Open Source
Text Scores Do Not Establish Performance on Lexically Non-Diagnostic Speech Tasks: A Qwen2-Audio Quantization Case Study
arXiv eess.AS0 views
arXiv:2609.26823v2 Announce Type: replace-cross Abstract: Text-output scores alone do not show whether quantization preserves performance on speech tasks whose target labels cannot be recovered from the transcript. We evaluate fixed mixed 4/8-bit Qwen2-Audio-7B-Instruct allocations averaging 6 and 7 bits per parameter on 508 English-to-German FLEURS utterances and on 512 RAVDESS emotion clips from 16 speakers. The BLEU and chrF differences from half precision (FP16) have intervals that include zero for both allocations. On RAVDESS, the same two sentences occur equally often with every emotion label. The absolute accuracy differences from FP16 are -3.71% for 6 bit and -1.17% for 7 bit. The 6-bit speaker interval excludes zero and an exact two-sided sign-flip test gives p=0.0148; the 7-bit interval includes zero. Same-budget controls do not identify either selected allocation as best. This case study shows why translation scores and performance on tasks beyond the transcript need separate evaluation.
Source:https://arxiv.org/abs/2609.26823PreviousVoxReason: Auditing Source-Grounded Speech Plans Before SynthesisNextEDAP: An Efficient Data-Gathering Protocol for Wireless Sensor Networks
Related posts
Anime Expeditions Codes (November 2026)
0FOSS Bytes

Karpathy built HackerTube in 2 hours. I rebuilt it in the open and measured it
0dev.to AI
Show HN: Poinky – Claude Code you talk to, that sees your screen
0Hacker News Show
Show HN: DeskAccess – Open-source remote access for Raspberry Pi
0Hacker News Show
Show HN: The Public Eye – Agentic search for congressional bill legibility
0Hacker News Show
NASA’s Perseverance Mars rover breaks historic record after traveling 28 miles
0Science Daily Tech