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Merge remote-tracking branch 'gitlab/dev_yf' into dev_yf

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语帆 2024-02-22 14:05:20 +08:00
commit 6c17f2a528
1 changed files with 7 additions and 39 deletions
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funasr/models/seaco_paraformer/model.py
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@ -212,88 +212,63 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
nfilter=50,
seaco_weight=1.0):
# decoder forward
pdb.set_trace()
decoder_out, decoder_hidden, _ = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, return_hidden=True, return_both=True)
pdb.set_trace()
decoder_pred = torch.log_softmax(decoder_out, dim=-1)
if hw_list is not None:
pdb.set_trace()
hw_lengths = [len(i) for i in hw_list]
hw_list_ = [torch.Tensor(i).long() for i in hw_list]
hw_list_pad = pad_list(hw_list_, 0).to(encoder_out.device)
pdb.set_trace()
selected = self._hotword_representation(hw_list_pad, torch.Tensor(hw_lengths).int().to(encoder_out.device))
pdb.set_trace()
contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
pdb.set_trace()
num_hot_word = contextual_info.shape[1]
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
pdb.set_trace()
# ASF Core
if nfilter > 0 and nfilter < num_hot_word:
for dec in self.seaco_decoder.decoders:
dec.reserve_attn = True
pdb.set_trace()
# cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
# cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist()
pdb.set_trace()
hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1]
# hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device)
pdb.set_trace()
dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist()
pdb.set_trace()
add_filter = dec_filter
pdb.set_trace()
add_filter.append(len(hw_list_pad)-1)
# filter hotword embedding
pdb.set_trace()
selected = selected[add_filter]
# again
pdb.set_trace()
contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
pdb.set_trace()
num_hot_word = contextual_info.shape[1]
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
pdb.set_trace()
for dec in self.seaco_decoder.decoders:
dec.attn_mat = []
dec.reserve_attn = False
pdb.set_trace()
# SeACo Core
cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
pdb.set_trace()
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
pdb.set_trace()
merged = self._merge(cif_attended, dec_attended)
pdb.set_trace()
dha_output = self.hotword_output_layer(merged) # remove the last token in loss calculation
pdb.set_trace()
dha_pred = torch.log_softmax(dha_output, dim=-1)
pdb.set_trace()
def _merge_res(dec_output, dha_output):
pdb.set_trace()
lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0])
pdb.set_trace()
dha_ids = dha_output.max(-1)[-1]# [0]
pdb.set_trace()
dha_mask = (dha_ids == 8377).int().unsqueeze(-1)
pdb.set_trace()
a = (1 - lmbd) / lmbd
b = 1 / lmbd
pdb.set_trace()
a, b = a.to(dec_output.device), b.to(dec_output.device)
pdb.set_trace()
dha_mask = (dha_mask + a.reshape(-1, 1, 1)) / b.reshape(-1, 1, 1)
# logits = dec_output * dha_mask + dha_output[:,:,:-1] * (1-dha_mask)
pdb.set_trace()
logits = dec_output * dha_mask + dha_output[:,:,:] * (1-dha_mask)
return logits
merged_pred = _merge_res(decoder_pred, dha_pred)
pdb.set_trace()
# import pdb; pdb.set_trace()
return merged_pred
else:
return decoder_pred
@ -347,7 +322,6 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
logging.info("enable beam_search")
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
pdb.set_trace()
meta_data = {}
# extract fbank feats
@ -355,7 +329,6 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
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()
@ -366,18 +339,15 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
pdb.set_trace()
# hotword
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, _, _ = predictor_outs[0], predictor_outs[1], \
@ -386,16 +356,14 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
if torch.max(pre_token_length) < 1:
return []
pdb.set_trace()
decoder_out = self._seaco_decode_with_ASF(encoder_out, encoder_out_lens,
pre_acoustic_embeds,
pre_token_length,
hw_list=self.hotword_list)
pdb.set_trace()
# decoder_out, _ = decoder_outs[0], decoder_outs[1]
_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
pre_token_length)
pdb.set_trace()
results = []
b, n, d = decoder_out.size()
for i in range(b):