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67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. 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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"""
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This file implemented unit tests for loading all pretrained FastPitch NGC checkpoints and generating Mel-spectrograms.
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The test duration breakdowns are shown below. In general, each test for a single model is ~25 seconds on an NVIDIA RTX A6000.
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"""
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import random
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import pytest
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import torch
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from nemo.collections.tts.models import FastPitchModel
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available_models = [model.pretrained_model_name for model in FastPitchModel.list_available_models()]
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@pytest.fixture(params=available_models, ids=available_models)
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def pretrained_model(request, get_language_id_from_pretrained_model_name):
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model_name = request.param
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language_id = get_language_id_from_pretrained_model_name(model_name)
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model = FastPitchModel.from_pretrained(model_name=model_name)
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return model, language_id
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# This test can only pass when nemo_text_process<=0.1.8rc0. If >0.1.8rc0, the normalized outputs are unexpected for Chinese.
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# Will remove the marker `pleasefixme` once next-text-processing new release fixes the bug.
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# Tracking bugfix in https://github.com/NVIDIA/NeMo-text-processing/issues/109.
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@pytest.mark.pleasefixme
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@pytest.mark.nightly
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@pytest.mark.run_only_on('GPU')
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def test_inference(pretrained_model, language_specific_text_example):
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model, language_id = pretrained_model
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text = language_specific_text_example[language_id]
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parsed_text = model.parse(text)
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# Multi-Speaker
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speaker_id = None
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reference_spec = None
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reference_spec_lens = None
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if hasattr(model.fastpitch, 'speaker_emb'):
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speaker_id = 0
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if hasattr(model.fastpitch, 'speaker_encoder'):
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if hasattr(model.fastpitch.speaker_encoder, 'lookup_module'):
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speaker_id = 0
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if hasattr(model.fastpitch.speaker_encoder, 'gst_module'):
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bs, lens, t_spec = parsed_text.shape[0], random.randint(50, 100), model.cfg.n_mel_channels
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reference_spec = torch.rand(bs, lens, t_spec)
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reference_spec_lens = torch.tensor([lens]).long().expand(bs)
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_ = model.generate_spectrogram(
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tokens=parsed_text, speaker=speaker_id, reference_spec=reference_spec, reference_spec_lens=reference_spec_lens
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)
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