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语帆 2024-02-21 19:07:25 +08:00
parent d2f1cf39f8
commit 0a7384a1ec
1 changed files with 8 additions and 17 deletions
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funasr/models/contextual_paraformer/model.py
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@ -294,10 +294,11 @@ class ContextualParaformer(Paraformer):
enforce_sorted=False)
_, (h_n, _) = self.bias_encoder(hw_embed)
hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
pdb.set_trace()
decoder_outs = self.decoder(
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed, clas_scale=clas_scale
)
pdb.set_trace()
decoder_out = decoder_outs[0]
decoder_out = torch.log_softmax(decoder_out, dim=-1)
return decoder_out, ys_pad_lens
@ -311,65 +312,55 @@ class ContextualParaformer(Paraformer):
**kwargs,
):
# init beamsearch
pdb.set_trace()
is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
if self.beam_search is None and (is_use_lm or is_use_ctc):
logging.info("enable beam_search")
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
pdb.set_trace()
meta_data = {}
# extract fbank feats
time1 = time.perf_counter()
pdb.set_trace()
audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
pdb.set_trace()
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
pdb.set_trace()
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
frontend=frontend)
time3 = time.perf_counter()
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
meta_data[
"batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
pdb.set_trace()
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
# hotword
pdb.set_trace()
self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend)
pdb.set_trace()
# Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
pdb.set_trace()
# predictor
predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
predictor_outs[2], predictor_outs[3]
pdb.set_trace()
pre_token_length = pre_token_length.round().long()
if torch.max(pre_token_length) < 1:
return []
pdb.set_trace()
decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens,
pre_acoustic_embeds,
pre_token_length,
hw_list=self.hotword_list,
clas_scale=kwargs.get("clas_scale", 1.0))
pdb.set_trace()
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
pdb.set_trace()