ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter8/speech-sft-experiment/validation/exp8-6-20260804-v1/REPORT.md

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+# Experiment 8-6 strict acceptance report
+
+Execution acceptance: **PASS**
+
+This run trained two real LoRA adapters on an RTX PRO 6000. It used 128 Orpheus training utterances plus 16 held-out utterances, and 168 stratified Sesame training utterances plus 24 held-out utterances. Each track completed 60 optimizer updates at effective batch size four. Both adapters are identified by local SHA-256 inventories and public Hugging Face repositories.
+
+## Execution gates
+
+- PASS — `orpheus_128_train_examples`
+- PASS — `orpheus_16_held_out_examples`
+- PASS — `orpheus_60_optimizer_steps`
+- PASS — `orpheus_remote_adapter_sha256_verified`
+- PASS — `orpheus_16_valid_comparison_files`
+- PASS — `sesame_128_train_examples`
+- PASS — `sesame_tag_categories_present`
+- PASS — `sesame_60_optimizer_steps`
+- PASS — `sesame_remote_adapter_sha256_verified`
+- PASS — `sesame_24_valid_comparison_files`
+
+## Hypothesis results
+
+- SUPPORTED — `orpheus_held_out_loss_decreased`
+- NOT SUPPORTED — `orpheus_cross_sentence_timbre_proxy_improved`
+- SUPPORTED — `sesame_held_out_loss_decreased`
+- SUPPORTED — `sesame_adapted_mean_tag_score_is_positive`
+- SUPPORTED — `sesame_tag_sensitivity_improved_over_base`
+
+Execution completion and hypothesis support are intentionally separate. A completed campaign may produce a negative hypothesis result.
+
+## Orpheus result
+
+- Held-out loss: 5.237792 before → 4.865821 after.
+- Mean cross-sentence MFCC-statistic cosine: 0.988627 base → 0.986702 adapted (Δ -0.001924).
+- Eight unseen sentences were generated for each arm with matched seeds. This metric is a timbre-consistency proxy; it is not speaker-verification or a listening-test score.
+
+## Sesame result
+
+- Held-out loss: 128.230759 before → 124.342400 after.
+- Mean matching AudioSet event-score difference (tagged − neutral): +0.000131 base → +0.001097 adapted (Δ +0.000966).
+- Positive matched pairs: 3/6 base; 4/6 adapted.
+- Six prompt pairs (laugh, giggle, sigh) were generated per arm with the same seed within each tagged/neutral pair. AudioSet scores are detector proxies, not proof of natural expression.
+
+## Failure retention and limits
+
+`failure_comparisons.json` retains silent/short outputs, each Orpheus arm's least-consistent sentence pair, and every Sesame pair where adding a tag did not raise the matching AudioSet score. `compatibility_failures.json` retains the disabled-source-dataset failure, current Unsloth CSM pad-token rejection, and Transformers bf16 codec merge failure, together with the exact standard-PEFT/float32 fallback. The Sesame held-out loss split contains laugh, sigh, and neutral examples but no giggle examples because all 32 available giggle-tagged rows were allocated to the substantive training split. The campaign does not include blinded human MOS, speaker-verification enrollment, confidence intervals over multiple training seeds, or deployment-scale data. Therefore it makes no claim of perceptual quality or generalization beyond this bounded run.
+
+## Adapter identity
+
+- Orpheus: https://huggingface.co/bojieli/exp8-6-orpheus-elise-lora/tree/536092e9479fa1717e2b8f9cc1be52728b273e95
+- Sesame: https://huggingface.co/bojieli/exp8-6-sesame-elise-tags-lora/tree/f2e042be0f38d6078976ef7e16cf49b91097f756
+- Exact revisions and every retained artifact hash are in `orpheus_manifest.json`, `sesame_manifest.json`, and `artifact_inventory.json`.