metadata

license: apache-2.0
language:
  - en
  - zh
  - de
  - es
  - ru
  - ko
  - fr
  - ja
  - pt
  - tr
  - pl
  - ca
  - nl
  - ar
  - sv
  - it
  - id
  - hi
  - fi
  - vi
  - he
  - uk
  - el
  - ms
  - cs
  - ro
  - da
  - hu
  - ta
  - 'no'
  - th
  - ur
  - hr
  - bg
  - lt
  - la
  - mi
  - ml
  - cy
  - sk
  - te
  - fa
  - lv
  - bn
  - sr
  - az
  - sl
  - kn
  - et
  - mk
  - br
  - eu
  - is
  - hy
  - ne
  - mn
  - bs
  - kk
  - sq
  - sw
  - gl
  - mr
  - pa
  - si
  - km
  - sn
  - yo
  - so
  - af
  - oc
  - ka
  - be
  - tg
  - sd
  - gu
  - am
  - yi
  - lo
  - uz
  - fo
  - ht
  - ps
  - tk
  - nn
  - mt
  - sa
  - lb
  - my
  - bo
  - tl
  - mg
  - as
  - tt
  - haw
  - ln
  - ha
  - ba
  - jw
  - su
tags:
  - audio
  - automatic-speech-recognition
base_model: openai/whisper-small
pipeline_tag: automatic-speech-recognition

Whisper-small OpenVINO IR

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning.

Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al from OpenAI. The original code repository can be found here.

Disclaimer: Content for this model card has partly been copied and pasted from this model card.

Model details

Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model.

Model Type	Parameters	n_audio_ctx	n_audio_state	n_audio_head	n_audio_layer	n_text_ctx	n_text_state	n_text_head	n_text_layer	n_mels	n_vocab
whisper_tiny	39 M	1500	384	6	4	224	384	6	4	80	51864
whisper_base	74 M	1500	512	8	6	224	512	8	6	80	51864
whisper_small	244 M	1500	768	12	12	224	768	12	12	80	51864
whisper_medium	769 M	1500	1024	16	24	224	1024	16	16	80	51864
whisper_large_v1	1550 M	1500	1280	20	32	224	1280	20	20	80	51864
whisper_large_v2	1550 M	1500	1280	20	32	224	1280	20	20	80	51864
whisper_large_v3	1550 M	1500	1280	20	32	224	1280	20	20	80	51864