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whisper-large-v3-turbo

Whisper Large-v3-Turbo is a distilled version of Whisper Large-v3, fine-tuned to achieve most of the large model's transcription accuracy at substantially lower inference cost. It supports over 99 languages and maintains the original model's multilingual ASR quality while requiring fewer decoder layers. MIT licensed and directly compatible with HuggingFace's whisper inference pipeline.

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Fields below are copied from the tags and counters on the HuggingFace repository openai/whisper-large-v3-turbo at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
openai
Pipeline tag
automatic-speech-recognition
Library
Transformers
Weight formats
safetensors
License tag
mit — read the license file in the repo before relying on it
Lineage
Language tags
English (en), Chinese (zh), German (de), Spanish (es), Russian (ru), Korean (ko), French (fr), Japanese (ja), Portuguese (pt), Turkish (tr), Polish (pl), Catalan (ca), Dutch (nl), Arabic (ar), Swedish (sv), Italian (it), Indonesian (id), Hindi (hi), Finnish (fi), Vietnamese (vi), Hebrew (he), Ukrainian (uk), Greek (el), Malay (ms), Czech (cs), Romanian (ro), Danish (da), Hungarian (hu), Tamil (ta), Norwegian (no), Thai (th), Urdu (ur), Croatian (hr), Bulgarian (bg), Lithuanian (lt), Latin (la), Māori (mi), Malayalam (ml), Welsh (cy), Slovak (sk), Telugu (te), Persian (fa), Latvian (lv), Bangla (bn), Serbian (sr), Azerbaijani (az), Slovenian (sl), Kannada (kn), Estonian (et), Macedonian (mk), Breton (br), Basque (eu), Icelandic (is), Armenian (hy), Nepali (ne), Mongolian (mn), Bosnian (bs), Kazakh (kk), Albanian (sq), Swahili (sw), Galician (gl), Marathi (mr), Punjabi (pa), Sinhala (si), Khmer (km), Shona (sn), Yoruba (yo), Somali (so), Afrikaans (af), Occitan (oc), Georgian (ka), Belarusian (be), Tajik (tg), Sindhi (sd), Gujarati (gu), Amharic (am), Yiddish (yi), Lao (lo), Uzbek (uz), Faroese (fo), Haitian Creole (ht), Pashto (ps), Turkmen (tk), Norwegian Nynorsk (nn), Maltese (mt), Sanskrit (sa), Luxembourgish (lb), Burmese (my), Tibetan (bo), Filipino (tl), Malagasy (mg), Assamese (as), Tatar (tt), Hawaiian (haw), Lingala (ln), Hausa (ha), Bashkir (ba), Javanese (jw), Sundanese (su)
Papers cited
arXiv:2212.04356
Downloads (HF counter at last fetch)
6,836,894
Likes (HF counter at last fetch)
3,288
Model card
https://huggingface.co/openai/whisper-large-v3-turbo

Use cases

  • Production multilingual transcription requiring large-model quality at reduced cost
  • Real-time or near-real-time ASR for 100+ language content
  • Meeting transcription and subtitle generation
  • Podcast and audio content processing at scale
  • Integration with pyannote speaker diarization for speaker-attributed transcription

Pros

  • MIT license for unrestricted commercial use
  • 99-language support at near Whisper-large-v3 accuracy with lower compute
  • Standard HuggingFace transformers compatibility
  • ONNX and endpoint deployment support for production infrastructure

Cons

  • Turbo distillation introduces slight accuracy tradeoffs vs. the full large-v3 on some languages
  • Still requires GPU for real-time throughput on long audio files
  • Word-level timestamps require additional post-processing
  • Accented speech and non-standard audio quality can degrade accuracy significantly
  • No speaker diarization built in — requires combining with pyannote or similar

Tags

transformerssafetensorswhisperautomatic-speech-recognitionaudioenzhdeesrukofrjapttrplcanlarsv