# DistilWhisperModel Documentation

## Overview

The `DistilWhisperModel` is a Python class designed to handle English speech recognition tasks. It leverages the capabilities of the Whisper model, which is fine-tuned for speech-to-text processes. It is designed for both synchronous and asynchronous transcription of audio inputs, offering flexibility for real-time applications or batch processing.

## Installation

Before you can use `DistilWhisperModel`, ensure you have the required libraries installed:

```sh
pip3 install --upgrade swarms
```

## Initialization

The `DistilWhisperModel` class is initialized with the following parameters:

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `model_id` | `str` | The identifier for the pre-trained Whisper model | `"distil-whisper/distil-large-v2"` |

Example of initialization:

```python
from swarms.models import DistilWhisperModel

# Initialize with default model
model_wrapper = DistilWhisperModel()

# Initialize with a specific model ID
model_wrapper = DistilWhisperModel(model_id='distil-whisper/distil-large-v2')
```

## Attributes

After initialization, the `DistilWhisperModel` has several attributes:

| Attribute | Type | Description |
|-----------|------|-------------|
| `device` | `str` | The device used for computation (`"cuda:0"` for GPU or `"cpu"`). |
| `torch_dtype` | `torch.dtype` | The data type used for the Torch tensors. |
| `model_id` | `str` | The model identifier string. |
| `model` | `torch.nn.Module` | The actual Whisper model loaded from the identifier. |
| `processor` | `transformers.AutoProcessor` | The processor for handling input data. |

## Methods

### `transcribe`

Transcribes audio input synchronously.

**Arguments**:

| Argument | Type | Description |
|----------|------|-------------|
| `inputs` | `Union[str, dict]` | File path or audio data dictionary. |

**Returns**: `str` - The transcribed text.

**Usage Example**:

```python
# Synchronous transcription
transcription = model_wrapper.transcribe('path/to/audio.mp3')
print(transcription)
```

### `async_transcribe`

Transcribes audio input asynchronously.

**Arguments**:

| Argument | Type | Description |
|----------|------|-------------|
| `inputs` | `Union[str, dict]` | File path or audio data dictionary. |

**Returns**: `Coroutine` - A coroutine that when awaited, returns the transcribed text.

**Usage Example**:

```python
import asyncio

# Asynchronous transcription
transcription = asyncio.run(model_wrapper.async_transcribe('path/to/audio.mp3'))
print(transcription)
```

### `real_time_transcribe`

Simulates real-time transcription of an audio file.

**Arguments**:

| Argument | Type | Description |
|----------|------|-------------|
| `audio_file_path` | `str` | Path to the audio file. |
| `chunk_duration` | `int` | Duration of audio chunks in seconds. |

**Usage Example**:

```python
# Real-time transcription simulation
model_wrapper.real_time_transcribe('path/to/audio.mp3', chunk_duration=5)
```

## Error Handling

The `DistilWhisperModel` class incorporates error handling for file not found errors and generic exceptions during the transcription process. If a non-recoverable exception is raised, it is printed to the console in red to indicate failure.

## Conclusion

The `DistilWhisperModel` offers a convenient interface to the powerful Whisper model for speech recognition. Its design supports both batch and real-time transcription, catering to different application needs. The class's error handling and retry logic make it robust for real-world applications.

## Additional Notes

- Ensure you have appropriate permissions to read audio files when using file paths.
- Transcription quality depends on the audio quality and the Whisper model's performance on your dataset.
- Adjust `chunk_duration` according to the processing power of your system for real-time transcription.

For a full list of models supported by `transformers.AutoModelForSpeechSeq2Seq`, visit the [Hugging Face Model Hub](https://huggingface.co/models).
