From e358063f03eb38ce258a2823b65b043484261341 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 24 四月 2023 23:15:20 +0800
Subject: [PATCH] update
---
funasr/models/e2e_asr_paraformer.py | 87 +------------------------------------------
1 files changed, 2 insertions(+), 85 deletions(-)
diff --git a/funasr/models/e2e_asr_paraformer.py b/funasr/models/e2e_asr_paraformer.py
index 605a95c..f414e4f 100644
--- a/funasr/models/e2e_asr_paraformer.py
+++ b/funasr/models/e2e_asr_paraformer.py
@@ -92,16 +92,7 @@
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
- self.preencoder = preencoder
- self.postencoder = postencoder
self.encoder = encoder
-
- if not hasattr(self.encoder, "interctc_use_conditioning"):
- self.encoder.interctc_use_conditioning = False
- if self.encoder.interctc_use_conditioning:
- self.encoder.conditioning_layer = torch.nn.Linear(
- vocab_size, self.encoder.output_size()
- )
self.error_calculator = None
@@ -170,9 +161,7 @@
# 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
@@ -189,30 +178,6 @@
# 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:
@@ -281,29 +246,8 @@
if self.normalize is not None:
feats, feats_lengths = self.normalize(feats, feats_lengths)
- # Pre-encoder, e.g. used for raw input data
- if self.preencoder is not None:
- feats, feats_lengths = self.preencoder(feats, feats_lengths)
-
# 4. Forward encoder
- # feats: (Batch, Length, Dim)
- # -> encoder_out: (Batch, Length2, Dim2)
- if self.encoder.interctc_use_conditioning:
- encoder_out, encoder_out_lens, _ = self.encoder(
- feats, feats_lengths, ctc=self.ctc
- )
- else:
- encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
- intermediate_outs = None
- if isinstance(encoder_out, tuple):
- intermediate_outs = encoder_out[1]
- encoder_out = encoder_out[0]
-
- # Post-encoder, e.g. NLU
- if self.postencoder is not None:
- encoder_out, encoder_out_lens = self.postencoder(
- encoder_out, encoder_out_lens
- )
+ encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
assert encoder_out.size(0) == speech.size(0), (
encoder_out.size(),
@@ -313,9 +257,6 @@
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
@@ -340,32 +281,8 @@
if self.normalize is not None:
feats, feats_lengths = self.normalize(feats, feats_lengths)
- # Pre-encoder, e.g. used for raw input data
- if self.preencoder is not None:
- feats, feats_lengths = self.preencoder(feats, feats_lengths)
-
# 4. Forward encoder
- # feats: (Batch, Length, Dim)
- # -> encoder_out: (Batch, Length2, Dim2)
- if self.encoder.interctc_use_conditioning:
- encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(
- feats, feats_lengths, cache=cache["encoder"], ctc=self.ctc
- )
- else:
- encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(feats, feats_lengths, cache=cache["encoder"])
- intermediate_outs = None
- if isinstance(encoder_out, tuple):
- intermediate_outs = encoder_out[1]
- encoder_out = encoder_out[0]
-
- # Post-encoder, e.g. NLU
- if self.postencoder is not None:
- encoder_out, encoder_out_lens = self.postencoder(
- encoder_out, encoder_out_lens
- )
-
- if intermediate_outs is not None:
- return (encoder_out, intermediate_outs), encoder_out_lens
+ encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(feats, feats_lengths, cache=cache["encoder"])
return encoder_out, torch.tensor([encoder_out.size(1)])
--
Gitblit v1.9.1