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  ## Dataset: `librispeech`
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- ### Quality Evaluation
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-
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  | | WER | QoI (%) | File Size (MB) |
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  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------:|----------:|-----------------:|
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  | [WhisperOpenAIAPI/openai_whisper-large-v2](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperOpenAIAPI/openai_whisper-large-v2/librispeech) | 2.85 | 100 | 3100 |
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  | [WhisperKit/openai_whisper-tiny](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-tiny/librispeech) | 8.94 | 52.4 | 66 |
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- We believe that rigorously measuring the "quality of inference" is necessary for developers and
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  enterprises to make informed decisions when opting to use optimized or compressed variants of
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  any machine learning model in production. To contextualize `WhisperKit`, we take the following Whisper
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  implementations and benchmark them using a consistent evaluation harness:
 
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  ## Dataset: `librispeech`
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  | | WER | QoI (%) | File Size (MB) |
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  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------:|----------:|-----------------:|
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  | [WhisperOpenAIAPI/openai_whisper-large-v2](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperOpenAIAPI/openai_whisper-large-v2/librispeech) | 2.85 | 100 | 3100 |
 
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  | [WhisperKit/openai_whisper-tiny](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-tiny/librispeech) | 8.94 | 52.4 | 66 |
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+ We believe that rigorously measuring the quality of inference is necessary for developers and
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  enterprises to make informed decisions when opting to use optimized or compressed variants of
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  any machine learning model in production. To contextualize `WhisperKit`, we take the following Whisper
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  implementations and benchmark them using a consistent evaluation harness: