README.md

metadata

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. 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.