From 2e769fb36ce88dabfa984e8b81e8cb1c90799c95 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 07 四月 2023 15:54:09 +0800
Subject: [PATCH] Merge branch 'main' into dev_cmz2
---
funasr/models/e2e_asr_paraformer.py | 90 ++++++++++++++++++++++++++++++++++++++-------
1 files changed, 76 insertions(+), 14 deletions(-)
diff --git a/funasr/models/e2e_asr_paraformer.py b/funasr/models/e2e_asr_paraformer.py
index 02f60af..f1bb2bf 100644
--- a/funasr/models/e2e_asr_paraformer.py
+++ b/funasr/models/e2e_asr_paraformer.py
@@ -370,19 +370,10 @@
encoder_out, encoder_out_lens
)
- assert encoder_out.size(0) == speech.size(0), (
- encoder_out.size(),
- speech.size(0),
- )
- assert encoder_out.size(1) <= encoder_out_lens.max(), (
- encoder_out.size(),
- encoder_out_lens.max(),
- )
-
if intermediate_outs is not None:
return (encoder_out, intermediate_outs), encoder_out_lens
- return encoder_out, encoder_out_lens
+ return encoder_out, torch.tensor([encoder_out.size(1)])
def calc_predictor(self, encoder_out, encoder_out_lens):
@@ -1034,16 +1025,76 @@
# 1. Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ intermediate_outs = None
+ if isinstance(encoder_out, tuple):
+ intermediate_outs = encoder_out[1]
+ encoder_out = encoder_out[0]
+ loss_att, acc_att, cer_att, wer_att = None, None, None, None
+ loss_ctc, cer_ctc = None, None
+ loss_pre = None
stats = dict()
+
+ # 1. CTC branch
+ if self.ctc_weight != 0.0:
+ loss_ctc, cer_ctc = self._calc_ctc_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # Collect CTC branch stats
+ stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
+ stats["cer_ctc"] = cer_ctc
+
+ # Intermediate CTC (optional)
+ loss_interctc = 0.0
+ if self.interctc_weight != 0.0 and intermediate_outs is not None:
+ for layer_idx, intermediate_out in intermediate_outs:
+ # we assume intermediate_out has the same length & padding
+ # as those of encoder_out
+ loss_ic, cer_ic = self._calc_ctc_loss(
+ intermediate_out, encoder_out_lens, text, text_lengths
+ )
+ loss_interctc = loss_interctc + loss_ic
+
+ # Collect Intermedaite CTC stats
+ stats["loss_interctc_layer{}".format(layer_idx)] = (
+ loss_ic.detach() if loss_ic is not None else None
+ )
+ stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
+
+ loss_interctc = loss_interctc / len(intermediate_outs)
+
+ # calculate whole encoder loss
+ loss_ctc = (
+ 1 - self.interctc_weight
+ ) * loss_ctc + self.interctc_weight * loss_interctc
+
+ # 2b. Attention decoder branch
+ if self.ctc_weight != 1.0:
+ loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
loss_pre2 = self._calc_pre2_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
- loss = loss_pre2
+ # 3. CTC-Att loss definition
+ if self.ctc_weight == 0.0:
+ loss = loss_att + loss_pre * self.predictor_weight + loss_pre2 * self.predictor_weight * 0.5
+ elif self.ctc_weight == 1.0:
+ loss = loss_ctc
+ else:
+ loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight + loss_pre2 * self.predictor_weight * 0.5
+ # Collect Attn branch stats
+ stats["loss_att"] = loss_att.detach() if loss_att is not None else None
+ stats["acc"] = acc_att
+ stats["cer"] = cer_att
+ stats["wer"] = wer_att
+ stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
stats["loss_pre2"] = loss_pre2.detach().cpu()
+
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
@@ -1094,6 +1145,7 @@
inner_dim: int = 256,
bias_encoder_type: str = 'lstm',
label_bracket: bool = False,
+ use_decoder_embedding: bool = False,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
@@ -1147,6 +1199,7 @@
self.hotword_buffer = None
self.length_record = []
self.current_buffer_length = 0
+ self.use_decoder_embedding = use_decoder_embedding
def forward(
self,
@@ -1288,7 +1341,10 @@
hw_list.append(hw_tokens)
# padding
hw_list_pad = pad_list(hw_list, 0)
- hw_embed = self.decoder.embed(hw_list_pad)
+ if self.use_decoder_embedding:
+ hw_embed = self.decoder.embed(hw_list_pad)
+ else:
+ hw_embed = self.bias_embed(hw_list_pad)
hw_embed, (_, _) = self.bias_encoder(hw_embed)
_ind = np.arange(0, len(hw_list)).tolist()
# update self.hotword_buffer, throw a part if oversize
@@ -1404,13 +1460,19 @@
# default hotword list
hw_list = [torch.Tensor([self.sos]).long().to(encoder_out.device)] # empty hotword list
hw_list_pad = pad_list(hw_list, 0)
- hw_embed = self.bias_embed(hw_list_pad)
+ if self.use_decoder_embedding:
+ hw_embed = self.decoder.embed(hw_list_pad)
+ else:
+ hw_embed = self.bias_embed(hw_list_pad)
_, (h_n, _) = self.bias_encoder(hw_embed)
contextual_info = h_n.squeeze(0).repeat(encoder_out.shape[0], 1, 1)
else:
hw_lengths = [len(i) for i in hw_list]
hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device)
- hw_embed = self.bias_embed(hw_list_pad)
+ if self.use_decoder_embedding:
+ hw_embed = self.decoder.embed(hw_list_pad)
+ else:
+ hw_embed = self.bias_embed(hw_list_pad)
hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hw_lengths, batch_first=True,
enforce_sorted=False)
_, (h_n, _) = self.bias_encoder(hw_embed)
--
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