---
license: apache-2.0
library_name: transformers
pipeline_tag: automatic-speech-recognition
tags:
- asr
- Pytorch
- pruned
- audio
- automatic-speech-recognition
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
base_model:
- openai/whisper-large-v3-turbo
---

# Whisper-large-v3-turbo-no-numbers

## Model info
This is a version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) 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.

**Example**: Instead of **"25"** this model will transcribe phrase as **"twenty five"**.

## Usage
Model can be used as an original whisper:

```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> import torchaudio

>>> # load audio
>>> wav, sr = torchaudio.load("audio.wav")

>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("waveletdeboshir/whisper-large-v3-turbo-no-numbers")
>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-large-v3-turbo-no-numbers")

>>> input_features = processor(wav[0], sampling_rate=sr, 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`.