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README.md
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library_name: transformers
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pipeline_tag: automatic-speech-recognition
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base_model: openai/whisper-base
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---
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# Whisper-base-ru-pruned
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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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Model size is 30% less then original whisper-base:
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| | openai/whisper-base | waveletdeboshir/whisper-base-ru-pruned |
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| :------ | :------ | :------ |
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| n of parameters | 74 M | 48.5 M |
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| n of parameters (with proj_out layer) | 99 M | 51 M |
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| model file size | 290 Mb | 203 Mb |
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library_name: transformers
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pipeline_tag: automatic-speech-recognition
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base_model: openai/whisper-base
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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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---
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# Whisper-base-ru-pruned
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## Model info
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This is a pruned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) 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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Model size is 30% less then original whisper-base:
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| | openai/whisper-base | waveletdeboshir/whisper-base-ru-pruned |
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| :------ | :------ | :------ |
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| n of parameters | 74 M | 48.5 M |
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| n of parameters (with proj_out layer) | 99 M | 51 M |
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| model file size | 290 Mb | 203 Mb |
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