegressionModel.from_config_dict(confdict)

        assert isinstance(instance2, EncDecRegressionModel)

    @pytest.mark.unit
    def test_transcription(self, speech_regression_model, test_data_dir):

        audio_filenames = ['an22-flrp-b.wav', 'an90-fbbh-b.wav']
        audio_paths = [os.path.join(test_data_dir, "asr", "train", "an4", "wav", fp) for fp in audio_filenames]

        model = speech_regression_model.eval()

        # Test Top 1 classification transcription
        results = model.transcribe(audio_paths, batch_size=2)
        assert len(results) == 2