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93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
# Copyright (c) 2020, 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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"""
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WARNING: Running this test will download ALL pre-trained NeMo models.
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This is bandwidth and disk space consuming.
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"""
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import nemo.collections.asr as nemo_asr
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import nemo.collections.nlp as nemo_nlp
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import nemo.collections.tts as nemo_tts
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def testclass_downloads(cls, refresh_cache, model_names=None):
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for model_info in cls.list_available_models():
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model = cls.from_pretrained(model_name=model_info.pretrained_model_name, refresh_cache=refresh_cache)
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assert isinstance(model, cls)
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if model_names is not None:
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assert set(model_names) == set([m.pretrained_model_name for m in cls.list_available_models()])
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for refresh_cache in [True, False]:
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# Test ASR collection
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testclass_downloads(
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nemo_asr.models.EncDecCTCModel,
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refresh_cache,
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[
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'stt_en_jasper10x5dr',
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'asr_talknet_aligner',
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],
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)
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testclass_downloads(nemo_asr.models.EncDecCTCModelBPE, refresh_cache, ['ContextNet-192-WPE-1024-8x-Stride'])
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testclass_downloads(
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nemo_asr.models.EncDecClassificationModel,
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refresh_cache,
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[
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'MatchboxNet-3x1x64-v1',
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'MatchboxNet-3x2x64-v1',
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'MatchboxNet-3x1x64-v2',
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'MatchboxNet-3x1x64-v2',
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'MatchboxNet-3x1x64-v2-subset-task',
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'MatchboxNet-3x2x64-v2-subset-task',
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'MatchboxNet-VAD-3x2',
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],
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)
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testclass_downloads(
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nemo_asr.models.EncDecSpeakerLabelModel,
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refresh_cache,
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[
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'speakerrecognition_speakernet',
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'speakerverification_speakernet',
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'speakerdiarization_speakernet',
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'ecapa_tdnn',
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],
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)
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# Test NLP collection
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testclass_downloads(nemo_nlp.models.TokenClassificationModel, refresh_cache, ['NERModel'])
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testclass_downloads(
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nemo_nlp.models.PunctuationCapitalizationModel,
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refresh_cache,
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['Punctuation_Capitalization_with_BERT', 'Punctuation_Capitalization_with_DistilBERT'],
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)
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testclass_downloads(
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nemo_nlp.models.QAModel,
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refresh_cache,
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[
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'BERTBaseUncasedSQuADv1.1',
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'BERTBaseUncasedSQuADv2.0',
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'BERTLargeUncasedSQuADv1.1',
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'BERTLargeUncasedSQuADv2.0',
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],
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)
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# testclass_downloads(nemo_nlp.models.IntentSlotClassificationModel, refresh_cache, ['Joint_Intent_Slot_Assistant'])
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# Test TTS collection
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testclass_downloads(nemo_tts.models.SqueezeWaveModel, refresh_cache, ['SqueezeWave-22050Hz'])
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testclass_downloads(nemo_tts.models.GlowTTSModel, refresh_cache, ['GlowTTS-22050Hz'])
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print("############ THAT'S ALL FOLKS! ############")
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