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--- |
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pretty_name: "WhisperKit ASR Evaluation Results" |
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tags: |
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- whisper |
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- whisperkit |
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- coreml |
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- asr |
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- quantized |
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--- |
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# WhisperKit Evaluation Results |
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## Dataset: `librispeech` |
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### WhisperKit + `openai_whisper-large-v3` (+optimized variants) |
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| | WER | QoI (%) | File Size (MB) | |
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|:----------------------------------------------------------------------------------------------------------------------------------------------|------:|----------:|-----------------:| |
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| [openai_whisper-large-v3](https://huggingface.co/argmaxinc/whisperkit-coreml-rc1/tree/main/openai_whisper-large-v3) | 2.44 | 100 | 3100 | |
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| [openai_whisper-large-v3_turbo](https://huggingface.co/argmaxinc/whisperkit-coreml-rc1/tree/main/openai_whisper-large-v3_turbo) | 2.41 | 99.8 | 3100 | |
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| [openai_whisper-large-v3_turbo_1307MB](https://huggingface.co/argmaxinc/whisperkit-coreml-rc1/tree/main/openai_whisper-large-v3_turbo_1307MB) | 2.6 | 97.7 | 1307 | |
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| [openai_whisper-large-v3_turbo_1049MB](https://huggingface.co/argmaxinc/whisperkit-coreml-rc1/tree/main/openai_whisper-large-v3_turbo_1049MB) | 4.81 | 91 | 1049 | |
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| [openai_whisper-large-v3_1053MB](https://huggingface.co/argmaxinc/whisperkit-coreml-rc1/tree/main/openai_whisper-large-v3_1053MB) | 4.65 | 90.8 | 1053 | |
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### Different Projects + `openai_whisper-large-v3` |
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| | WER | Commit Hash | Model Format | |
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|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|:--------------|:---------------| |
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| [WhisperKit](https://github.com/argmaxinc/whisperkit) | [2.44](https://hf.co/datasets/argmaxinc/whisperkit-evals-rc1/tree/main/WhisperKit/openai_whisper-large-v3/librispeech) | 0f8b4fe | Core ML | |
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| [WhisperCpp](https://github.com/ggerganov/whisper.cpp) | [2.36](https://hf.co/datasets/argmaxinc/whisperkit-evals-rc1/tree/main/whisper.cpp/openai_whisper-large-v3/librispeech) | e72e415 | Core ML + GGUF | |
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| [WhisperMLX](https://github.com/ml-explore/mlx-examples/blob/main/whisper/whisper/transcribe.py) | [2.69](https://hf.co/datasets/argmaxinc/whisperkit-evals-rc1/tree/main/WhisperMLX/openai_whisper-large-v3/librispeech) | 614de66 | MLX (Numpy) | |
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### Quality-of-Inference (QoI) Certification |
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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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Whisper models in production. The current measurements are between reference and optimized |
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WhisperKit models. We are going to extend the scope of this measurement to other Whisper |
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implementations soon so developers can certify the behavior change (if any) caused by |
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alternating use of WhisperKit with (or migration from) these implementations. |
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In all measurements, we care primarily about per-example no-regressions (quantified as `qoi` below) |
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which is a stricter metric compared to dataset average WER. A 100% `qoi` preserves perfect |
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backwards-compatibility on the test distribution and avoids "perceived regressions", the phenomenon |
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where per-example known behavior changes after a code/model update and causes divergence in |
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downstream code or breaks the user experience itself (even if dataset averages might stay flat |
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across updates). Pseudocode for `qoi`: |
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```python |
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qoi = [] |
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for example in dataset: |
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no_regression = wer(optimized_model(example)) <= wer(reference_model(example)) |
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qoi.append(no_regression) |
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qoi = (sum(qoi) / len(qoi)) * 100. |
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``` |
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We define the reference model as the default float16 precision Core ML model that is generated by |
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whisperkittools. This reference model matches the accuracy of the original PyTorch model |
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on the specified test sets. We use `librispeech/test.clean` (5 hours of short English audio clips) |
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as our testing set for Whisper. We are actively expanding our test set coverage to `earnings22` |
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(120 hours of long English audio clips with various accents). We anticipate developers that use Whisper in production to have |
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their own Quality Assurance test sets and whisperkittools offers the tooling necessary to run the |
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same measurements on such custom test sets, please see the [Model Evaluation on Custom Dataset](#evaluate-on-custom-dataset) |
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for details. |
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### Reproducing Results |
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Results in this page are generated by our cluster of Apple Silicon Macs. We use them as self-hosted runners on |
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Github Actions as our CI infrastructure. Due to [security concerns](https://docs.github.com/en/actions/security-guides/security-hardening-for-github-actions#hardening-for-self-hosted-runners), |
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we are unable to open up the cluster to the public. However, any Apple Silicon Mac (even with 8GB RAM) can be used to |
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run identical [evaluation jobs](#evaluation) |
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locally. For reference, our M2 Ultra devices complete a `librispeech` + `openai/whisper-large-v3` |
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evaluation in under 1 hour regardless of the Whisper implementation. Older Apple Silicon Macs should take less than |
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1 day to complete the same evaluation. |
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Glossary: |
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- `_turbo`: Indicates the presence of additional optimizations (not compression) to unlock streaming transcription |
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as described in our [Blog Post](https://www.takeargmax.com/blog/whisperkit). |
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- `_*MB`: Indicates the presence of mixed-bit quantization. Instead of cluttering the filename with details like |
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`_AudioEncoder-5.8bits_TextDecoder-6.1bits`, we choose to summarize the compression spec as the resulting total file size since this is what matters to developers in production. |
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