For brokkr's dataset foundry (operator-approved 2026-09-26, relayed). - stacks/lfm-vl-seat: llama.cpp server-cuda b11176 (digest-pinned), Q5_K_M + mmproj Q8_0, :8030; gateway alias lfm25-vl-3b (LiteLLM restarted, 36 s). Positive control exact; null control shows it describes a missing image. - stacks/vibevoice-asr-seat: audio.cpp v0.8.2-audio8-perf-hotfix (the GGUF's own runtime, not vibevoice.cpp) on cuda 12.8 runtime + libgomp + libsoxr, sha256-pinned; :8031 direct. LibriSpeech WER 3/69, RTF 0.07-0.14; ~31 s cold first request.
31 lines
1.7 KiB
Markdown
31 lines
1.7 KiB
Markdown
# vibevoice-asr-seat
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**Microsoft VibeVoice-ASR-Streaming-1.5B** (Q4_K) on **nh3-ml1**, served by
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**audio.cpp** (`0xShug0/audio.cpp` v0.8.2-audio8-perf-hotfix, Linux CUDA 12.8
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build) on `:8031`, with direct access only. A utility seat for brokkr's dataset
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foundry (speech → text with inline speaker labels). Operator-approved 2026-09-26,
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relayed by brokkr-smithy-dev.
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- `POST /v1/audio/transcriptions`: multipart `file=@x.wav`, `model=vibevoice-asr-streaming-1.5b`.
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Returns `{"text": " \n Speaker 0:...", "timing": {"rtf": ...}}`.
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- `POST /v1/audio/transcriptions/live?model=…&sample_rate=16000&channels=1&sample_format=s16le`
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takes chunked raw PCM.
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- `GET /v1/models`, `GET /health`.
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**Why audio.cpp and not vibevoice.cpp or llama.cpp.** The
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`christopherthompson81/VibeVoice-ASR-Streaming-1.5B-GGUF` files are audio.cpp
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packages with their sidecars embedded, per the model card, so no separate
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tokenizer is needed. The release binary expects the CUDA 12 runtime libraries,
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so `Dockerfile` puts it on `nvidia/cuda:12.8.1-runtime` with a SHA-256-pinned
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download, plus `libgomp1` and **`libsoxr0`**. Without libsoxr it falls back to
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linear resampling (16k→24k), and on one clip that turned "cutter" into "country".
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**Checks, 2026-09-26.** On the 4 LibriSpeech clips shipped with audio.cpp, WER
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is 3/69 = 4.35%, identical across 3 reps. Two of the three errors are "I'm" vs
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"I am" normalization. RTF is 0.07–0.14. ⚠ **The first request after a start
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takes ~31 s** (CUDA graph warmup); later ones take 0.3–1 s. VRAM ~2.0 GB.
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The model card recommends Q8_0 (3.3 GB; its CUDA WER is 5.80% vs Q4_K's 7.25%,
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on 69 words). To switch, change `path` in `conf/server.json`, download the file,
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and recreate the container.
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