Common Voice dataset vs. Appen
A side-by-side comparison based on G2 review data.
| Metric | Common Voice dataset | Appen |
|---|---|---|
| Overall rating | 4.5 | 4.2 |
| Review count | 11 | 35 |
| Ease of use | 8.4 | 8.5 |
| Ease of setup | 8.6 | 8.9 |
| Quality of support | 8.3 | 8.4 |
| Meets requirements | 8.3 | 8.4 |
| NPS | 63.636364 | 36.363636 |
| Small Business | 63.6% | 51.4% |
| Mid-Market | 27.3% | 25.7% |
| Enterprise | 9.1% | 22.9% |
| Total page views | 16 | 1,012 |
| Total LLM citations | 11 | 8,281 |
Common Voice dataset
Each entry in the dataset consists of a unique MP3 and corresponding text file. Many of the 1,368 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help train the accuracy of speech recognition engines. The dataset currently consists of 1,087 validated hours in 18 languages, but we're always adding more voices and languages.
Appen
Appen collects and labels images, text, speech, audio, video, and other data to create training data used to build and continuously improve the world’s most innovative artificial intelligence systems. We offer a state of the art, licensable data annotation platform to annotate training data use cases in computer vision and natural language processing. Our platform enhances accuracy and efficiency through our Smart Labeling and Pre-Labeling features which use Machine Learning to ease human annotations.
- Useful
- Flexibility
- Ease of Use