Show HN: Built speech models for Europe, accidentally topped Open-ASR in English (www.reson8.dev)

Resonant-1, a newly developed speech recognition model, has achieved the lowest average Word Error Rate (WER) across various short-form English benchmarks, outperforming established models in Open-ASR. Notably, its variant, resonant-1-flash, excels in speed, achieving real-time processing of 1 hour of speech in under 3 seconds without sacrificing accuracy. This breakthrough demonstrates the model's efficiency and potential for practical applications in real-time communication technologies. The significance of this achievement lies not only in its performance for English but also in its impressive results across multiple European languages, including French, Dutch, Spanish, and Polish. By leading in WER metrics, Resonant-1 showcases its capacity for multilingual capabilities, an essential aspect in the growing AI/ML community focused on natural language processing. The developers noted that while datasets like Librispeech and Voxpopuli were excluded from the evaluation to avoid contamination, training on them did enhance performance, highlighting the delicate balance of data usage and model generalization. This progress suggests a promising avenue for future developments in speech recognition and multilingual applications.