A bug of modeling_wav2vec2.py:1033 line
transformers/models/wav2vec2/modeling_wav2vec2.py
now is :

            >>> import torch
            >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
            >>> from datasets import load_dataset
            >>> import soundfile as sf

            >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
            >>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

            >>> def map_to_array(batch):
            >>>     speech, _ = sf.read(batch["file"])
            >>>     batch["speech"] = speech
            >>>     return batch

            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
            >>> ds = ds.map(map_to_array)

            >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
            >>> logits = model(input_values).logits
            >>> predicted_ids = torch.argmax(logits, dim=-1)

            >>> transcription = processor.decode(predicted_ids[0])

            >>> # compute loss
            >>> target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST"

            >>> # wrap processor as target processor to encode labels
            >>> with processor.as_target_processor():
            >>>     labels = processor(transcription, return_tensors="pt").input_ids

            >>> loss = model(input_values, labels=labels).loss

it should be 

            >>> with processor.as_target_processor():
            >>>     labels = processor(target_transcription , return_tensors="pt").input_ids




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## Information

Model I am using (Bert, XLNet ...):

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1.
2.
3.

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## Expected behavior

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@patrickvonplaten 
Great catch @zhangbo2008!

Would you like to open a PR to fix it?<|||||>ok 
i see you have fixed it in the latest version thanks for your works.