File size: 2,510 Bytes
4462309 51793f7 4462309 51793f7 4462309 51793f7 b3bdc93 4462309 b329385 b3bdc93 261e132 0a192f7 b3bdc93 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
---
license: apache-2.0
language:
- ru
library_name: transformers
pipeline_tag: automatic-speech-recognition
tags:
- asr
- Pytorch
- pruned
- audio
- automatic-speech-recognition
---
# Whisper-base-ru-pruned
## Model info
This is a pruned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) model with only russian tokens left.
Pruning was made without any fine-tuning. Method from [this post](https://medium.com/m/global-identity-2?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) was used.
## Size
Only 10% tokens was left including special whisper tokens, added whisper tokens, 100 most popular tokens from tokenizer and 3000 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 |
| :------ | :------ | :------ |
| n of parameters | 74 M | 48.5 M |
| n of parameters (with proj_out layer) | 99 M | 51 M |
| model file size | 290 Mb | 203 Mb |
| vocab_size | 51865 | 4705 |
## 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")
>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-base-ru-pruned")
>>> 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)
## Metrics
Metrics for this model are on the same level as for openai/whisper-base.
You can fine-tune this model on your data to achive better performance.
## Colab for pruning
TODO |