def test_constructor(self, asr_model):
asr_model.train()
# TODO: make proper config and assert correct number of weights
# Check to/from config_dict:
confdict = asr_model.to_config_dict()
instance2 = EncDecRNNTBPEModel.from_config_dict(confdict)
assert isinstance(instance2, EncDecRNNTBPEModel)
def test_save_restore_artifact_agg(self, asr_model, test_data_dir):
tokenizer_dir = os.path.join(test_data_dir, "asr", "tokenizers",
"an4_spe_128")
tok_en = {"dir": tokenizer_dir, "type": "wpe"}
# the below is really an english tokenizer but we pretend it is spanish
tok_es = {"dir": tokenizer_dir, "type": "wpe"}
tcfg = DictConfig({
"type": "agg",
"langs": {
"en": tok_en,
"es": tok_es
}
})
with tempfile.TemporaryDirectory() as tmpdir:
asr_model.change_vocabulary(new_tokenizer_dir=tcfg,
new_tokenizer_type="agg")
save_path = os.path.join(tmpdir, "ctc_agg.nemo")
asr_model.train()
asr_model.save_to(save_path)
new_model = EncDecRNNTBPEModel.restore_from(save_path)
assert isinstance(new_model, type(asr_model))
assert isinstance(new_model.tokenizer,
tokenizers.AggregateTokenizer)
# should be double
assert new_model.tokenizer.tokenizer.vocab_size == 254
assert len(new_model.tokenizer.tokenizer.get_vocab()) == 254
def asr_model(test_data_dir):
preprocessor = {'cls': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor', 'params': dict({})}
model_defaults = {'enc_hidden': 1024, 'pred_hidden': 64}
encoder = {
'cls': 'nemo.collections.asr.modules.ConvASREncoder',
'params': {
'feat_in': 64,
'activation': 'relu',
'conv_mask': True,
'jasper': [
{
'filters': model_defaults['enc_hidden'],
'repeat': 1,
'kernel': [1],
'stride': [1],
'dilation': [1],
'dropout': 0.0,
'residual': False,
'separable': True,
'se': True,
'se_context_size': -1,
}
],
},
}
decoder = {
'_target_': 'nemo.collections.asr.modules.RNNTDecoder',
'prednet': {'pred_hidden': model_defaults['pred_hidden'], 'pred_rnn_layers': 1,},
}
joint = {
'_target_': 'nemo.collections.asr.modules.RNNTJoint',
'jointnet': {'joint_hidden': 32, 'activation': 'relu',},
}
decoding = {'strategy': 'greedy_batch', 'greedy': {'max_symbols': 30}}
tokenizer = {'dir': os.path.join(test_data_dir, "asr", "tokenizers", "an4_wpe_128"), 'type': 'wpe'}
modelConfig = DictConfig(
{
'preprocessor': DictConfig(preprocessor),
'model_defaults': DictConfig(model_defaults),
'encoder': DictConfig(encoder),
'decoder': DictConfig(decoder),
'joint': DictConfig(joint),
'tokenizer': DictConfig(tokenizer),
'decoding': DictConfig(decoding),
}
)
model_instance = EncDecRNNTBPEModel(cfg=modelConfig)
return model_instance
def test_save_restore_artifact(self, asr_model):
asr_model.train()
with tempfile.TemporaryDirectory() as tmp_dir:
path = os.path.join(tmp_dir, 'rnnt_bpe.nemo')
asr_model.save_to(path)
new_model = EncDecRNNTBPEModel.restore_from(path)
assert isinstance(new_model, type(asr_model))
assert new_model.vocab_path == 'vocab.txt'
assert len(new_model.tokenizer.tokenizer.get_vocab()) == 128
def test_save_restore_artifact_spe(self, asr_model, test_data_dir):
asr_model.train()
with tempfile.TemporaryDirectory() as tmpdir:
tokenizer_dir = os.path.join(test_data_dir, "asr", "tokenizers",
"an4_spe_128")
asr_model.change_vocabulary(new_tokenizer_dir=tokenizer_dir,
new_tokenizer_type='bpe')
save_path = os.path.join(tmpdir, 'ctc_bpe.nemo')
asr_model.train()
asr_model.save_to(save_path)
new_model = EncDecRNNTBPEModel.restore_from(save_path)
assert isinstance(new_model, type(asr_model))
assert isinstance(new_model.tokenizer,
tokenizers.SentencePieceTokenizer)
assert new_model.model_path.endswith('_tokenizer.model')
assert new_model.vocab_path.endswith('_vocab.txt')
assert new_model.spe_vocab_path.endswith('_tokenizer.vocab')
