ZacharyZhang-NY
836a4908e1
Generated by .venv/bin/python -m bench.asr_check after 709104e, so the stamp
names the code under measurement instead of its parent. Two passes agreed:
WER 0.242 both times, transcripts byte-identical.
22 lines
1.7 KiB
Markdown
22 lines
1.7 KiB
Markdown
# ASR: what the speech reader hears (commit 709104e80)
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- generated 2026-10-08T02:24:47+00:00
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- model: Systran/faster-whisper-tiny (cpu int8, 4 threads), model.bin sha256 dcb76c6586fc06cb…
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- decode: word_timestamps=True, beam_size=1, temperature=(0.0,), condition_on_previous_text=False, vad_filter=False
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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': [9.65, 2.54, 0.88]}
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| video | audio s | transcribe s | ×realtime | reference words | words | WER | mean word confidence |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| 0IdYJGBmguM | 88.93 | 1.39 | 63.9 | 201 | 219 | 0.229 | 0.861 |
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| 1q-5IIyZL20 | 109.18 | 1.33 | 82.4 | 296 | 313 | 0.253 | 0.821 |
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| 1tidNDIpKSY | 60.07 | 0.54 | 112.2 | 36 | 31 | 0.222 | 0.852 |
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Over 3 videos and 258.18 seconds of audio: word error rate **0.242** over 533 reference words (563 words heard), transcribed in 3.26 seconds.
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Repeating the pass on the same machine gave WER 0.242 then 0.242; the transcripts were byte-identical. Decoding is greedy with no retry ladder, so a rerun is the same answer, not a new draw.
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Ingested through the product path, the store holds 80 audio atoms from 3 sources carrying 563 word timestamps (0 out of order, 0 before their own segment) out of 133 atoms in total.
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The captions are not a transcript, so this WER is not a bound in either direction: a caption that drops words also hides words the model dropped. The third row has only 36 reference words, where one word moves its rate by several points — read the denominators, not the decimals.
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