Commit History
11 days ago
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Reduce to the dataset-evaluation path, and stop trimming dataset clips
Two changes, one of which fixes a real measurement error. trim_to_speech ran by default on dataset audio. It exists for live microphone input, which arrives with room noise around the words; a dataset clip is already cut to the utterance, so re-cutting it on this pipeline's own intensity estimate takes speech with it -- and it is a step the published runs never performed, so the WER it produced was not comparable with theirs. Measured on four yodas-granary utterances: corpus WER 49.30% with the trim, 45.98% without, and one utterance went 84.8% -> 60.6%, recovering "confirm the status" and "approve or not" that the trim had been cutting off. It is gone from this repo entirely, along with the --trim flag and the trim= parameter on extract_features and transcribe. And the stroke code is gone: midi_to_hz, pressure_to_db, resample_stroke, smooth, features_from_stroke, pad_with_silence, concatenate and the drawable MIDI/silence constants. Resampling pen-tablet events has no place in a mod…a7f5301 -
Quieten the per-utterance warning, and exit 0 after a good run
Two things that made a 3000-utterance run read as broken. The checkpoint's generation_config carries max_length=448 while every call passes max_new_tokens, so transformers warned once per generate() -- three thousand lines burying the running WER. max_new_tokens wins either way, so drop the overridden field rather than filter the message. And the process exited 134. A streaming dataset keeps a prefetch thread alive that closing the iterator does not retire (measured: still in threading.enumerate() at atexit), and interpreter finalization under it dies with PyGILState_Release and SIGABRT. That is after the scores print and the file flushes, so the run succeeded -- but 134 makes every && chain, Makefile and CI job call it a failure. os._exit skips finalization and returns the status main() chose. Also: warn_if_driver_hidden cannot fix a hidden driver in-process, so stop implying it might. torch resolves and caches CUDA state at import, so dlopening libcuda afterwards leaves device_cou…6ed6eb1 -
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