From 73959c6e8e80a6e735bbb7d63acf942a6e2f652d Mon Sep 17 00:00:00 2001
From: hnluo <haoneng.lhn@alibaba-inc.com>
Date: 星期四, 07 十二月 2023 14:54:25 +0800
Subject: [PATCH] Merge pull request #1157 from alibaba-damo-academy/dev_lhn
---
funasr/models/e2e_asr.py | 3 +
funasr/models/e2e_asr_paraformer.py | 57 ++++++++++++++++++----------
funasr/models/e2e_asr_contextual_paraformer.py | 3 +
funasr/models/e2e_uni_asr.py | 3 +
4 files changed, 46 insertions(+), 20 deletions(-)
diff --git a/funasr/models/e2e_asr.py b/funasr/models/e2e_asr.py
index 79c5387..162bfba 100644
--- a/funasr/models/e2e_asr.py
+++ b/funasr/models/e2e_asr.py
@@ -122,6 +122,7 @@
self.ctc = ctc
self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
+ self.length_normalized_loss = length_normalized_loss
def forward(
self,
@@ -220,6 +221,8 @@
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = (text_lengths + 1).sum().type_as(batch_size)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
diff --git a/funasr/models/e2e_asr_contextual_paraformer.py b/funasr/models/e2e_asr_contextual_paraformer.py
index a2f7078..d4dc784 100644
--- a/funasr/models/e2e_asr_contextual_paraformer.py
+++ b/funasr/models/e2e_asr_contextual_paraformer.py
@@ -125,6 +125,7 @@
if self.crit_attn_weight > 0:
self.attn_loss = torch.nn.L1Loss()
self.crit_attn_smooth = crit_attn_smooth
+ self.length_normalized_loss = length_normalized_loss
def forward(
self,
@@ -231,6 +232,8 @@
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
diff --git a/funasr/models/e2e_asr_paraformer.py b/funasr/models/e2e_asr_paraformer.py
index e157454..b793d52 100644
--- a/funasr/models/e2e_asr_paraformer.py
+++ b/funasr/models/e2e_asr_paraformer.py
@@ -137,6 +137,7 @@
self.predictor_bias = predictor_bias
self.sampling_ratio = sampling_ratio
self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
+ self.length_normalized_loss = length_normalized_loss
self.step_cur = 0
self.share_embedding = share_embedding
@@ -253,6 +254,8 @@
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
@@ -352,8 +355,9 @@
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
encoder_out.device)
- pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(encoder_out, None, encoder_out_mask,
- ignore_id=self.ignore_id)
+ pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(encoder_out, None,
+ encoder_out_mask,
+ ignore_id=self.ignore_id)
return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
@@ -487,8 +491,9 @@
if self.step_cur < 2:
logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
if self.use_1st_decoder_loss:
- sematic_embeds, decoder_out_1st, pre_loss_att = self.sampler_with_grad(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
- pre_acoustic_embeds)
+ sematic_embeds, decoder_out_1st, pre_loss_att = self.sampler_with_grad(encoder_out, encoder_out_lens,
+ ys_pad, ys_pad_lens,
+ pre_acoustic_embeds)
else:
sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
pre_acoustic_embeds)
@@ -727,6 +732,7 @@
self.predictor = predictor
self.predictor_weight = predictor_weight
self.predictor_bias = predictor_bias
+ self.length_normalized_loss = length_normalized_loss
self.sampling_ratio = sampling_ratio
self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
self.step_cur = 0
@@ -860,11 +866,13 @@
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
def encode(
- self, speech: torch.Tensor, speech_lengths: torch.Tensor, ind: int = 0,
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor, ind: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Frontend + Encoder. Note that this method is used by asr_inference.py
Args:
@@ -885,7 +893,7 @@
# 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)
@@ -970,11 +978,11 @@
return encoder_out, torch.tensor([encoder_out.size(1)])
def _calc_att_predictor_loss(
- self,
- encoder_out: torch.Tensor,
- encoder_out_lens: torch.Tensor,
- ys_pad: torch.Tensor,
- ys_pad_lens: torch.Tensor,
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
):
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
encoder_out.device)
@@ -1006,7 +1014,7 @@
attention_chunk_center_bias = 0
attention_chunk_size = encoder_chunk_size
decoder_att_look_back_factor = self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
- mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls.\
+ mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls. \
get_mask_shift_att_chunk_decoder(None,
device=encoder_out.device,
batch_size=encoder_out.size(0)
@@ -1106,7 +1114,8 @@
input_mask_expand_dim, 0)
return sematic_embeds * tgt_mask, decoder_out * tgt_mask
- def sampler_with_grad(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds, chunk_mask=None):
+ def sampler_with_grad(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds,
+ chunk_mask=None):
tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
if self.share_embedding:
@@ -1158,7 +1167,7 @@
target_label_length=None,
)
predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
- encoder_out_lens+1 if self.predictor.tail_threshold > 0.0 else encoder_out_lens)
+ encoder_out_lens + 1 if self.predictor.tail_threshold > 0.0 else encoder_out_lens)
scama_mask = None
if self.encoder.overlap_chunk_cls is not None and self.decoder_attention_chunk_type == 'chunk':
@@ -1166,7 +1175,7 @@
attention_chunk_center_bias = 0
attention_chunk_size = encoder_chunk_size
decoder_att_look_back_factor = self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
- mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls.\
+ mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls. \
get_mask_shift_att_chunk_decoder(None,
device=encoder_out.device,
batch_size=encoder_out.size(0)
@@ -1484,6 +1493,8 @@
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
@@ -1589,8 +1600,9 @@
if self.predictor_bias == 1:
_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
ys_pad_lens = ys_pad_lens + self.predictor_bias
- pre_acoustic_embeds, pre_token_length, _, pre_peak_index, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
- ignore_id=self.ignore_id)
+ pre_acoustic_embeds, pre_token_length, _, pre_peak_index, _ = self.predictor(encoder_out, ys_pad,
+ encoder_out_mask,
+ ignore_id=self.ignore_id)
# 0. sampler
decoder_out_1st = None
@@ -1739,7 +1751,7 @@
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
+ 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
@@ -1752,6 +1764,8 @@
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
@@ -1952,6 +1966,8 @@
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
@@ -2107,7 +2123,8 @@
return loss_att, acc_att, cer_att, wer_att, loss_pre
- def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None, clas_scale=1.0):
+ def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None,
+ clas_scale=1.0):
if hw_list is None:
# default hotword list
hw_list = [torch.Tensor([self.sos]).long().to(encoder_out.device)] # empty hotword list
@@ -2245,4 +2262,4 @@
"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
var_dict_tf[name_tf].shape))
- return var_dict_torch_update
+ return var_dict_torch_update
\ No newline at end of file
diff --git a/funasr/models/e2e_uni_asr.py b/funasr/models/e2e_uni_asr.py
index 8bc3b42..45d90f1 100644
--- a/funasr/models/e2e_uni_asr.py
+++ b/funasr/models/e2e_uni_asr.py
@@ -167,6 +167,7 @@
self.enable_maas_finetune = enable_maas_finetune
self.freeze_encoder2 = freeze_encoder2
self.encoder1_encoder2_joint_training = encoder1_encoder2_joint_training
+ self.length_normalized_loss = length_normalized_loss
def forward(
self,
@@ -440,6 +441,8 @@
stats["loss2"] = torch.clone(loss2.detach())
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = (text_lengths + 1).sum().type_as(batch_size)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
--
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