Can't reproduce training of wav2vec2-large from documentation
### System Info

```shell
- `transformers` version: 4.19.0.dev0
- Platform: Linux-5.4.0-109-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.11.0+cu102 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
```


### Who can help?

@patrickvonplaten 

### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Pretraining a wav2vec-large model using the documentation under [examples/speech-pretraining](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-pretraining) does not work. 

Running the following code (copy-pasted from the README) gives an error due to `model_path_or_dir` not found:
```
accelerate launch run_wav2vec2_pretraining_no_trainer.py \ 
	--dataset_name=librispeech_asr \
	--dataset_config_names clean clean other \
	--dataset_split_names train.100 train.360 train.500 \
	--output_dir=./test \
	--max_train_steps=200000 \
	--num_warmup_steps=32000 \
	--gradient_accumulation_steps=8 \
	--learning_rate=0.001 \
	--weight_decay=0.01 \
	--max_duration_in_seconds=20.0 \
	--min_duration_in_seconds=2.0 \
	--model_name_or_path=./ 
	--logging_steps=1 \
	--saving_steps=10000 \
	--per_device_train_batch_size=2 \
	--per_device_eval_batch_size=4 \
	--adam_beta1=0.9 \
	--adam_beta2=0.98 \
	--adam_epsilon=1e-06 \
	--gradient_checkpointing \
```

I tried using ´facebook/wav2vec-large-lv60' in `model_name_or_path` but receive the following error:
```
Traceback (most recent call last):
  File "run_wav2vec2_pretraining_no_trainer.py", line 730, in <module>
    main()
  File "run_wav2vec2_pretraining_no_trainer.py", line 572, in main
    for step, batch in enumerate(train_dataloader):
  File "/home/ucloud/.local/lib/python3.8/site-packages/accelerate/data_loader.py", line 303, in __iter__    for batch in super().__iter__():  File "/home/ucloud/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 530, in __next__    data = self._next_data()
  File "/home/ucloud/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 570, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/home/ucloud/.local/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
    return self.collate_fn(data)
  File "run_wav2vec2_pretraining_no_trainer.py", line 326, in __call__
    sampled_negative_indices = _sample_negative_indices(
  File "/home/ucloud/.local/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 336, in _sample_negative_indices
    sampled_indices = np.random.randint(0, high, size=(high + 1, num_negatives))
  File "mtrand.pyx", line 748, in numpy.random.mtrand.RandomState.randint
  File "_bounded_integers.pyx", line 1247, in numpy.random._bounded_integers._rand_int64
ValueError: high <= 0
```

The demo script trains without issue. Using the parameters from the demo script and changing `model_name_or_path` from 'patrickvonplaten/wav2vec2-base-v2` to ´facebook/wav2vec-large-lv60´ gives the above error. 

Training on a single T4 GPU (benchmarking purposes)

### Expected behavior

```shell
Wav2vec-large pretraining to run.
```

Hey @HLasse,

Could you increase this parameter: https://huggingface.co/facebook/wav2vec2-large-lv60/blob/main/config.json#L62 to `0.5` and see if it works then? It seems like given the sequence length you are not sampling enough negative targets.

Also it'll be really hard / impossible to do a full pretraining on a single T4 GPU<|||||>That works, thanks! 

> Also it'll be really hard / impossible to do a full pretraining on a single T4 GPU

I know - this was mainly to get an estimate of training time on different hardware setups. Danish wav2vec models coming up soon! :) 