Multimodal conversation format dataloading
> 1. what do we still need in dataset code?

Roughly the following:

```python
def __getitem__(self, cuts):

    # existing audio / text processing code

    multimodal_convo_examples = cuts.filter(lambda c: isinstance(c, NeMoMultimodalConversation))
    if multimodal_convo_examples:
        audio_turn_cuts  = []
        formatted_chats = []
        for example in multimodal_convo_examples:
            # input_ids / context_ids / etc. will be pre-populated when you specify train_ds.prompt_format
            formatted_chats.append({"input_ids": example.input_ids, "context_ids": example.context_ids, ...})
            audio_turn_cuts.extend([turn.cut for turn in example.turns])
        audio, audio_lens = self.load_audio(audio_turn_cuts)
        batch["multimodal_conversation"] = {"audio": audio, "audio_lens": audio_lens, "input_ids": collate([c["input_ids"] for c in formatted_chats), ...}
```

> 2. we need to add model code for i) fprop multiple audios ii) replacing audio_locator_tag with audios

Yes. 

> 3. Tar the audios

Yes.
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