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# Copyright (c) 2020, 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.
"""
WARNING: Running this test will download ALL pre-trained NeMo models.
This is bandwidth and disk space consuming.
"""
import nemo.collections.asr as nemo_asr
import nemo.collections.nlp as nemo_nlp
import nemo.collections.tts as nemo_tts
def testclass_downloads(cls, refresh_cache, model_names=None):
for model_info in cls.list_available_models():
model = cls.from_pretrained(model_name=model_info.pretrained_model_name, refresh_cache=refresh_cache)
assert isinstance(model, cls)
if model_names is not None:
assert set(model_names) == set([m.pretrained_model_name for m in cls.list_available_models()])
for refresh_cache in [True, False]:
# Test ASR collection
testclass_downloads(
nemo_asr.models.EncDecCTCModel,
refresh_cache,
[
'QuartzNet15x5Base-En',
'QuartzNet15x5Base-Zh',
'QuartzNet5x5LS-En',
'QuartzNet15x5NR-En',
'Jasper10x5Dr-En',
],
)
testclass_downloads(nemo_asr.models.EncDecCTCModelBPE, refresh_cache, ['ContextNet-192-WPE-1024-8x-Stride'])
testclass_downloads(
nemo_asr.models.EncDecClassificationModel,
refresh_cache,
[
'MatchboxNet-3x1x64-v1',
'MatchboxNet-3x2x64-v1',
'MatchboxNet-3x1x64-v2',
'MatchboxNet-3x1x64-v2',
'MatchboxNet-3x1x64-v2-subset-task',
'MatchboxNet-3x2x64-v2-subset-task',
'MatchboxNet-VAD-3x2',
],
)
testclass_downloads(
nemo_asr.models.EncDecSpeakerLabelModel,
refresh_cache,
[
'speakerrecognition_speakernet',
'speakerverification_speakernet',
'speakerdiarization_speakernet',
'ecapa_tdnn',
],
)
# Test NLP collection
testclass_downloads(nemo_nlp.models.TokenClassificationModel, refresh_cache, ['NERModel'])
testclass_downloads(
nemo_nlp.models.PunctuationCapitalizationModel,
refresh_cache,
['Punctuation_Capitalization_with_BERT', 'Punctuation_Capitalization_with_DistilBERT'],
)
testclass_downloads(
nemo_nlp.models.QAModel,
refresh_cache,
[
'BERTBaseUncasedSQuADv1.1',
'BERTBaseUncasedSQuADv2.0',
'BERTLargeUncasedSQuADv1.1',
'BERTLargeUncasedSQuADv2.0',
],
)
# testclass_downloads(nemo_nlp.models.IntentSlotClassificationModel, refresh_cache, ['Joint_Intent_Slot_Assistant'])
# Test TTS collection
testclass_downloads(nemo_tts.models.Tacotron2Model, refresh_cache, ['Tacotron2-22050Hz'])
testclass_downloads(nemo_tts.models.WaveGlowModel, refresh_cache, ['WaveGlow-22050Hz'])
testclass_downloads(nemo_tts.models.SqueezeWaveModel, refresh_cache, ['SqueezeWave-22050Hz'])
testclass_downloads(nemo_tts.models.GlowTTSModel, refresh_cache, ['GlowTTS-22050Hz'])
print("############ THAT'S ALL FOLKS! ############")
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