def load_model(
device,
model_path: str,
):
with open('labels.json') as label_file:
labels = json.load(label_file)
hparams = {
"model": {
"hidden_size": 1024,
"hidden_layers": 5,
},
"audio_conf": {
"sample_rate": 16000,
"window_size": .02,
"window_stride": .01,
"window": "hamming",
},
"num_classes": len(labels)
}
model = DeepSpeech.load_from_checkpoint(
checkpoint_path=to_absolute_path(model_path),
hparams=hparams,
decoder=None,
)
model.to(device)
model.eval()
return model
def load_model(device, model_path):
model = DeepSpeech.load_from_checkpoint(
hydra.utils.to_absolute_path(model_path))
model.eval()
model = model.to(device)
return model