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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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base_model: waveletdeboshir/whisper-base-ru-pruned |
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tags: |
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- asr |
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- Pytorch |
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- pruned |
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- finetune |
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- audio |
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- automatic-speech-recognition |
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model-index: |
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- name: Whisper Base Pruned and Finetuned 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: 26.52 |
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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: 21.35 |
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datasets: |
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- mozilla-foundation/common_voice_15_0 |
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--- |
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# Whisper-base-ru-pruned-ft |
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## Model info |
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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. |
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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. |
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## Metrics |
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| metric | dataset | waveletdeboshir/whisper-base-ru-pruned | waveletdeboshir/whisper-base-ru-pruned-ft | |
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| :------ | :------ | :------ | :------ | |
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| WER (without punctuation) | common_voice_15_0_test | 0.3352 | **0.2135** | |
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| WER | common_voice_15_0_test | 0.4050 | **0.2652** | |
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## Limitations |
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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. |
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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 30% less then original whisper-base: |
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| | openai/whisper-base | waveletdeboshir/whisper-base-ru-pruned-ft | |
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| :------ | :------ | :------ | |
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| n of parameters | 74 M | 48 M | |
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| n of parameters (with proj_out layer) | 99 M | 50 M | |
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| model file size | 290 Mb | 201 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-base-ru-pruned-ft") |
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>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-base-ru-pruned-ft") |
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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-small-ru-pruned](https://huggingface.co/waveletdeboshir/whisper-small-ru-pruned) |
