Move the ~20 optional cluster knobs out of the StartOS "Configure Sparks"
action (now just the 4 required fields) and into a dashboard ⚙ Settings gear,
backed by a /data/app_settings.json overlay keyed by env-var names. One shared
mutable Settings instance + Settings.reload() applies edits live without a
restart; existing installs' values migrate automatically on first boot.
Also: support-service ports (parakeet/kokoro/embed/qdrant + vllm) are now
configurable, and GET /api/swap/lock no longer 404s (it was shadowed by the
/api/swap/{job_id} catch-all). WebhookNotifier is re-pointed on save so its
url/secret reload live too.
The dashboard menu is now the set of models actually downloaded on the
Sparks, not a hard-coded catalog. models.yaml + overrides are reframed as
launch recipes matched to an on-disk model by repo; an on-disk model with
no recipe is flagged needs_setup and its launch settings are inferred from
its config.json for a one-time operator confirmation (discovery.py).
- delete now removes weights AND the menu card (delete_from_disk sweeps all
hosts; the delete endpoint resolves keys via the live menu)
- new GET /api/models/suggest; /api/models returns the menu + a recipes list
(download autocomplete); GET /api/models/disk-status removed
- dropped the two legacy Qwen recipes (235B FP8, 2.5 72B)
- tests: +test_discovery.py (cache parsing, infer_recipe, build_menu merge)
Make the cluster topology configurable so an adopter wired differently
(vLLM on both Sparks, port 8000, different container name, no Parakeet)
can monitor without forking. Covers the OpenClaw report P4/P5/#6.
- VLLM_CONTAINER override (default vllm_node), validated at the boundary
and quote_arg-quoted into the swap log-tail + pre-flight validator exec.
- DISABLED_SERVICES list: hidden services show no tile and are skipped by
status/deep-health/connectivity probes (kills the Parakeet-on-8000
collision).
- kind: vllm custom service monitors a second Spark's vLLM via the shared
probe_vllm_endpoint; /api/endpoints gains a disabled flag.
Swap mechanism intentionally not generalized to raw docker run (that's
coordination, roadmap item 4).
Add models that live as a directory on a Spark (e.g. LoRA-merged fine-tunes),
not just Hugging Face repos.
- ModelDef gains local_path; a model must set exactly one of repo / local_path.
The validator also enforces the local-path whitelist and that any
--chat-template lives inside local_path (only that dir is mounted).
- build_launch_command bind-mounts the dir into the vLLM container at the SAME
host==container path via the launch script's VLLM_SPARK_EXTRA_DOCKER_ARGS hook,
then `vllm serve <dir>`. No launch-cluster.sh change (verified the upstream
expands that var unquoted; contract noted in runbook.md).
- shellsafe.validate_local_path: absolute path, charset whitelist, no '.'/'..'.
- POST /api/models validates the full entry via ModelDef before persisting, so a
bad entry can't be written and then break catalog load; _merge_overrides skips
an invalid override entry instead of failing the whole catalog.
- disk.py size-probes a local path with du; disk-delete refused for local models.
- UI: "+ Add local model" dialog, `local` badge, path shown instead of an HF
link, delete button hidden for local models.
- Tests: local launch + injection round-trip, chat-template location, traversal,
exactly-one-source, _merge_overrides skip-invalid (94 pass). Reviewer-agent
pass; findings addressed.
- Configure Sparks gains a vLLM port field (blank => 8888, our launch-cluster.sh
default); VLLM_PORT plumbed configureSparks -> sparkConfig.yaml -> main.ts env
-> config.py. So an adopter whose vLLM listens elsewhere (e.g. 8000) can fix
the "vLLM unreachable" health check without rebuilding the package.
- Harden numeric env parsing (config._env_int): a blank or malformed port now
falls back to its default instead of crashing daemon startup (closes a P3
tech-debt item; the Configure panel passes unset optional fields as "").
