| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - automatic-speech-recognition |
| tags: |
| - benchmark |
| - macos |
| - apple-speechanalyzer |
| - whisper-cpp |
| - word-error-rate |
| size_categories: |
| - n<1K |
| pretty_name: Apple SpeechAnalyzer vs whisper.cpp on Mac |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/clip-results.csv |
| --- |
| |
| # Apple SpeechAnalyzer vs whisper.cpp on Mac |
|
|
| Four complete speech-recognition benchmark runs over the same deterministic |
| 40-speaker LibriSpeech test-clean snapshot: |
|
|
| | Engine | Model path | WER | CER | Repeated median post-speech latency | Repeated p95 | |
| | --- | --- | ---: | ---: | ---: | ---: | |
| | Apple SpeechAnalyzer | `progressiveTranscription` on macOS 26.5 | 1.98% | 1.02% | 125–132 ms | 194–201 ms | |
| | whisper.cpp server | 1.8.4 · `ggml-small.en` | 4.28% | 1.79% | 122–125 ms | 152–161 ms | |
|
|
| Every run completed 40/40 clips with no failures. Accuracy matched exactly |
| across the two repeated runs for each engine. |
|
|
| Canonical research page, readable methodology, visual results, and correction |
| path: |
|
|
| **https://iravoice.com/research/apple-speechanalyzer-vs-whisper-cpp-mac** |
|
|
| Public artifact repository, verifier, and disclosed production revision: |
|
|
| **https://github.com/mvplab-ai/mac-asr-benchmark** |
|
|
| Versioned GitHub release with downloadable artifacts: |
|
|
| **https://github.com/mvplab-ai/mac-asr-benchmark/releases/tag/v1.0.0** |
|
|
| ## What is included |
|
|
| - `data/clip-results.csv`: 160 clip-run rows with engine and model labels, |
| source commit, manifest digest, hardware, OS, clip and speaker identifiers, |
| timing, WER, CER, word edit counts, references, and hypotheses. |
| - `raw/full-results.json`: all four native run records, including complete |
| engine, environment, protocol, summary, and clip-level output. |
| - `manifest.json`: the deterministic 40-clip selection and reference text. |
| - `CITATION.cff` and `benchmark.bib`: citation metadata. |
| - `CHECKSUMS.sha256`: hashes for every mirrored publication artifact. |
| - `source-provenance.json`: the tested private-application revision and |
| visibility disclosure. |
| - `verify.mjs`: a dependency-free verifier that recomputes aggregate WER and |
| CER from the clip-level edit counts and checks repeated-run invariants. |
|
|
| The original LibriSpeech audio is not mirrored here. Obtain the official |
| test-clean archive from [OpenSLR SLR12](https://www.openslr.org/12). The public |
| GitHub repository includes the deterministic manifest, raw outputs, checksums, |
| and an executable scoring-consistency verifier. The tested application revision |
| is preserved in every run record. IraVoice's production application source |
| remains proprietary, so the public repository does not claim that the engine |
| runner itself is open source. |
|
|
| ## Protocol |
|
|
| - Corpus: one deterministic clip from each of the 40 LibriSpeech test-clean |
| speakers; 20 female and 20 male speakers; 8–20 reference words and 4–10 |
| seconds per clip. |
| - Hardware: Apple M5 Max, `Mac17,7`, 128 GiB memory. |
| - OS: macOS 26.5 build 25F71. |
| - Audio: mono 16 kHz PCM fed in 100 ms chunks at real-time pace. |
| - Warmup: the first clip is run once and excluded, then all 40 clips are |
| measured. |
| - Engines: the production IraVoice Apple SpeechAnalyzer streaming adapter and |
| whisper.cpp batch-server adapter. |
| - Accuracy: micro-averaged WER and CER after disclosed normalization. Digits are |
| not rewritten as words. No vocabulary injection, formatting model, cleanup, |
| or hand correction is applied. |
| - Latency: measured after the final audio chunk until the complete raw |
| transcript returns. Startup, formatting, and text insertion are excluded. |
| - Repetition: two complete final-commit runs per engine. |
|
|
| ## Limitations |
|
|
| This is clean read English audiobook speech, not spontaneous desktop dictation. |
| The 40 clips are a deterministic speaker-balanced snapshot, not all 2,620 |
| test-clean clips. The results cover one Mac, OS build, locale, and Whisper |
| model. They do not establish noisy-speech, accented-speech, multilingual, |
| accessibility, disability, or population-wide performance. |
|
|
| Apple SpeechAnalyzer is tested as a streaming engine and this whisper.cpp path |
| as a batch server. Only the disclosed final transcript and post-speech interval |
| are compared. Repeated latency ranges were close; this dataset does not support |
| a decisive universal latency winner. |
|
|
| IraVoice publishes this study and currently ships the Apple recognition path. |
| Inspect the code, manifest, raw outputs, and limitations before reusing the |
| headline result. The study did not test Superwhisper, Wispr Flow, Raycast, or |
| any other commercial dictation product. |
|
|
| ## Citation |
|
|
| > IraVoice for Mac. Apple SpeechAnalyzer vs whisper.cpp on Mac: Reproducible |
| > ASR Benchmark. Version 1.0.0, July 26, 2026. |
| > https://iravoice.com/research/apple-speechanalyzer-vs-whisper-cpp-mac |
|
|
| The published result data and metadata are licensed CC BY 4.0. LibriSpeech |
| source material remains subject to the corpus license and attribution described |
| by OpenSLR. |
|
|
