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README.md

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+---
+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
+license: mit
+base_model:
+- openai/whisper-large-v2
+pipeline_tag: automatic-speech-recognition
+---
+
+# Den4ikAI/whisper-large-v2-no-digits-norm-punct
+
+This is a special version of the `openai/whisper-large-v2` model whose vocabulary has had all tokens corresponding to digits removed, as well as tokens with extraneous punctuation.
+
+The primary goal of this modification is to **force the model to generate numbers as words rather than digits**. This is extremely useful for text normalization tasks, for example when preparing data for text-to-speech (TTS) systems, where numbers need to be fully spelled out.
+
+## Comparison with the Original Model
+
+Here’s a clear example demonstrating the difference in behavior between the models when transcribing the same audio clip containing the phrase “Билет стоил двадцать тысяч рублей” (“The ticket cost twenty thousand rubles”).
+
+| Model                                                       | Transcription Output                                                                                   |
+| ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ |
+| `openai/whisper-large-v2` (Original)                        | `<\|startoftranscript\|><\|ru\|><\|transcribe\|><\|notimestamps\|> Билет стоил **20000** рублей.<\|endoftext\|>` |
+| `Den4ikAI/whisper-large-v2-no-digits-norm-punct` (This model) | `<\|startoftranscript\|><\|ru\|><\|transcribe\|><\|notimestamps\|> Билет стоил **двадцать тысяч** рублей.<\|endoftext\|>` |
+
+As you can see, this modified model correctly normalized the number into words, whereas the original version left it as digits.
+
+## How to Use
+
+You can use this model just like any other Whisper model in the `transformers` library.
+
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import torchaudio
+import torch
+
+# Specify the device (GPU if available)
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# Load the audio file
+wav, sr = torchaudio.load("numbers5.mp3")
+# Convert to mono and resample to 16 kHz
+if wav.shape[0] > 1:
+    wav = torch.mean(wav, dim=0, keepdim=True)
+resampler = torchaudio.transforms.Resample(sr, 16000)
+wav = resampler(wav)
+audio_input = wav.squeeze(0)
+
+# Load the processor and model
+model_id = "Den4ikAI/whisper-large-v2-no-digits-norm-punct"
+processor = WhisperProcessor.from_pretrained(model_id)
+model = WhisperForConditionalGeneration.from_pretrained(model_id).to(device)
+
+# Prepare inputs and extract features
+input_features = processor(
+    audio_input,
+    sampling_rate=16000,
+    return_tensors="pt"
+).input_features.to(device)
+
+# Generate token IDs (for Russian specify language="russian")
+predicted_ids = model.generate(input_features, language="russian", task="transcribe")
+
+# Decode tokens back to text
+transcription = processor.batch_decode(
+    predicted_ids,
+    skip_special_tokens=False
+)
+
+print(transcription)
+
+# Example output for an audio clip with numbers:
+# ['<|startoftranscript|><|ru|><|transcribe|><|notimestamps|> Билет стоил двадцать тысяч рублей.<|endoftext|>']

config.json

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+{
+  "alignment_heads": [
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+      12
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+    [
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+    [
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+    [
+      26,
+      1
+    ],
+    [
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+    ],
+    [
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+    ]
+  ],
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+}

model.bin

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