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Conformer-2: Advanced AI Model for Speech Recognition
Freemium

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Conformer Features

Conformer-2: Revolutionizing Speech Recognition

Conformer-2 is an advanced AI model designed specifically for automatic speech recognition (ASR). It builds upon the success of its predecessor, Conformer-1, and has been trained on a massive dataset of 1.1 million hours of English audio. This extensive training has resulted in remarkable improvements in various aspects of speech recognition.

Focus Areas: Conformer-2 aims to enhance the recognition of proper nouns, alphanumerics, and noise robustness. By prioritizing these critical areas, the model significantly improves its ability to accurately transcribe spoken content.

Scaling Laws and Training Data: The development of Conformer-2 follows the scaling laws proposed in DeepMind’s Chinchilla paper. Recognizing the importance of sufficient training data, Conformer-2 leverages a massive 1.1 million hours of English audio data during its training process.

Ensembling Technique: One of the standout features of Conformer-2 is its use of model ensembling. Instead of relying on predictions from a single teacher model, Conformer-2 generates labels from multiple strong teachers. This technique reduces variance and enhances the model’s performance when dealing with previously unseen data during training.

Improved Speed and Processing: Despite its increased model size, Conformer-2 exhibits improvements in terms of speed compared to Conformer-1. The serving infrastructure has been meticulously optimized, resulting in faster processing times. Conformer-2 achieves up to a 55% reduction in relative processing duration across all audio file durations.

Real-World Performance: In real-world applications, Conformer-2 demonstrates significant enhancements in various user-oriented metrics. Notably, it achieves a 31.7% improvement on alphanumerics, a 6.8% improvement on proper noun error rate, and a 12.0% improvement in noise robustness. These enhancements are attributed to both the vast training data and the use of an ensemble of models.

Ideal for AI Pipelines: The Conformer-2 model is an invaluable component for AI pipelines focusing on generative AI applications using spoken data. Its exceptional speech-to-text transcription capabilities make it a valuable tool for generating accurate transcriptions with precision and reliability.

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