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--- |
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pretty_name: "WhisperKit ASR Evaluation Results" |
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viewer: false |
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library_name: whisperkit |
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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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# Whisper Transcription Quality |
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## Dataset: `librispeech` |
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Short-form Audio (<30s/clip) - 5 hours of English audiobook clips |
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| | WER (↓) | QoI (↑) | File Size (MB) | Code Commit | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|----------:|-----------------:|:---------------------------------------------------------------| |
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| large-v2 (WhisperOpenAIAPI) | [2.35](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperOpenAIAPI/openai_whisper-large-v2/librispeech) | 100 | 3100 | N/A | |
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| [large-v2](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v2) | [2.77](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v2/librispeech) | 96.6 | 3100 | [Link](https://github.com/argmaxinc/WhisperKit/commit/2846fd9) | |
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| [large-v2_949MB](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v2_949MB) | [2.4](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v2_949MB/librispeech) | 94.6 | 949 | [Link](https://github.com/argmaxinc/WhisperKit/commit/eca4a2e) | |
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| [large-v2_turbo](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v2_turbo) | [2.76](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v2_turbo/librispeech) | 96.6 | 3100 | [Link](https://github.com/argmaxinc/WhisperKit/commit/2846fd9) | |
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| [large-v2_turbo_955MB](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v2_turbo_955MB) | [2.41](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v2_turbo_955MB/librispeech) | 94.6 | 955 | [Link](https://github.com/argmaxinc/WhisperKit/commit/cf75348) | |
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| [large-v3](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v3) | [2.04](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v3/librispeech) | 95.2 | 3100 | [Link](https://github.com/argmaxinc/WhisperKit/commit/2846fd9) | |
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| [large-v3_947MB](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v3_947MB) | [2.46](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v3_947MB/librispeech) | 93.9 | 947 | [Link](https://github.com/argmaxinc/WhisperKit/commit/eca4a2e) | |
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| [large-v3_turbo](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v3_turbo) | [2.03](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v3_turbo/librispeech) | 95.4 | 3100 | [Link](https://github.com/argmaxinc/WhisperKit/commit/2846fd9) | |
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| [large-v3_turbo_954MB](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v3_turbo_954MB) | [2.47](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v3_turbo_954MB/librispeech) | 93.9 | 954 | [Link](https://github.com/argmaxinc/WhisperKit/commit/cf75348) | |
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| [distil-whisper_distil-large-v3](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/distil-whisper_distil-large-v3) | [2.47](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/distil-whisper_distil-large-v3/librispeech) | 89.7 | 1510 | [Link](https://github.com/argmaxinc/WhisperKit/commit/cf75348) | |
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| [distil-whisper_distil-large-v3_594MB](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/distil-whisper_distil-large-v3_594MB) | [2.96](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/distil-whisper_distil-large-v3_594MB/librispeech) | 85.4 | 594 | [Link](https://github.com/argmaxinc/WhisperKit/commit/508240f) | |
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| [distil-whisper_distil-large-v3_turbo](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/distil-whisper_distil-large-v3_turbo) | [2.47](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/distil-whisper_distil-large-v3_turbo/librispeech) | 89.7 | 1510 | [Link](https://github.com/argmaxinc/WhisperKit/commit/508240f) | |
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| [distil-whisper_distil-large-v3_turbo_600MB](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/distil-whisper_distil-large-v3_turbo_600MB) | [2.78](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/distil-whisper_distil-large-v3_turbo_600MB/librispeech) | 86.2 | 600 | [Link](https://github.com/argmaxinc/WhisperKit/commit/ae1cf96) | |
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| [small.en](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-small.en) | [3.12](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-small.en/librispeech) | 85.8 | 483 | [Link](https://github.com/argmaxinc/WhisperKit/commit/228630c) | |
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| [small](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-small) | [3.45](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-small/librispeech) | 83 | 483 | [Link](https://github.com/argmaxinc/WhisperKit/commit/228630c) | |
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| [base.en](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-base.en) | [3.98](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-base.en/librispeech) | 75.3 | 145 | [Link](https://github.com/argmaxinc/WhisperKit/commit/228630c) | |
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| [base](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-base) | [4.97](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-base/librispeech) | 67.2 | 145 | [Link](https://github.com/argmaxinc/WhisperKit/commit/228630c) | |
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| [tiny.en](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-tiny.en) | [5.61](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-tiny.en/librispeech) | 63.9 | 66 | [Link](https://github.com/argmaxinc/WhisperKit/commit/228630c) | |
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| [tiny](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-tiny) | [7.47](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-tiny/librispeech) | 52.5 | 66 | [Link](https://github.com/argmaxinc/WhisperKit/commit/228630c) | |
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## Dataset: `earnings22` |
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Long-Form Audio (>1hr/clip) - 120 hours of earnings call recordings in English with various accents |
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| | WER (↓) | QoI (↑) | File Size (MB) | Code Commit | |
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|:----------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|----------:|-----------------:|:---------------------------------------------------------------| |
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| large-v2 (WhisperOpenAIAPI) | [16.27](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperOpenAIAPI/openai_whisper-large-v2/earnings22) | 100 | 3100 | N/A | |
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| [large-v3](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v3) | [15.17](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-large-v3/earnings22) | 58.5 | 3100 | [Link](https://github.com/argmaxinc/WhisperKit/commit/2846fd9) | |
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| [base.en](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-base.en) | [23.49](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-base.en/earnings22) | 6.5 | 145 | [Link](https://github.com/argmaxinc/WhisperKit/commit/dda6571) | |
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| [tiny.en](https://hf.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-tiny.en) | [28.64](https://hf.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/openai_whisper-tiny.en/earnings22) | 5.7 | 66 | [Link](https://github.com/argmaxinc/WhisperKit/commit/dda6571) | |
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### Explanation |
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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) |
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($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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(All on-device implementations are available for free under MIT license as of 03/19/2024) |
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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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### Why are there so many Whisper versions? |
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WhisperKit is an SDK for building speech-to-text features in apps across a wide range of Apple devices. We are working towards abstracting away the model versioning from the developer so WhisperKit |
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"just works" by deploying the highest-quality model version that a particular device can execute. In the interim, we leave the choice to the developer by providing quality and size trade-offs. |
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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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Benchmark results on this page were automatically generated by [whisperkittools](https://github.com/argmaxinc/whisperkittools) using our cluster of Apple Silicon Macs as self-hosted runners on |
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Github Actions. We periodically recompute these benchmarks as part of our CI pipeline. 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. Oldest 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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