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seaco with cifv2 (#1450)

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* seaco with cifv2
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Shi Xian 2024-03-08 11:33:04 +08:00 committed by GitHub
parent 500197b8ad
commit 74f4f7b4c7
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1 changed files with 46 additions and 28 deletions
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74
funasr/models/seaco_paraformer/model.py
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@ -30,7 +30,7 @@ from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
import pdb
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
else:
@ -99,6 +99,7 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
)
self.train_decoder = kwargs.get("train_decoder", False)
self.NO_BIAS = kwargs.get("NO_BIAS", 8377)
self.predictor_name = kwargs.get("predictor")
def forward(
self,
@ -170,6 +171,16 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
def _merge(self, cif_attended, dec_attended):
return cif_attended + dec_attended
def calc_predictor(self, encoder_out, encoder_out_lens):
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
encoder_out.device)
predictor_outs = self.predictor(encoder_out, None, encoder_out_mask, ignore_id=self.ignore_id)
if len(predictor_outs) == 4:
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs
else:
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index, pre_token_length2 = predictor_outs
return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
def _calc_seaco_loss(
self,
encoder_out: torch.Tensor,
@ -248,7 +259,7 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
def _merge_res(dec_output, dha_output):
lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0])
dha_ids = dha_output.max(-1)[-1]# [0]
dha_mask = (dha_ids == 8377).int().unsqueeze(-1)
dha_mask = (dha_ids == self.NO_BIAS).int().unsqueeze(-1)
a = (1 - lmbd) / lmbd
b = 1 / lmbd
a, b = a.to(dec_output.device), b.to(dec_output.device)
@ -332,23 +343,28 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
# predictor
predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
pre_acoustic_embeds, pre_token_length, _, _ = predictor_outs[0], predictor_outs[1], \
predictor_outs[2], predictor_outs[3]
pre_acoustic_embeds, pre_token_length = predictor_outs[0], predictor_outs[1]
pre_token_length = pre_token_length.round().long()
if torch.max(pre_token_length) < 1:
return []
decoder_out = self._seaco_decode_with_ASF(encoder_out, encoder_out_lens,
pre_acoustic_embeds,
pre_token_length,
hw_list=self.hotword_list)
decoder_out = self._seaco_decode_with_ASF(encoder_out,
encoder_out_lens,
pre_acoustic_embeds,
pre_token_length,
hw_list=self.hotword_list
)
# decoder_out, _ = decoder_outs[0], decoder_outs[1]
_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
pre_token_length)
if self.predictor_name == "CifPredictorV3":
_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out,
encoder_out_lens,
pre_token_length)
else:
us_alphas = None
results = []
b, n, d = decoder_out.size()
for i in range(b):
@ -393,23 +409,25 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
# Change integer-ids to tokens
token = tokenizer.ids2tokens(token_int)
text = tokenizer.tokens2text(token)
_, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:encoder_out_lens[i] * 3],
us_peaks[i][:encoder_out_lens[i] * 3],
copy.copy(token),
vad_offset=kwargs.get("begin_time", 0))
text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(
token, timestamp)
result_i = {"key": key[i], "text": text_postprocessed,
"timestamp": time_stamp_postprocessed
}
if ibest_writer is not None:
ibest_writer["token"][key[i]] = " ".join(token)
ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
ibest_writer["text"][key[i]] = text_postprocessed
if us_alphas is not None:
_, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:encoder_out_lens[i] * 3],
us_peaks[i][:encoder_out_lens[i] * 3],
copy.copy(token),
vad_offset=kwargs.get("begin_time", 0))
text_postprocessed, time_stamp_postprocessed, _ = \
postprocess_utils.sentence_postprocess(token, timestamp)
result_i = {"key": key[i], "text": text_postprocessed,
"timestamp": time_stamp_postprocessed}
if ibest_writer is not None:
ibest_writer["token"][key[i]] = " ".join(token)
ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
ibest_writer["text"][key[i]] = text_postprocessed
else:
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
result_i = {"key": key[i], "text": text_postprocessed}
if ibest_writer is not None:
ibest_writer["token"][key[i]] = " ".join(token)
ibest_writer["text"][key[i]] = text_postprocessed
else:
result_i = {"key": key[i], "token_int": token_int}
results.append(result_i)