Smoke testing v0.10.0:0 against a real anarlog audio.mp3 showed the
output running words together: "I'mrecordingrightnow", "don'tyoutry".
Root cause: _merge_words_with_speakers was doing "".join(cur_words),
assuming Parakeet returns words with leading whitespace (which the
hyprnote local Parakeet does, but the Spark-hosted Parakeet does not).
Rewrote the join with a small helper that:
- Strips each token (handles both leading-space and no-leading-space
word formats)
- Joins with a single space
- Keeps punctuation tight — no space before period/comma/colon/etc.
Verified post-install with the same test audio:
[00:06] Speaker_0: I'm I'm recording right now.
[00:18] Speaker_1: you're you're on your computer and your phone, right?
No other changes — Parakeet container patches and the endpoint shape
stay identical.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a new pipeline for diarized transcription that any client (recap-relay,
ad-hoc curl, future Mac-side tools) can call. Pure data pipeline, no LLM
or UI included — name resolution / analysis happen downstream where prompts
and rendering are configurable.
Architecture:
Spark 2 / parakeet-asr container:
+ /opt/parakeet/app/diarizer.py (new: SortformerDiarizer class)
+ /opt/parakeet/app/main.py (patched: loads diarizer, adds
/v1/audio/diarize endpoint)
Model: nvidia/diar_sortformer_4spk-v1 (~150 MB, ungated, NeMo native)
Spark Control:
+ POST /api/audio/transcribe-with-speakers
Body: multipart file
Returns: {
duration, language, speakers_detected,
segments: [{start_ms, end_ms, speaker, text}, ...],
models: {transcription, diarization}
}
Runs Parakeet ASR + Sortformer in parallel, merges words to speaker
turns by timestamp, groups into speaker-change blocks (breaks also
on >1.5s silence gaps).
+ If Parakeet 500s mid-pipeline, kicks deep-health probe and returns
503/Retry-After: 60 — same wedge-recovery pattern as v0.9.0:2.
Apply Sortformer patches to the running Parakeet container with:
bash image/parakeet_patches/apply.sh <spark2-host> <ssh-user>
Patches are reversible — apply.sh backs up the original main.py inside the
container at main.py.pre-sortformer before overwriting. Restore by copying
that file back and removing diarizer.py, then docker restart.
v0.11 follow-up: dashboard "Speech Models" panel to swap/update model
versions from the UI instead of needing to re-run apply.sh.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Parakeet's recurring CUDA wedge (CUBLAS_STATUS_*_ERROR mid-attention)
fires reliably on Open WebUI's WebM/Opus->MP3 audio. Previously the
proxy relayed the upstream 500 verbatim, Open WebUI showed "Server
connection error" with no signal to retry, and recovery took up to
5 minutes (waiting for the next periodic deep-health probe).
Now the proxy:
1. Detects 500 from /v1/audio/transcriptions
2. Fires deep_health.run_one("parakeet") as a background asyncio task
(which contains the same wedge-detect + rate-limited auto-restart
logic, but runs immediately instead of waiting for the next tick)
3. Returns 503 with a clear detail message and Retry-After: 60
The client (Open WebUI, Home Assistant, etc.) gets a proper retry
signal; the auto-restart triggers inside seconds; the next attempt
~60s later succeeds. Rate-limiting (3 restarts per 30 min) is
inherited from the deep-health module so this can't cause restart
storms.
server.py: pass deep_health into build_audio_router().
audio_proxy.py: new 503-with-restart branch; signature now accepts
deep_health as an optional dependency.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds three new endpoints to spark-control that translate OpenAI's
audio API shapes to the Parakeet (STT) and Magpie (TTS, NVIDIA Riva)
services on the Sparks:
GET /v1/models — STT model + Magpie's 60+ voices
POST /v1/audio/speech — OpenAI body -> Magpie multipart synthesize
(returns audio/wav passthrough)
POST /v1/audio/transcriptions — relay to Parakeet (already compatible)
Verified shapes against the live services:
- Parakeet returns OpenAI-style {"text": "..."} or verbose_json with
segments+words. Already a perfect drop-in for OpenAI clients.
- Magpie returns raw WAV bytes with Content-Type: audio/wav. NOT
base64-wrapped JSON as one might assume. The proxy is literally a
body-translation on the request side; response is passthrough.
Voice language is auto-derived from the voice name (e.g.
Magpie-Multilingual.EN-US.Mia -> language=en-US) so clients don't
need to set it explicitly.
Open WebUI / Home Assistant / Recap Relay can now all point at one
URL — https://<spark-control>.local/v1 — and get LLM, STT, TTS
behind a single identity. No shim service to deploy.
Pure addition: no existing routes touched; the dashboard, /api/*,
download flow, deep-health, hardware probes are all unchanged.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Keysat