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---
base_model:
- openai/whisper-large-v3
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
library_name: transformers
license: apache-2.0
pipeline_tag: automatic-speech-recognition
tags:
- asr
- Pytorch
- pruned
- audio
- automatic-speech-recognition
---
# Whisper-large-v3-no-numbers
## Model info
This is a version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) model without number tokens (token ids corresponding to numbers are excluded).
NO fine-tuning was used.
Phrases with spoken numbers will be transcribed with numbers as words. It can be useful for TTS data preparation.
**Example**: Instead of **"25"** this model will transcribe phrase as **"twenty five"**.
## Usage
`transformers` version `4.45.2`
Model can be used as an original whisper:
```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> import torchaudio
>>> # load audio
>>> wav, sr = torchaudio.load("audio.wav")
>>> # resample if necessary
>>> wav = torchaudio.functional.resample(wav, sr, 16000)
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("waveletdeboshir/whisper-large-v3-no-numbers")
>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-large-v3-no-numbers")
>>> input_features = processor(wav[0], sampling_rate=16000, return_tensors="pt").input_features
>>> # generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Twenty seven years. <|endoftext|>']
```
The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`. |