Audio Modelspeech_to_textWhisper Family

Whisper

Whisper is an open-source automatic speech recognition (ASR) system developed by OpenAI, trained on a large, diverse dataset of multilingual and multitask supervised data collected from the web. It supports robust speech-to-text transcription, translation, and language identification across many languages and is designed to be particularly resilient to accents, background noise, and technical language.

by OpenAIReleased 2022-09-21MIT
API Access
Available

Key Capabilities

  • +Robust multilingual speech-to-text transcription
  • +Automatic speech translation to English
  • +Language identification from audio
  • +Strong robustness to accents and background noise
  • +Support for long-form audio transcription
  • +Open-source weights and inference code
  • +Runs on both GPU and CPU (with performance trade-offs)

Limitations

  • -Higher latency and compute cost on edge devices for larger model sizes
  • -May struggle with very low-resource languages or highly specialized jargon
  • -No built-in diarization (speaker separation) in the base models
  • -Quality depends on audio quality; extreme noise or clipping degrades performance
  • -On-device fine-tuning is non-trivial and not officially supported

Benchmark Performance

speech

speech

LibriSpeech Clean Test

2.5% WER
speech

LibriSpeech Other Test

5.2% WER

Alternatives & Comparisons

Multilingual ASR and translation model optimized for NVIDIA GPUs and integrated with the Open ASR Leaderboard.

Strengths
  • + Competitive WER on many languages
  • + Optimized for NVIDIA hardware
Weaknesses
  • - Not as widely adopted as Whisper
  • - Less community ecosystem than Whisper

Self-supervised speech representation models often fine-tuned for specific languages or domains.

Strengths
  • + Strong performance with domain-specific fine-tuning
  • + Broad research ecosystem
Weaknesses
  • - Typically not end-to-end multilingual out of the box
  • - Fine-tuning and deployment complexity