Create README.md
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README.md
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| 1 |
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: automatic-speech-recognition
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tags:
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- asr
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- Pytorch
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- pruned
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- audio
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- automatic-speech-recognition
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language:
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- en
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- zh
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- de
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- es
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- ru
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| 17 |
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- ko
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- fr
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| 19 |
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- ja
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| 20 |
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- pt
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- tr
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- pl
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- ca
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- nl
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- ar
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- sv
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- it
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- id
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- hi
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- fi
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- vi
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- he
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- uk
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- el
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- ms
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- cs
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- ro
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- da
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- hu
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- ta
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- 'no'
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- th
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- ur
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- hr
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- bg
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- lt
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- la
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- mi
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- ml
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- cy
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- sk
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- te
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| 53 |
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- fa
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| 54 |
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- lv
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| 55 |
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- bn
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| 56 |
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- sr
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| 57 |
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- az
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| 58 |
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- sl
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| 59 |
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- kn
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| 60 |
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- et
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| 61 |
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- mk
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| 62 |
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- br
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| 63 |
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- eu
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| 64 |
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- is
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| 65 |
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- hy
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| 66 |
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- ne
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| 67 |
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- mn
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| 68 |
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- bs
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| 69 |
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- kk
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| 70 |
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- sq
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| 71 |
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- sw
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| 72 |
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- gl
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| 73 |
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- mr
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| 74 |
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- pa
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| 75 |
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- si
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| 76 |
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- km
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| 77 |
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- sn
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| 78 |
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- yo
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| 79 |
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- so
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| 80 |
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- af
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| 81 |
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- oc
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| 82 |
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- ka
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| 83 |
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- be
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- tg
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- sd
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- gu
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| 87 |
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- am
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| 88 |
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- yi
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| 89 |
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- lo
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| 90 |
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- uz
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| 91 |
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- fo
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| 92 |
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- ht
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| 93 |
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- ps
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| 94 |
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- tk
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| 95 |
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- nn
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| 96 |
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- mt
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| 97 |
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- sa
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| 98 |
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- lb
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| 99 |
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- my
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| 100 |
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- bo
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| 101 |
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- tl
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| 102 |
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- mg
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| 103 |
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- as
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| 104 |
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- tt
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| 105 |
+
- haw
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| 106 |
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- ln
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| 107 |
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- ha
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| 108 |
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- ba
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| 109 |
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- jw
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| 110 |
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- su
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| 111 |
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base_model:
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| 112 |
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- openai/whisper-large-v3-turbo
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| 113 |
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---
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| 114 |
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# Whisper-large-v3-turbo-no-numbers
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## Model info
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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).
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NO fine-tuning was used.
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| 120 |
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| 121 |
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Phrases with spoken numbers will be transcribed with numbers as words.
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| 122 |
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Example: Instead of "25" this model will transcribe phrase as "twenty five".
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## Usage
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| 126 |
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Model can be used as an original whisper:
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```python
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| 129 |
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>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
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| 130 |
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>>> import torchaudio
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| 131 |
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| 132 |
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>>> # load audio
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| 133 |
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>>> wav, sr = torchaudio.load("audio.wav")
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| 134 |
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| 135 |
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>>> # load model and processor
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| 136 |
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>>> processor = WhisperProcessor.from_pretrained("waveletdeboshir/whisper-large-v3-turbo-no-numbers")
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>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-large-v3-turbo-no-numbers")
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>>> input_features = processor(wav[0], sampling_rate=sr, return_tensors="pt").input_features
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| 140 |
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| 141 |
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>>> # generate token ids
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| 142 |
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>>> predicted_ids = model.generate(input_features)
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| 143 |
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>>> # decode token ids to text
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| 144 |
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>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
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| 145 |
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['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> I'm twenty seven years old. <|endoftext|>']
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| 146 |
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| 147 |
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```
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| 148 |
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The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`.
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