def test_EncDecCTCModel_adapted_export_to_onnx(self):
model_config = DictConfig({
'preprocessor': DictConfig(self.preprocessor),
'encoder': DictConfig(self.encoder_dict),
'decoder': DictConfig(self.decoder_dict),
})
# support adapter in encoder
model_config.encoder.cls = model_config.encoder.cls + 'Adapter' # ConvASREncoderAdapter
# load model
model = EncDecCTCModel(cfg=model_config)
# add adapter
adapter_cfg = OmegaConf.structured(
LinearAdapterConfig(
in_features=model_config.encoder.params.jasper[0].filters,
dim=32))
model.add_adapter('temp', cfg=adapter_cfg)
model = model.cuda()
with tempfile.TemporaryDirectory() as tmpdir:
filename = os.path.join(tmpdir, 'qn.onnx')
model.export(
output=filename,
check_trace=True,
)
onnx_model = onnx.load(filename)
onnx.checker.check_model(onnx_model,
full_check=True) # throws when failed
assert onnx_model.graph.input[0].name == 'audio_signal'
assert onnx_model.graph.output[0].name == 'logprobs'
def generate_ref_hyps(asr_model: EncDecCTCModel, search: str, arpa: str):
if can_gpu:
asr_model = asr_model.cuda()
print("USING GPU!")
asr_model.eval()
vocabulary = asr_model.decoder.vocabulary
labels_map = dict([(i, vocabulary[i]) for i in range(len(vocabulary))])
wer = WER(vocabulary=vocabulary)
if search == "kenlm" or search == "beamsearch":
arpa_file = prepare_arpa_file(arpa)
lm_path = arpa_file if search == "kenlm" else None
beamsearcher = nemo_asr.modules.BeamSearchDecoderWithLM(
vocab=list(vocabulary),
beam_width=16,
alpha=2,
beta=1.5,
lm_path=lm_path,
num_cpus=max(os.cpu_count(), 1),
input_tensor=True,
)
for batch in asr_model.test_dataloader():
# TODO(tilo): test_loader should return dict or some typed object not tuple of tensors!!
if can_gpu:
batch = [x.cuda() for x in batch]
input_signal, inpsig_len, transcript, transc_len = batch
with autocast():
log_probs, encoded_len, greedy_predictions = asr_model(
input_signal=input_signal, input_signal_length=inpsig_len)
if search == "greedy":
decoded = wer.ctc_decoder_predictions_tensor(greedy_predictions)
else:
decoded = beamsearch_forward(beamsearcher,
log_probs=log_probs,
log_probs_length=encoded_len)
for i, hyp in enumerate(decoded):
reference = "".join([
labels_map[c]
for c in transcript[i].cpu().detach().numpy()[:transc_len[i]]
])
yield reference, hyp