ZacharyZhang-NY
5289cb4b01
An audio file used to produce no memory at all: _ingest_file returned an empty batch for any suffix it did not know, with no log and no reason. It now raises UnsupportedFile when you name the file and records a SkippedFile when it was a directory walk, so a refusal is visible either way. Speech is measured, not promised. Over 210.5 seconds of real Video-MME audio, faster-whisper-tiny on CPU int8 transcribed at 51x realtime and reached word error rate 0.242 against community subtitles nobody here wrote (a caption track drops words a transcript keeps, so that is an upper bound). 66 speech atoms now carry 550 word-level timestamps, 0 words out of order and 0 starting before their own segment. The snapshot is 78.2 MB, under the 500 MB cap, and the report is bench/reports/ASR.md. Two library facts were measured rather than assumed. faster-whisper's own audio loader is broken against PyAV 19 (av.open got an unexpected keyword 'metadata_errors') and it declares av with no version bound, so asr.decode_audio decodes with PyAV directly and hands the model a float32 array instead of pinning an old av with no cp314 wheel. And av.Error does not exist on PyAV 19 - the real base is av.error.FFmpegError - so that is the only thing caught and my own ValueErrors keep their messages. Files over the 500-line rule were split rather than excused: ingestion.py (748) into ingestion_common / ingestion_media / ingestion_repository / ingestion, and cli.py (544) into cli_senses plus cli_common, following the cli_transactive register pattern. MediaIngest declares _source_record abstract, so a half-wired host fails at construction instead of returning None. Verification: 207 tests pass; ruff clean on the 13 touched files; mypy clean on them, and repo-wide mypy fell from 29 errors to 24 (5 fixed, none added); pyright reports 0 errors on the touched files.
17 lines
1.2 KiB
Markdown
17 lines
1.2 KiB
Markdown
# ASR: what the speech reader hears (commit 03ca35763)
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- generated 2026-10-08T01:05:45+00:00
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- model: Systran/faster-whisper-tiny (cpu int8), model.bin sha256 dcb76c6586fc06cb…
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- reference: Video-MME community subtitles (third-party captions, not verbatim transcripts)
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- machine: {'system': 'Linux', 'release': '6.18.40.1-microsoft-standard-WSL2', 'cpus': 32, 'threads_per_query': 4, 'load_average_1_5_15': [1.84, 1.85, 6.99]}
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| video | audio s | transcribe s | ×realtime | speech atoms | words | WER | mean word confidence |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| 0IdYJGBmguM | 88.9 | 1.73 | 51.3 | 31 | 218 | 0.179 | 0.879 |
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| 1q-5IIyZL20 | 99.9 | 1.94 | 51.6 | 32 | 300 | 0.28 | 0.83 |
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| 1tidNDIpKSY | 21.7 | 1.34 | 16.1 | 3 | 32 | 0.278 | 0.852 |
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Over 3 videos and 210.5 seconds of audio: word error rate **0.242** against captions nobody here wrote, 66 speech atoms carrying 550 word-level timestamps, 0 words out of order and 0 starting before their own segment.
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A caption track drops words a transcript keeps, so this WER is an upper bound on the model's real error. The point of the run is not the number: before this change an audio file produced no memory at all, and every word here now has an instant attached to it.
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