Datasets:
erratum: G2 is consistency evidence, not a front-end check (README.md)
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README.md
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## Independent evaluation and replication
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- **Second implementation (`G2`).** `independent_eval.py`
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resampling, clip identity, the audio front end and the ranking,
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pinned weights. At batch 1 it agrees with the canonical results on
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outcomes in all eight
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- **Metric parity.** `gate2.py` recomputes every shipped embedding dump two ways and finds
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0.0e+00 difference across the four canonical cells, two of which exercise multi-positive
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average precision.
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## Independent evaluation and replication
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- **Second implementation (`G2`), with an important limit.** `independent_eval.py`
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reimplements decoding, resampling, clip identity, the audio front end and the ranking,
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then runs the same pinned weights. At batch 1 it agrees with the canonical results on
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100% of R@1 hit/miss outcomes in all eight comparisons. That agreement is evidence of
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internal consistency, not an independent check of the front end: the second
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implementation sets `is_longer=True` the same way the processor does (see the
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correction at the top), so it reproduced the same branch routing and could not have
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detected the defect. It also shares the checkpoint's tokenizer and filterbank
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coefficients. Read it as "the scoring pipeline is reproducible end to end", not as
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"the preprocessing is right".
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- **Metric parity.** `gate2.py` recomputes every shipped embedding dump two ways and finds
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0.0e+00 difference across the four canonical cells, two of which exercise multi-positive
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average precision.
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