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 = EncDecCTCModelBPE.from_config_dict(confdict)
assert isinstance(instance2, EncDecCTCModelBPE)
def __init__(
self,
asr_model,
frame_len=1.6,
total_buffer=4.0,
batch_size=4,
):
'''
Args:
frame_len: frame's duration, seconds
frame_overlap: duration of overlaps before and after current frame, seconds
offset: number of symbols to drop for smooth streaming
'''
self.frame_bufferer = FeatureFrameBufferer(asr_model=asr_model,
frame_len=frame_len,
batch_size=batch_size,
total_buffer=total_buffer)
self.asr_model = asr_model
self.batch_size = batch_size
self.all_logits = []
self.all_preds = []
self.unmerged = []
if hasattr(asr_model.decoder, "vocabulary"):
self.blank_id = len(asr_model.decoder.vocabulary)
else:
self.blank_id = len(asr_model.joint.vocabulary)
self.tokenizer = asr_model.tokenizer
self.toks_unmerged = []
self.frame_buffers = []
self.reset()
cfg = copy.deepcopy(asr_model._cfg)
self.frame_len = frame_len
OmegaConf.set_struct(cfg.preprocessor, False)
# some changes for streaming scenario
cfg.preprocessor.dither = 0.0
cfg.preprocessor.pad_to = 0
cfg.preprocessor.normalize = "None"
self.raw_preprocessor = EncDecCTCModelBPE.from_config_dict(
cfg.preprocessor)
self.raw_preprocessor.to(asr_model.device)