Whisper Large V2
| Property | Value |
|---|---|
| Parameter Count | 1.54B |
| License | Apache 2.0 |
| Paper | View Paper |
| Downloads | 18.4M+ |
| Languages Supported | 99 |
What is whisper-large-v2?
Whisper Large V2 is OpenAI's state-of-the-art speech recognition model that represents a significant advancement in automatic speech recognition (ASR) technology. Trained on an impressive 680,000 hours of multilingual audio data, this model is designed to handle both transcription and translation tasks across 99 different languages. As an improvement over the original large model, V2 underwent 2.5x more training epochs with enhanced regularization for superior performance.
Implementation Details
The model employs a Transformer-based encoder-decoder architecture, utilizing a sequence-to-sequence approach for processing audio inputs. It features 1.54 billion parameters and operates using 32-bit floating-point precision. The model processes audio by converting it into log-Mel spectrograms and can handle audio segments up to 30 seconds in length, with special provisions for longer recordings through chunking.
- Transformer-based sequence-to-sequence architecture
- Supports both transcription and translation tasks
- Processes audio using log-Mel spectrograms
- Includes specialized tokens for language and task control
Core Capabilities
- Multilingual speech recognition across 99 languages
- Speech translation to English from multiple languages
- Zero-shot capability for cross-lingual translation
- Robust performance with different accents and background noise
- Long-form transcription through efficient chunking
- Timestamp prediction for audio alignment
Frequently Asked Questions
Q: What makes this model unique?
Whisper Large V2 stands out due to its extensive multilingual capabilities, robust performance across different acoustic conditions, and ability to handle both transcription and translation tasks without fine-tuning. The model's training on 680k hours of labeled data makes it particularly resilient to various accents and background noise.
Q: What are the recommended use cases?
The model is ideal for automatic speech recognition, audio transcription, and speech translation tasks. It's particularly well-suited for research applications, accessibility tools, and large-scale transcription projects. However, it's not recommended for real-time transcription out of the box or high-stakes decision-making contexts.
