Feature/conversations tarred
nice! 
1. can u add an example jsonl manifest in the PR description to show the idea after tarring?
2. can this PR get rid of pre-creating lhotse shar in speech-to-speech training example since now we have codec on the fly extraction on audio? 
+@subhankar-ghosh 
# What does this PR do ?

Support for multimodal conversation tar format writing and reading.

Script usage:
```bash
python scripts/speech_llm/export_conversations_to_tar.py --shard-size 100 \
  in_manifest_conversations.jsonl \
  out_tarred_conversations
```

Example output:
```
$ out_tarred_conversations
audio_0.tar  audio_1.tar  audio_2.tar  manifest_0.jsonl  manifest_1.jsonl  manifest_2.jsonl
```

Example input_cfg for dataloader initialization:

```yaml
- input_cfg:
  - type: multimodal_conversation
     manifest_filepath: out_tarred_conversations/manifest_{0..2}.jsonl
     tarred_audio_filepaths: out_tarred_conversations/audio_{0..2}.tar
     audio_locator_tag: "[audio]"
```

Manifest JSONL format. One conversation per line. Mixed audio and text turns in user and assistant roles, supports {text,audio}-to-{text,audio}.
```
{"id":"page_0026_120644_conversation2","conversations":[{"value":"/data/conversations/mydataset/audio/user_turn0.wav","from":"User","type":"audio","duration":5.31},{"value":"Automatic speech recognition is a technology that allows computers to recognize and transcribe spoken language. In the NeMo Framework, ASR is used for tasks such as speech-to-text and voice recognition.","from":"Assistant","type":"text"},{"value":"/data/conversations/mydataset/audio/user_turn1.wav","from":"User","type":"audio","duration":5.61},{"value":"The NeMo Framework provides a range of tools and features for training and deploying ASR models, including model parallelism, data parallelism, and distributed checkpointing. This allows for faster training and inference times, as well as improved model accuracy and reliability.","from":"Assistant","type":"text"}]}
```

Unrolled for readability:

```
{
  "id": "page_0026_120644_conversation2",
  "conversations": [
    {
      "value": "/data/conversations/mydataset/audio/user_turn0.wav",
      "from": "User",
      "type": "audio",
      "duration": 5.31
    },
    {
      "value": "Automatic speech recognition is a technology that allows computers to recognize and transcribe spoken language. In the NeMo Framework, ASR is used for tasks such as speech-to-text and voice recognition.",
      "from": "Assistant",
      "type": "text"
    },
    {
      "value": "/data/conversations/mydataset/audio/user_turn1.wav",
      "from": "User",
      "type": "audio",
      "duration": 5.61
    },
    {
      "value": "The NeMo Framework provides a range of tools and features for training and deploying ASR models, including model parallelism, data parallelism, and distributed checkpointing. This allows for faster training and inference times, as well as improved model accuracy and reliability.",
      "from": "Assistant",
      "type": "text"
    }
  ]
}
```

The format stays the same after being exported to tarfile, but the audio turns are filled with the contents of the corresponding tar files on the fly.

JSONL manifest after tar export:

```
{
  "id": "page_0026_120644_conversation2",
  "conversations": [
    {
      "type": "audio",
      "from": "User",
      "duration": 5.3058125,
      "value": "user_turn0.flac"
    },
    {
      "type": "text",
      "from": "Assistant",
      "value": "Automatic speech recognition is a technology that allows computers to recognize and transcribe spoken language. In the NeMo Framework, ASR is used for tasks such as speech-to-text and voice recognition."
    },
    {
      "type": "audio",
      "from": "User",
      "duration": 5.607625,
      "value": "user_turn1.flac"
    },
    {
      "type": "text",
      "from": "Assistant",
      "value": "The NeMo Framework provides a range of tools and features for training and deploying ASR models, including model parallelism, data parallelism, and distributed checkpointing. This allows for faster training and inference times, as well as improved model accuracy and reliability."
    }
  ]
}
```

**Collection**: multimodal

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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