# Evaluating Transcription Models: Apple’s Latest API, OpenAI Whisper, and NVIDIA Parakeet
With the increasing need for precise and effective transcription services, a range of models have surfaced, each featuring unique advantages and limitations. Recently, Apple’s latest transcription API has attracted attention due to its speed and precision, leading to comparisons with well-known models such as OpenAI’s Whisper and NVIDIA’s Parakeet. This article examines the functionality of these transcription tools based on recent evaluations.
## Model Overview
1. **Apple’s Latest Transcription API**: A new component of Apple’s developer toolkit, this API offers quick and dependable transcription features.
2. **OpenAI Whisper Large V3 Turbo**: Renowned for its exceptional accuracy, Whisper has been a favored option for transcription tasks, although it operates at a slower pace compared to some alternatives.
3. **NVIDIA Parakeet v2**: Esteemed for its rapid processing, this model is apt for situations where prompt transcriptions are crucial, even if it sacrifices some accuracy.
## Methodology for Testing
The testing was inspired by developer Prakash Pax, who gathered 15 English audio samples, varying from 15 seconds to 2 minutes. He assessed the efficacy of the three transcription tools referenced earlier. For this investigation, I utilized a recent episode of the *9to5Mac Daily* podcast, which had a duration of 7:31 minutes. The tools were tested on an M2 Pro MacBook Pro with 16GB of RAM.
### Analysis of Error Rates
To measure each model’s performance, I computed the Character Error Rate (CER) and Word Error Rate (WER) using Hugging Face Spaces. This method allowed for a uniform assessment across all models.
## Performance Outcomes
The table below outlines the transcription times, CER, and WER for each model:
| Model | Transcription Time | Character Error Rate | Word Error Rate |
|—————————|——————–|———————-|——————|
| Parakeet v2 | 2 seconds | 5.8% | 12.3% |
| Whisper Large V3 Turbo | 40 seconds | 0.2% | 1.5% |
| Apple | 9 seconds | 1.9% | 10.3% |
### Additional Assessments
Further assessments were conducted with ChatGPT, Claude, and Gemini, which yielded slightly varied results while maintaining the overall performance trend:
#### Results from ChatGPT
| Model | Transcription Time | Character Error Rate | Word Error Rate |
|—————————|——————–|———————-|——————|
| Parakeet v2 | 2 seconds | 6.0% | 12.3% |
| Whisper Large V3 Turbo | 40 seconds | 0.4% | 1.4% |
| Apple | 9 seconds | 2.1% | 10.2% |
#### Results from Claude
| Model | Transcription Time | Character Error Rate | Word Error Rate |
|—————————|——————–|———————-|——————|
| Parakeet v2 | 2 seconds | 8.4% | 11.0% |
| Whisper Large V3 Turbo | 40 seconds | 0.1% | 1.0% |
| Apple | 9 seconds | 3.5% | 8.2% |
#### Results from Gemini
| Model | Transcription Time | Character Error Rate | Word Error Rate |
|—————————|——————–|———————-|——————|
| Parakeet v2 | 2 seconds | 7.6% | 12.3% |
| Whisper Large V3 Turbo | 40 seconds | 0.3% | 0.4% |
| Apple | 9 seconds | 3.4% | 5.3% |
## Conclusion: Which Model Reigns Supreme?
Determining which transcription model is best hinges on the specific requirements of the user.
– **Whisper** excels in accuracy, rendering it the optimal choice for high-stakes transcription where exactness is imperative.
– **Parakeet** is unbeatable in speed, making it suitable for cases demanding quick transcriptions, such as swiftly navigating through extensive recordings.
– **Apple’s API** offers an appropriate compromise between speed and accuracy, making it a robust option for general use, especially as it functions natively without reliance on external APIs.
As developers progressively embrace these innovations, the transcription services landscape is likely to change, with each model enhancing their capabilities over time.
