Whisper Speech-to-Text Model
| Property | Value |
|---|---|
| Model Type | Speech-to-Text |
| Base Architecture | OpenAI Whisper-Base |
| Quantization | FP16 |
| Dataset | Mozilla Common Voice 13.0 |
| Word Error Rate | 8.2% |
| Character Error Rate | 4.5% |
What is whisper-speech-text?
The whisper-speech-text model is a fine-tuned version of OpenAI's Whisper-Base architecture, specifically optimized for speech-to-text transcription tasks. Developed by AventIQ-AI, this model has been trained on the Mozilla Common Voice 13.0 dataset to provide accurate and efficient speech recognition capabilities while maintaining a smaller footprint through FP16 quantization.
Implementation Details
The model is implemented using the Hugging Face Transformers framework and can be easily integrated into existing pipelines. It supports both CPU and CUDA execution, automatically detecting available hardware resources. The training process involved 3 epochs with a batch size of 8, focused on optimizing for transcription accuracy while maintaining computational efficiency.
- FP16 quantization for reduced model size and faster inference
- Built-in support for both CPU and GPU execution
- Simple integration through Hugging Face Transformers library
- Optimized for real-world speech recognition scenarios
Core Capabilities
- High-accuracy speech transcription with 8.2% WER
- Efficient processing through quantization
- Support for various audio input formats
- Robust performance across different speech patterns
- Easy-to-use API for speech recognition tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out through its optimized balance between accuracy and efficiency, achieved through careful fine-tuning on the Mozilla Common Voice dataset and FP16 quantization. The low WER of 8.2% and CER of 4.5% demonstrate its high performance while maintaining deployment flexibility.
Q: What are the recommended use cases?
The model is ideal for applications requiring accurate speech-to-text conversion, such as transcription services, subtitle generation, and voice command systems. However, it may have limitations with highly noisy environments or overlapping speech, and performance can vary across different accents and dialects.
