speech_recognition/FunASR
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update seaco finetune

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This commit is contained in:
维石 2024-04-17 14:33:24 +08:00
parent a65016e23e
commit 149063ced4
2 changed files with 10 additions and 8 deletions
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3
funasr/models/paraformer/model.py
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@ -181,8 +181,6 @@ class Paraformer(torch.nn.Module):
text: (Batch, Length)
text_lengths: (Batch,)
"""
# import pdb;
# pdb.set_trace()
if len(text_lengths.size()) > 1:
text_lengths = text_lengths[:, 0]
if len(speech_lengths.size()) > 1:
@ -190,7 +188,6 @@ class Paraformer(torch.nn.Module):
batch_size = speech.shape[0]
# Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)

15
funasr/models/seaco_paraformer/model.py
View File

@ -97,7 +97,8 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
smoothing=seaco_lsm_weight,
normalize_length=seaco_length_normalized_loss,
)
self.train_decoder = kwargs.get("train_decoder", False)
self.train_decoder = kwargs.get("train_decoder", True)
self.seaco_weight = kwargs.get("seaco_weight", 0.01)
self.NO_BIAS = kwargs.get("NO_BIAS", 8377)
self.predictor_name = kwargs.get("predictor")
@ -117,9 +118,10 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
text: (Batch, Length)
text_lengths: (Batch,)
"""
text_lengths = text_lengths.squeeze()
speech_lengths = speech_lengths.squeeze()
assert text_lengths.dim() == 1, text_lengths.shape
if len(text_lengths.size()) > 1:
text_lengths = text_lengths[:, 0]
if len(speech_lengths.size()) > 1:
speech_lengths = speech_lengths[:, 0]
# Check that batch_size is unified
assert (
speech.shape[0]
@ -131,6 +133,8 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
hotword_pad = kwargs.get("hotword_pad")
hotword_lengths = kwargs.get("hotword_lengths")
seaco_label_pad = kwargs.get("seaco_label_pad")
if len(hotword_lengths.size()) > 1:
hotword_lengths = hotword_lengths[:, 0]
batch_size = speech.shape[0]
# for data-parallel
@ -156,11 +160,12 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
loss_att, acc_att = self._calc_att_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
loss = loss_seaco + loss_att
loss = loss_seaco + loss_att * self.seaco_weight
stats["loss_att"] = torch.clone(loss_att.detach())
stats["acc_att"] = acc_att
else:
loss = loss_seaco
stats["loss_seaco"] = torch.clone(loss_seaco.detach())
stats["loss"] = torch.clone(loss.detach())