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--- |
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license: apache-2.0 |
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language: |
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- ru |
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library_name: transformers |
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pipeline_tag: automatic-speech-recognition |
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tags: |
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- asr |
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- Pytorch |
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- pruned |
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- audio |
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- automatic-speech-recognition |
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metrics: |
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- cer |
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- wer |
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model-index: |
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- name: Whisper Small Pruned for Russian |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 15.0 (Russian part, test) |
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type: mozilla-foundation/common_voice_15_0 |
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args: ru |
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metrics: |
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- name: WER |
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type: wer |
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value: 24.98 |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 15.0 (Russian part, test) |
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type: mozilla-foundation/common_voice_15_0 |
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args: ru |
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metrics: |
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- name: WER (without punctuation) |
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type: wer |
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value: 17.48 |
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--- |
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# Whisper-small-ru-pruned |
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## Model info |
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This is a pruned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) model with only russian tokens left. |
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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. |
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## Size |
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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. |
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Model size is 15% less then original whisper-small: |
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| | openai/whisper-small | waveletdeboshir/whisper-small-ru-pruned | |
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| :------ | :------ | :------ | |
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| n of parameters | 242 M | 205 M | |
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| n of parameters (with proj_out layer) | 281 M | 208 M | |
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| model file size | 967 Mb | 834 Mb | |
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| vocab_size | 51865 | 4207 | |
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## Usage |
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Model can be used as an original whisper: |
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```python |
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>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration |
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>>> import torchaudio |
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>>> # load audio |
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>>> wav, sr = torchaudio.load("audio.wav") |
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>>> # load model and processor |
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>>> processor = WhisperProcessor.from_pretrained("waveletdeboshir/whisper-small-ru-pruned") |
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>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-small-ru-pruned") |
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>>> input_features = processor(wav[0], sampling_rate=sr, return_tensors="pt").input_features |
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>>> # generate token ids |
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>>> predicted_ids = model.generate(input_features) |
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>>> # decode token ids to text |
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>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) |
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['<|startoftranscript|><|ru|><|transcribe|><|notimestamps|> Начинаем работу.<|endoftext|>'] |
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``` |
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The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`. |
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## Other pruned whisper models |
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* [waveletdeboshir/whisper-tiny-ru-pruned](https://huggingface.co/waveletdeboshir/whisper-tiny-ru-pruned) |
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* [waveletdeboshir/whisper-base-ru-pruned](https://huggingface.co/waveletdeboshir/whisper-base-ru-pruned) |
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## Metrics |
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| metric | dataset | openai/whisper-small | waveletdeboshir/whisper-small-ru-pruned | |
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| :------ | :------ | :------ | :------ | |
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| WER* | golos-test-crowd | 0.3358 | 0.3471 | |
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| CER* | golos-test-crowd | 0.1561 | 0.1444 | |
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| WER* | common_voice_15_0_test | 0.1749 | 0.1748 | |
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| WER | common_voice_15_0_test | 0.2492 | 0.2498 | |
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*Metrics were computed after text normalization |
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You can fine-tune this model on your data to achive better performance. |
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## Colab for vocab pruning |
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Check https://github.com/waveletdeboshir/whisper-lang-remover |
