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:
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:
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:
# 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:
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:
# 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_durationaccording 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.