From 74f4f7b4c7228763fd923d2a27eb7d2515f89907 Mon Sep 17 00:00:00 2001
From: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
Date: 星期五, 08 三月 2024 11:33:04 +0800
Subject: [PATCH] seaco with cifv2 (#1450)
---
funasr/models/seaco_paraformer/model.py | 74 +++++++++++++++++++++++--------------
1 files changed, 46 insertions(+), 28 deletions(-)
diff --git a/funasr/models/seaco_paraformer/model.py b/funasr/models/seaco_paraformer/model.py
index a8b1f1f..f671db6 100644
--- a/funasr/models/seaco_paraformer/model.py
+++ b/funasr/models/seaco_paraformer/model.py
@@ -30,7 +30,7 @@
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 @@
)
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,
@@ -169,6 +170,16 @@
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,
@@ -248,7 +259,7 @@
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 @@
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 @@
# 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)
--
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