From a016617c7ec98ab9c7475ff7d3b6150b98d5beeb Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 28 二月 2023 18:36:52 +0800
Subject: [PATCH] Merge pull request #165 from alibaba-damo-academy/dev_cmz
---
funasr/punctuation/espnet_model.py | 55 ++++++++++++++++++++++++++++++++++++-------------------
1 files changed, 36 insertions(+), 19 deletions(-)
diff --git a/funasr/punctuation/espnet_model.py b/funasr/punctuation/espnet_model.py
index 65edaad..c513779 100644
--- a/funasr/punctuation/espnet_model.py
+++ b/funasr/punctuation/espnet_model.py
@@ -14,15 +14,18 @@
class ESPnetPunctuationModel(AbsESPnetModel):
- def __init__(self, punc_model: AbsPunctuation, vocab_size: int, ignore_id: int = 0):
+ def __init__(self, punc_model: AbsPunctuation, vocab_size: int, ignore_id: int = 0, punc_weight: list = None):
assert check_argument_types()
super().__init__()
self.punc_model = punc_model
+ self.punc_weight = torch.Tensor(punc_weight)
self.sos = 1
self.eos = 2
# ignore_id may be assumed as 0, shared with CTC-blank symbol for ASR.
self.ignore_id = ignore_id
+ if self.punc_model.with_vad():
+ print("This is a vad puncuation model.")
def nll(
self,
@@ -31,6 +34,8 @@
text_lengths: torch.Tensor,
punc_lengths: torch.Tensor,
max_length: Optional[int] = None,
+ vad_indexes: Optional[torch.Tensor] = None,
+ vad_indexes_lengths: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Compute negative log likelihood(nll)
@@ -49,19 +54,16 @@
else:
text = text[:, :max_length]
punc = punc[:, :max_length]
- # 1. Create a sentence pair like '<sos> w1 w2 w3' and 'w1 w2 w3 <eos>'
- # text: (Batch, Length) -> x, y: (Batch, Length + 1)
- #x = F.pad(text, [1, 0], "constant", self.eos)
- #t = F.pad(text, [0, 1], "constant", self.ignore_id)
- #for i, l in enumerate(text_lengths):
- # t[i, l] = self.sos
- #x_lengths = text_lengths + 1
+
+ if self.punc_model.with_vad():
+ # Should be VadRealtimeTransformer
+ assert vad_indexes is not None
+ y, _ = self.punc_model(text, text_lengths, vad_indexes)
+ else:
+ # Should be TargetDelayTransformer,
+ y, _ = self.punc_model(text, text_lengths)
- # 2. Forward Language model
- # x: (Batch, Length) -> y: (Batch, Length, NVocab)
- y, _ = self.punc_model(text, text_lengths)
-
- # 3. Calc negative log likelihood
+ # Calc negative log likelihood
# nll: (BxL,)
if self.training == False:
_, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
@@ -72,7 +74,8 @@
nll = torch.Tensor([f1_score]).repeat(text_lengths.sum())
return nll, text_lengths
else:
- nll = F.cross_entropy(y.view(-1, y.shape[-1]), punc.view(-1), reduction="none", ignore_index=self.ignore_id)
+ self.punc_weight = self.punc_weight.to(punc.device)
+ nll = F.cross_entropy(y.view(-1, y.shape[-1]), punc.view(-1), self.punc_weight, reduction="none", ignore_index=self.ignore_id)
# nll: (BxL,) -> (BxL,)
if max_length is None:
nll.masked_fill_(make_pad_mask(text_lengths).to(nll.device).view(-1), 0.0)
@@ -130,9 +133,16 @@
assert x_lengths.size(0) == total_num
return nll, x_lengths
- def forward(self, text: torch.Tensor, punc: torch.Tensor, text_lengths: torch.Tensor,
- punc_lengths: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
- nll, y_lengths = self.nll(text, punc, text_lengths, punc_lengths)
+ def forward(
+ self,
+ text: torch.Tensor,
+ punc: torch.Tensor,
+ text_lengths: torch.Tensor,
+ punc_lengths: torch.Tensor,
+ vad_indexes: Optional[torch.Tensor] = None,
+ vad_indexes_lengths: Optional[torch.Tensor] = None,
+ ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+ nll, y_lengths = self.nll(text, punc, text_lengths, punc_lengths, vad_indexes=vad_indexes)
ntokens = y_lengths.sum()
loss = nll.sum() / ntokens
stats = dict(loss=loss.detach())
@@ -145,5 +155,12 @@
text_lengths: torch.Tensor) -> Dict[str, torch.Tensor]:
return {}
- def inference(self, text: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
- return self.punc_model(text, text_lengths)
+ def inference(self,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
+ vad_indexes: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, None]:
+ if self.punc_model.with_vad():
+ assert vad_indexes is not None
+ return self.punc_model(text, text_lengths, vad_indexes)
+ else:
+ return self.punc_model(text, text_lengths)
--
Gitblit v1.9.1