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 (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 50% less then original whisper-tiny:

	openai/whisper-tiny	waveletdeboshir/whisper-tiny-ru-pruned
n of parameters	38 M	19.4 M
n of parameters (with proj_out layer)	57.6 M	21 M
model file size	151 Mb	86 Mb
vocab_size	51865	4207

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

• waveletdeboshir/whisper-base-ru-pruned
• waveletdeboshir/whisper-small-ru-pruned

Metrics

TODO

You can fine-tune this model on your data to achive better performance.

Colab for vocab pruning

Check https://github.com/waveletdeboshir/whisper-lang-remover