Automatic Speech Recognition
Welsh
whispercpp
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ datasets:
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+ - techiaith/commonvoice_18_0_cy
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+ language:
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+ - cy
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+ base_model:
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+ - openai/whisper-base
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+ pipeline_tag: automatic-speech-recognition
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+ tags:
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+ - whispercpp
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+ ---
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+
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+
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+ # whisper-base-ft-cv-cy-cpp
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+
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+ This model is a version of the [openai/whisper-base](https://huggingface.co/openai/whisper-base) model, fine-tuned on the
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+ [techiaith/commonvoice_18_0_cy](https://huggingface.co/datasets/techiaith/commonvoice_18_0_cy) dataset, and then
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+ [converted for use in whisper.cpp](https://github.com/ggerganov/whisper.cpp/tree/master/models#fine-tuned-models). Whispercpp is
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+ a C/C++ port of Whisper that provides high performance inference on offline hardware such as desktops, laptops and mobile devices.
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+
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+ The model is a smaller in size to the corresponding cloud hosted model
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+ [techiaith/whisper-large-v3-ft-cv-cy](https://huggingface.co/techiaith/whisper-large-v3-ft-cv-cy).
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+ It achieves the following WER results for transcribing:
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+
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+ - Wer: 42.68
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+ - Cer: 14.14
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+
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+ ## Usage
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+
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+ whispercpp makes it easy to use models in many platforms and applications. See the 'examples' folder
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+ in the whispercpp github repo for more information and example code.
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+
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+ To get quickly started with whispercpp's basic usage however, follow the '[Quick Start](https://github.com/ggerganov/whisper.cpp?tab=readme-ov-file#quick-start)'
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+ but download this model with the following command:
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+
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+
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+ `$ wget https://huggingface.co/techiaith/whisper-base-ft-cv-cy-cpp/resolve/main/ggml-model.bin`