metadata
license: apache-2.0
language:
- ru
library_name: transformers
pipeline_tag: automatic-speech-recognition
tags:
- asr
- Pytorch
- pruned
- audio
- automatic-speech-recognition
Whisper-tiny-ru-pruned
Model info
This is a pruned version of openai/whisper-tiny model with only russian tokens left. Pruning was made without any fine-tuning. Method from this post 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 50% less then original whisper-tiny:
| openai/whisper-tiny | waveletdeboshir/whisper-tiny-ru-pruned | |
|---|---|---|
| n of parameters | 38 M | 19.6 M |
| n of parameters (with proj_out layer) | 57.6 M | 21.5 M |
| model file size | 151 Mb | 86 Mb |
| vocab_size | 51865 | 4705 |
Usage
Model can be used as an original whisper:
>>> 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-tiny-ru-pruned")
>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-tiny-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
Metrics
TODO
You can fine-tune this model on your data to achive better performance.
Colab for pruning
TODO