- Add scripts/gitea-release.sh + `make release` to publish the built s9pk to
Gitea Releases, so the OpenClaw adopter pulls updates with a read-only token
instead of being hand-sent the package.
- Capture the OpenClaw/Johnny-5 coexistence epic and the "control plane, not a
job runner" stance in ROADMAP.md and Current state.
Cross-repo git-hygiene audit remediation: surface ~/Projects/standards/INBOX.md items at session start, and switch .gitignore to the deny-by-default .claude/* block (shared wiring allow-listed) plus the canonical secrets/env lines — per standards/portability.md.
- AGENTS.md: rewrite Current state lean for v0.19.0:0; drop the now-completed
full-eval triage block (history lives in git log + EVALUATION.md).
- docs/guides/fastapi-image.md: add two durable conventions — user values
crossing into SSH must go through shellsafe; new endpoints and the
csrf_guard exempt-prefix rule.
- ROADMAP.md: park the remaining non-blocking P2/P3 tech debt from the eval.
History was rewritten with git filter-repo to purge owner-specific values
(IPs, hostnames, SSH username, key name, personal names) from all commits,
tags, and messages — including three LAN IPs and one Start9 address the
v0.18.0:1 working-tree scrub had missed (one still live in HEAD at
docs/AUDIO_API.md). Verified 0 hits across all refs.
- AGENTS.md: Portability + Repo-wart + work-queue #2 + shipping note updated;
commit-SHA references repointed to post-rewrite SHAs (367d986->8d839e3).
- EVALUATION.md: P0 owner-data finding marked resolved; cleaned shorthand
IP-octet fragments (/.87, /11) left by the placeholder substitution.
Triaged from a full independent evaluation (EVALUATION.md). Addresses the
three P0/P1 code findings; the proxy/data APIs that downstream apps consume
are deliberately untouched.
- ssh command injection (P0): new shellsafe.py validates + shlex.quotes every
user-supplied value crossing into an SSH command on the Sparks (model repo,
vllm args/knobs, NIM image/container/volume/port/env, service names).
Boundary validation on POST /api/models and POST /api/nim/install; quoting at
every sink in models/download/nim/services. NGC key now quoted too.
- qdrant path injection (P1): /api/search validates the collection name against
a metacharacter-free whitelist and URL-encodes the path segment.
- csrf (P1): csrf_guard middleware enforces same-origin on state-changing
control endpoints; /v1/*, /scrub, /rehydrate, /api/search, /api/audio/* and
/api/health-event are exempt so external consumers are unaffected.
Verified: injection survives only as a single quoted token, vLLM preflight
shlex.split round-trip intact, CSRF behaviors covered via TestClient, both
offline redaction suites still pass, tsc clean, s9pk rebuilt.
Replace real cluster IPs/hosts/usernames and example names with neutral
placeholders across docs, ops notes, package install text, and the offline
redaction test; delete the obsolete build-time starter prompt. Closes the
portability audit's single blocker. No runtime behavior change.
Rename CLAUDE.md -> AGENTS.md (cross-vendor standard) with a relative
CLAUDE.md symlink so Claude Code still loads it. Move each .claude/rules
file into docs/guides/ (paths: frontmatter preserved) and replace the
rules file with a relative symlink into the guide. Repoint the AGENTS.md
index paragraph at docs/guides/ so non-Claude agents find the guides.
- CLAUDE.md trimmed to whole-repo facts (58 lines); subsystem guidance
moved to .claude/rules/{startos-package,fastapi-image,redaction,
audio-speech}.md with paths: frontmatter so each loads only when
matching files are touched
- .gitignore: track .claude/rules/ while keeping the rest of .claude/
(settings.local.json) ignored
- test-audio-with-speakers.sh: require audio-file arg in docs, replace
owner-specific SPARK_CONTROL/VLLM defaults with generic ones
(localhost dev server + Spark Control vLLM proxy), discover the
loaded LLM via /api/status since /v1/models lists audio models only
- document REDACTION_MAP_DB + CONNECTIVITY_LOG as required for local
dev (/data only exists in the container)
- prettier pass over startos/actions (formatting drift)
Recap Relay dev caught that all audio endpoints route through Spark
Control but chat-completions didn't — clients had to know about both
SC AND the direct vLLM URL on Spark 1. Closes that last gap.
