The WhisperKit library has been revolutionizing Automatic Speech Recognition (ASR) for various audio datasets. In this blog, we will dive into the evaluation results of WhisperKit, focusing on transcription quality for different datasets, and provide insights into how you can leverage this in your projects.
Dataset Overview
WhisperKit was evaluated using multiple datasets, each designed to test transcription quality across various audio samples. The primary datasets used include:
- Librispeech: 5 hours of short English audio clips
- Earnings22: 120 hours of long-form earnings call recordings with various accents
- Common Voice 17.0: A multilingual dataset with short audio samples from up to 400 languages
Understanding WhisperKit’s Transcription Quality Metrics
WhisperKit measures transcription quality using two primary metrics:
- Word Error Rate (WER): A lower WER indicates better transcription quality.
- Quality of Inference (QoI): Higher QoI suggests that transcriptions are consistent with known examples, avoiding regressions.
Let’s break down the evaluation results with an analogy: Think of WhisperKit like a skilled chef preparing meals. The WER represents how well the chef retains the original recipe (the audio) when serving the dish (the transcription). A lower WER signifies that the dish closely resembles the original recipe, which is desirable. On the other hand, the QoI is like a restaurant’s customer satisfaction score. A high QoI means that customers (users) are returning for the same dish, indicating consistency and quality in the chef’s preparation.
The Results
| Model | WER (↓) | QoI (↑) | File Size (MB) | Code Commit |
|---|---|---|---|---|
| large-v2 (WhisperOpenAIAPI) | 2.35 | 100 | 3100 | NA |
| large-v3 | 2.04 | 95.2 | 3100 | Link |
How to Reproduce the Evaluation Results
To replicate these evaluations, you can utilize the WhisperKit tools available on GitHub. Simply follow these steps:
- Clone the WhisperKit repository from GitHub.
- Set up your evaluation environment using an Apple Silicon Mac or any compatible device.
- Run the evaluation jobs using the automated scripts provided in the repository.
Troubleshooting Common Issues
If you encounter issues during evaluation, consider the following troubleshooting tips:
- Ensure you are using a compatible Apple Silicon device.
- Verify the installation of dependencies as outlined in the repository’s documentation.
- Check if you’ve set the correct paths for your dataset files.
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Conclusion
WhisperKit serves as a powerful framework for developing speech-to-text applications across diverse datasets. The combination of low WER and high QoI provides developers with the confidence to utilize or customize ASR models for their specific needs.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.