train_dataset does not implement __len__
when train using data  precessored by the datasets, I get follow warning and it leads to that I can not set epoch numbers:
`ValueError: The train_dataset does not implement __len__, max_steps has to be specified. The number of steps needs to be known in advance for the learning rate scheduler.`
Thanks for reporting, @v-yunbin.

Could you please give more details, the steps to reproduce the bug, the complete error back trace and the environment information (`datasets-cli env`)? this is a detail error info from transformers：
```
Traceback (most recent call last):
  File "finetune.py", line 177, in <module>
    whisper_finetune(traindir,devdir,outdir)
  File "finetune.py", line 161, in whisper_finetune
    trainer = Seq2SeqTrainer(
  File "/home/ybZhang/miniconda3/envs/whister/lib/python3.8/site-packages/transformers/trainer_seq2seq.py", line 56, in __init__
    super().__init__(
  File "/home/ybZhang/miniconda3/envs/whister/lib/python3.8/site-packages/transformers/trainer.py", line 567, in __init__
    raise ValueError(
ValueError: The train_dataset does not implement __len__, max_steps has to be specified. The number of steps needs to be known in advance for the learning rate scheduler.
```
 How did you create `train_dataset`? The `datasets` library does not appear in your stack trace.

We need more information in order to reproduce the issue... ```
def asr_dataset(traindir,devdir):
    we_voice = IterableDatasetDict()
    #we_voice["train"] = load_from_disk(traindir,streaming=True)
    #we_voice["test"]= load_from_disk(devdir,streaming=True)
    we_voice["train"] = load_dataset("csv",data_files=os.path.join(traindir,"train.csv"),split="train",streaming=True)
    #print(load_dataset("csv",data_files=os.path.join(traindir,"train.csv"),split="train"))
    we_voice["test"] = load_dataset("csv",data_files=os.path.join(devdir,"dev.csv"), split="train",streaming=True)
    we_voice = we_voice.remove_columns(["id"])
    we_voice = we_voice.cast_column("audio", Audio(sampling_rate=16000))
    return we_voice

``` As you are using iterable datasets (`streaming=True`), their length is not defined.

You should:
- Either use non-iterable datasets, which have a defined length: use `DatasetDict` and not passing `streaming=True`
- Or pass `args.max_steps` to the `Trainer` I don't know how to give a reasonable args.max_steps........................... Then you should not use streaming. @albertvillanova I think @v-yunbin, myself, and others might be slightly confused about max_steps and how it differs from num_train_epochs. @lkurlandski A **step** is referring to optimizer's update after back propagation, and it's associated with a batch of data. For example, if a dataset contains 64 examples and you have an overall batch size of 4, then an epoch will have 64/4=16 batches. Therefore, in one epoch, you will have 16 optimizer **steps**.