---
license: apache-2.0
language:
- ru
library_name: transformers
pipeline_tag: automatic-speech-recognition
base_model: waveletdeboshir/whisper-base-ru-pruned
tags:
- asr
- Pytorch
- pruned
- finetune
- audio
- automatic-speech-recognition
model-index:
- name: Whisper Base Pruned and Finetuned for Russian
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 15.0 (Russian part, test)
      type: mozilla-foundation/common_voice_15_0
      args: ru
    metrics:
    - name: WER
      type: wer
      value: 26.52
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 15.0 (Russian part, test)
      type: mozilla-foundation/common_voice_15_0
      args: ru
    metrics:
    - name: WER (without punctuation)
      type: wer
      value: 21.35
datasets:
- mozilla-foundation/common_voice_15_0
---

# Whisper-base-ru-pruned-ft

## Model info
This is a finetuned version of pruned whisper-base model ([waveletdeboshir/whisper-base-ru-pruned](https://huggingface.co/waveletdeboshir/whisper-base-ru-pruned)) for Russian language.

Model was finetuned on russian part of [mozilla-foundation/common_voice_15_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_15_0) with Specaugment, Colored Noise augmentation and Noise from file augmentation.

## Metrics

| metric | dataset | waveletdeboshir/whisper-base-ru-pruned | waveletdeboshir/whisper-base-ru-pruned-ft |
| :------ | :------ | :------ | :------ |
| WER (without punctuation) | common_voice_15_0_test | 0.3352 | **0.2135** |
| WER | common_voice_15_0_test | 0.4050 | **0.2652** |

## Limitations
Because texts in Common Voice don't contain digits and other characters except letters and punctuation signs, model lost an ability to predict numbers and special characters.

## Size
Only 10% tokens was left including special whisper tokens (no language tokens except \<|ru|\> and \<|en|\>, no timestamp tokens), 200 most popular tokens from tokenizer and 4000 most popular Russian tokens computed by tokenization of russian text corpus.

Model size is 30%  less then original whisper-base:
|  | openai/whisper-base | waveletdeboshir/whisper-base-ru-pruned-ft |
| :------ | :------ | :------ |
| n of parameters | 74 M | 48 M |
| n of parameters (with proj_out layer) | 99 M | 50 M |
| model file size | 290 Mb | 193 Mb |
| vocab_size | 51865 | 4207 |

## 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-base-ru-pruned-ft")
>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-base-ru-pruned-ft")

>>> 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|><|ru|><|transcribe|><|notimestamps|> Начинаем работу.<|endoftext|>']

```
The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`.

## Other pruned whisper models
* [waveletdeboshir/whisper-tiny-ru-pruned](https://huggingface.co/waveletdeboshir/whisper-tiny-ru-pruned)
* [waveletdeboshir/whisper-small-ru-pruned](https://huggingface.co/waveletdeboshir/whisper-small-ru-pruned)