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update seaco finetune (#1526)

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Shi Xian 2024-03-21 14:59:46 +08:00 committed by GitHub
parent 944a053a66
commit 980007a486
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3 changed files with 7 additions and 8 deletions
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2
examples/industrial_data_pretraining/seaco_paraformer/finetune.sh
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@ -10,7 +10,7 @@ gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
## option 1, download model automatically
model_name_or_model_dir="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
model_revision="v2.0.7"
## option 2, download model by git
#local_path_root=${workspace}/modelscope_models

6
funasr/models/contextual_paraformer/model.py
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@ -94,10 +94,8 @@ class ContextualParaformer(Paraformer):
text: (Batch, Length)
text_lengths: (Batch,)
"""
if len(text_lengths.size()) > 1:
text_lengths = text_lengths[:, 0]
if len(speech_lengths.size()) > 1:
speech_lengths = speech_lengths[:, 0]
text_lengths = text_lengths.squeeze()
speech_lengths = speech_lengths.squeeze()
batch_size = speech.shape[0]

7
funasr/models/seaco_paraformer/model.py
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@ -117,6 +117,8 @@ 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
# Check that batch_size is unified
assert (
@ -164,7 +166,7 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
# 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)
batch_size = (text_lengths + self.predictor_bias).sum()
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
@ -190,8 +192,7 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
# predictor forward
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
encoder_out.device)
pre_acoustic_embeds, _, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
ignore_id=self.ignore_id)
pre_acoustic_embeds = self.predictor(encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id)[0]
# decoder forward
decoder_out, _ = self.decoder(encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_lengths, return_hidden=True)
selected = self._hotword_representation(hotword_pad,