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# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. 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.
"""
This file implemented unit tests for loading all pretrained MixerTTS NGC checkpoints and generating Mel-spectrograms.
The test duration breakdowns are shown below. In general, each test for a single model is ~25 seconds on an NVIDIA RTX A6000.
"""
import pytest
from nemo.collections.tts.models import MixerTTSModel
available_models = [model.pretrained_model_name for model in MixerTTSModel.list_available_models()]
@pytest.fixture(params=available_models, ids=available_models)
@pytest.mark.run_only_on('GPU')
def pretrained_model(request, get_language_id_from_pretrained_model_name):
model_name = request.param
language_id = get_language_id_from_pretrained_model_name(model_name)
model = MixerTTSModel.from_pretrained(model_name=model_name)
return model, language_id
@pytest.mark.nightly
@pytest.mark.run_only_on('GPU')
def test_inference(pretrained_model, language_specific_text_example):
model, language_id = pretrained_model
text = language_specific_text_example[language_id]
parsed_text = model.parse(text)
_ = model.generate_spectrogram(tokens=parsed_text, raw_texts=[text])
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