From 63a70a00f7c9f162e8d7b3e330516438fb8cd87b Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期五, 19 四月 2024 16:08:05 +0800
Subject: [PATCH] add dynamic batch for fetch
---
funasr/models/seaco_paraformer/model.py | 88 ++++++++++++++++++++++++++------------------
1 files changed, 52 insertions(+), 36 deletions(-)
diff --git a/funasr/models/seaco_paraformer/model.py b/funasr/models/seaco_paraformer/model.py
index 20b0cc8..92fc989 100644
--- a/funasr/models/seaco_paraformer/model.py
+++ b/funasr/models/seaco_paraformer/model.py
@@ -99,6 +99,7 @@
)
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,
@@ -127,8 +128,8 @@
hotword_pad = kwargs.get("hotword_pad")
hotword_lengths = kwargs.get("hotword_lengths")
- dha_pad = kwargs.get("dha_pad")
-
+ seaco_label_pad = kwargs.get("seaco_label_pad")
+
batch_size = speech.shape[0]
# for data-parallel
text = text[:, : text_lengths.max()]
@@ -147,7 +148,7 @@
ys_lengths,
hotword_pad,
hotword_lengths,
- dha_pad,
+ seaco_label_pad,
)
if self.train_decoder:
loss_att, acc_att = self._calc_att_loss(
@@ -170,6 +171,12 @@
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)
+ return predictor_outs[:4]
+
def _calc_seaco_loss(
self,
encoder_out: torch.Tensor,
@@ -178,7 +185,7 @@
ys_lengths: torch.Tensor,
hotword_pad: torch.Tensor,
hotword_lengths: torch.Tensor,
- dha_pad: torch.Tensor,
+ seaco_label_pad: torch.Tensor,
):
# predictor forward
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
@@ -197,7 +204,7 @@
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_out, ys_lengths)
merged = self._merge(cif_attended, dec_attended)
dha_output = self.hotword_output_layer(merged[:, :-1]) # remove the last token in loss calculation
- loss_att = self.criterion_seaco(dha_output, dha_pad)
+ loss_att = self.criterion_seaco(dha_output, seaco_label_pad)
return loss_att
def _seaco_decode_with_ASF(self,
@@ -209,17 +216,20 @@
nfilter=50,
seaco_weight=1.0):
# decoder forward
+
decoder_out, decoder_hidden, _ = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, return_hidden=True, return_both=True)
+
decoder_pred = torch.log_softmax(decoder_out, dim=-1)
if hw_list is not None:
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)
selected = self._hotword_representation(hw_list_pad, torch.Tensor(hw_lengths).int().to(encoder_out.device))
+
contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
num_hot_word = contextual_info.shape[1]
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
-
+
# ASF Core
if nfilter > 0 and nfilter < num_hot_word:
hotword_scores = self.seaco_decoder.forward_asf6(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
@@ -239,13 +249,13 @@
cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
merged = self._merge(cif_attended, dec_attended)
-
+
dha_output = self.hotword_output_layer(merged) # remove the last token in loss calculation
dha_pred = torch.log_softmax(dha_output, dim=-1)
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)
@@ -253,8 +263,8 @@
# logits = dec_output * dha_mask + dha_output[:,:,:-1] * (1-dha_mask)
logits = dec_output * dha_mask + dha_output[:,:,:] * (1-dha_mask)
return logits
+
merged_pred = _merge_res(decoder_pred, dha_pred)
- # import pdb; pdb.set_trace()
return merged_pred
else:
return decoder_pred
@@ -304,7 +314,6 @@
logging.info("enable beam_search")
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
-
meta_data = {}
# extract fbank feats
@@ -332,21 +341,26 @@
# 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):
@@ -391,23 +405,25 @@
# 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)
--
Gitblit v1.9.1