Kshitijpawar
commented
4 months ago trainer = CTCTrainer(
model=model,
data_collator=data_collator,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=processor.feature_extractor,
)In the above code replace CTCTrainer with PyTorch trainer class Trainer. This tutorial is outdated due to newer versions of acclerate and transformers.
Error while trying the regression model
C:\Users\XTEND\anaconda3\envs\ftorch_gpu\python.exe "C:/Program Files/JetBrains/PyCharm Community Edition 2022.3.2/plugins/python-ce/helpers/pydev/pydevd.py" --multiprocess --qt-support=auto --client 127.0.0.1 --port 63293 --file C:\Users\XTEND\PycharmProjects\Regression_Wav2vec\regression_model_train.py Connected to pydev debugger (build 223.8617.48) Dataset({ features: ['name', 'path', 'emotion'], num_rows: 6925 }) Dataset({ features: ['name', 'path', 'emotion'], num_rows: 1732 }) A regression problem with 3 items: [0, 1, 2] C:\Users\XTEND\anaconda3\envs\ftorch_gpu\lib\site-packages\transformers\configuration_utils.py:380: UserWarning: Passing
gradient_checkpointingto a config initialization is deprecated and will be removed in v5 Transformers. Usingmodel.gradient_checkpointing_enable()instead, or if you are using theTrainerAPI, passgradient_checkpointing=Truein yourTrainingArguments. warnings.warn( regression Ignored unknown kwarg option normalize Ignored unknown kwarg option normalize Ignored unknown kwarg option normalize Ignored unknown kwarg option normalize The target sampling rate: 16000 Map: 100%|██████████| 100/100 [00:01<00:00, 60.78 examples/s] Map: 100%|██████████| 100/100 [00:01<00:00, 56.42 examples/s] Some weights of Wav2Vec2ForSpeechClassification were not initialized from the model checkpoint at lighteternal/wav2vec2-large-xlsr-53-greek and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original1'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. 0%| | 0/60 [00:00<?, ?it/s]C:\Users\XTEND\anaconda3\envs\ftorch_gpu\lib\site-packages\torch\amp\autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling warnings.warn( C:\Users\XTEND\anaconda3\envs\ftorch_gpu\lib\site-packages\torch\utils\checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( Traceback (most recent call last): File "C:\Users\XTEND\anaconda3\envs\ftorch_gpu\lib\contextlib.py", line 153, in exit self.gen.throw(typ, value, traceback) File "C:\Users\XTEND\anaconda3\envs\ftorch_gpu\lib\site-packages\accelerate\accelerator.py", line 988, in accumulate yield File "C:\Users\XTEND\anaconda3\envs\ftorch_gpu\lib\site-packages\transformers\trainer.py", line 1892, in _inner_training_loop tr_loss_step = self.training_step(model, inputs) File "C:\Users\XTEND\PycharmProjects\Regression_Wav2vec\regression_model_train.py", line 456, in training_step self.scaler.scale(loss).backward() AttributeError: 'CTCTrainer' object has no attribute 'scaler' python-BaseException 0%| | 0/60 [00:18<?, ?it/s]Process finished with exit code -1073741510 (0xC000013A: interrupted by Ctrl+C)