Conformer2 vs Whisper (OpenAI)
Side-by-side comparison · Updated May 2026
| Description | Conformer-2 is AssemblyAI's latest AI model for automatic speech recognition, designed to enhance performance on proper nouns, alphanumerics, and resistance to noise. Trained on an extensive dataset of 1.1M hours of English audio, Conformer-2 builds on the success of Conformer-1, providing a substantial 31.7% improvement on alphanumerics, a 6.8% improvement on Proper Noun Error Rate, and a 12.0% boost in noise robustness. Additionally, it maintains Conformer-1's word error rate while significantly reducing latency by up to 53.7%. | Whisper is a cutting-edge automatic speech recognition (ASR) system created by OpenAI. Trained on 680,000 hours of multilingual and multitask supervised data from the web, Whisper boasts improved robustness to accents, background noise, and technical language. It provides transcription services in multiple languages and translates those languages into English. Whisper uses an encoder-decoder Transformer architecture that captures 30-second audio chunks, converts them to log-Mel spectrograms, and predicts corresponding text captions. Its large and diverse dataset helps Whisper outperform existing systems in zero-shot performance across diverse scenarios. |
| Category | Speech-To-Text | Speech-To-Text |
| Rating | No reviews | No reviews |
| Pricing | Pricing unavailable | Free |
| Starting Price | N/A | Free |
| Plans | — |
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| Tags | AI modelautomatic speech recognitionConformer-2proper nounsalphanumerics | Automatic Speech RecognitionASRSpeech RecognitionTranscriptionTranslation |
| Features | ||
| 31.7% improvement on alphanumerics | ||
| 6.8% improvement on Proper Noun Error Rate | ||
| 12.0% boost in noise robustness | ||
| Trained on 1.1M hours of English audio | ||
| Maintains word error rate parity with Conformer-1 | ||
| Up to 53.7% reduction in latency | ||
| Enhanced performance in real-world audio conditions | ||
| Improved transcription accuracy | ||
| Increased number of models used for pseudo-labeling data | ||
| Developed by AssemblyAI | ||
| High robustness to accents and background noise | ||
| Supports multiple languages | ||
| Translates languages into English | ||
| Encoder-decoder Transformer architecture | ||
| Processes 30-second audio chunks | ||
| Predicts text captions with special tokens integration | ||
| Improved zero-shot performance | ||
| Open-source with detailed resources | ||
| Enables voice interfaces for applications | ||
| Outperforms on CoVoST2 for English translation | ||
| View Conformer2 | View Whisper (OpenAI) | |
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