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--- |
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pretty_name: "WhisperKit ASR Evaluation Results" |
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viewer: false |
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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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### Quality Evaluation |
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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-large-v3](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v3/librispeech) | 2.48 | 95.2 | 3100 | |
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| [WhisperKit/openai_whisper-large-v3_turbo](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v3_turbo/librispeech) | 2.44 | 95.4 | 3100 | |
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| [WhisperKit/openai_whisper-large-v3_turbo_1018MB](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v3_turbo_1018MB/librispeech) | 2.49 | 94.8 | 1018 | |
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| [WhisperKit/openai_whisper-large-v2](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v2/librispeech) | 3.28 | 96.6 | 3100 | |
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| [WhisperKit/openai_whisper-large-v2_1050MB](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v2_1050MB/librispeech) | 3.32 | 95 | 1050 | |
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| [WhisperKit/openai_whisper-large-v2_turbo](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v2_turbo/librispeech) | 3.24 | 96.6 | 3100 | |
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| [WhisperKit/openai_whisper-large-v2_turbo_1022MB](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v2_turbo_1022MB/librispeech) | 3.33 | 94.9 | 1022 | |
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| [WhisperKit/openai_whisper-small.en](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-small.en/librispeech) | 4.31 | 85.9 | 483 | |
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| [WhisperKit/openai_whisper-small](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-small/librispeech) | 3.98 | 82.9 | 483 | |
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| [WhisperKit/openai_whisper-base.en](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-base.en/librispeech) | 4.76 | 75.5 | 145 | |
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| [WhisperKit/openai_whisper-base](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-base/librispeech) | 6.11 | 67.1 | 145 | |
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| [WhisperKit/openai_whisper-tiny.en](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-tiny.en/librispeech) | 6.72 | 64 | 66 | |
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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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Server-side: |
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- `WhisperOpenAIAPI`: [OpenAI's Whisper API](https://platform.openai.com/docs/guides/speech-to-text) ($0.36 per hour of audio as of 02/29/24, 25MB file size limit per request) |
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On-device: |
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- `WhisperKit`: Argmax's implementation [[Eval Harness]](https://github.com/argmaxinc/whisperkittools/blob/main/whisperkit/pipelines.py#L100) [[Repo]](https://github.com/argmaxinc/WhisperKit) |
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- `whisper.cpp`: A C++ implementation form ggerganov [[Eval Harness]](https://github.com/argmaxinc/whisperkittools/blob/main/whisperkit/pipelines.py#L212) [[Repo]](https://github.com/ggerganov/whisper.cpp) |
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- `WhisperMLX`: A Python implementation from Apple MLX [[Eval Harness]](https://github.com/argmaxinc/whisperkittools/blob/main/whisperkit/pipelines.py#L338) [[Repo]](https://github.com/ml-explore/mlx-examples/blob/main/whisper/whisper/transcribe.py) |
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`WhisperOpenAIAPI` sets the reference and we assume that it is using the equivalent of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) |
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in float16 precision along with additional undisclosed optimizations from OpenAI. 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 [Word Error RATE (WER)](https://en.wikipedia.org/wiki/Word_error_rate). A 100% `qoi` preserves perfect 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 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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Note that the ordering of models with respect to `WER` does not necessarily match the ordering with respect to `QoI`. This is because the reference model gets assigned |
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a QoI of 100% by definition. Any per-example regression by other implementations get penalized while per-example improvements are not rewarded. `QoI` (higher is better) matters |
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where the production behavior is established by the reference results and the goal is to not regress when switching to an optimized or compressed model. On the other hand, |
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`WER` (lower is better) matters when there is no established production behavior and one is picking the best quality versus model size trade off point. |
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We anticipate developers that use Whisper (or similar models) in production to have their own Quality Assurance test sets and [whisperkittools](https://github.com/argmaxinc/whisperkittools) offers |
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the tooling necessary to run the same measurements on such custom test sets, please see the [Model Evaluation on Custom Dataset]((https://github.com/argmaxinc/whisperkittools)) for details. |
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### Datasets |
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- [librispeech](https://huggingface.co/datasets/argmaxinc/librispeech): ~5 hours of short English audio clips, tests short-form transcription quality |
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- [earnings22](https://huggingface.co/datasets/argmaxinc/earnings22): ~120 hours of English audio clips from earnings calls with various accents, tests long-form transcription quality |
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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) 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 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 model compression. Instead of cluttering the filename with details like |
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`_AudioEncoder-5.8bits_TextDecoder-6.1bits_QLoRA-rank=16`, we choose to summarize the compression spec as the |
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resulting total file size since this is what matters to developers in production. |
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