New endpoints:
POST /v1/chat/completions — OpenAI-shape, forwards to vLLM on Spark 1
POST /v1/completions — legacy OpenAI completions, same path
Implementation (image/app/llm_proxy.py):
- Dumb forwarder: request body passed through verbatim, response body
streamed back chunk-by-chunk. No transformation. vLLM already speaks
the same shape; adding any logic here would just create skew.
- Streaming: parses body for `stream: true` and uses httpx.AsyncClient
.stream() + FastAPI StreamingResponse if so. Non-streaming path is
a simple post-and-return.
- 30-minute timeout to accommodate large-context completions (default
httpx 5s would kill anything substantial).
- On upstream non-200 in streaming mode: emits one SSE `error` event
so the client's parser doesn't hang on an empty stream forever.
- On upstream connection error: HTTP 502 with "vllm unreachable" detail.
Now clients can use ONE host for everything:
POST https://spark-control/api/audio/diarize-chunk
POST https://spark-control/v1/audio/transcriptions
POST https://spark-control/v1/chat/completions
GET https://spark-control/api/endpoints (still works for clients that
prefer the direct URLs)
No parakeet container changes. No Reapply patches needed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Recap Relay dev asked: can the diarization output include a confidence
level per segment so the UI can render "Speaker_0?" for uncertain
assignments rather than confidently mislabeling?
Answer: yes. Sortformer's diarize() with include_tensor_outputs=True
returns the per-frame per-speaker sigmoid scores (shape [B, T, 4spk],
~12.6 fps frame rate). The current code argmaxes those into segment
strings and throws the raw scores away. Now: for each output segment,
compute mean probability of the assigned speaker across the segment's
frames → confidence in [0, 1].
Implementation:
- diarizer.py: diarize_chunk() now calls diarize() with
include_tensor_outputs=True, and a new _attach_confidence() helper
derives the per-segment mean probability after parsing the segment
strings. The frame-rate is computed from tensor shape vs audio
duration (no need to hard-code the model's stride).
- All failure paths return confidence=None gracefully — Recap Relay
can treat None as "no info" or fall back to a default threshold.
Endpoint shape change: segments[] now have an optional `confidence`
field in [0, 1] (or None). All other fields unchanged. Existing callers
that ignore the field aren't affected.
Verified with a 5s test signal that the tensor has shape [1, 63, 4]
(63 frames / 5s = 12.6 fps) and values in [0, 1] (sigmoid outputs,
independent per speaker so overlap detection works). Real speech values
will be much higher than the near-zero values of the pure-tone test
signal.
Reapply patches on the Speech Models card after installing v0.13.0:2
to pick up the updated diarizer.py + main.py in the parakeet container.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Spark Control now exposes a per-chunk worker designed for Recap Relay
to orchestrate against. Recap Relay does the chunking + global speaker
clustering (consistent with how it already handles the Gemini path);
Spark Control handles the GPU-bound per-chunk work.
Parakeet container:
- diarizer.py: now also loads NVIDIA TitaNet speaker-verification model
(~25 MB, NeMo-native, no torchaudio). New diarize_chunk() method
runs Sortformer + extracts one 192-dim voice fingerprint per detected
local speaker (concatenating each speaker's audio across the chunk
and running TitaNet's get_embedding).
- main.py: new POST /v1/audio/diarize-chunk endpoint that returns
segments + speakers_detected + fingerprints + models in one shot.
Spark Control:
- new POST /api/audio/diarize-chunk that proxies to parakeet's new
endpoint. Same CUDA-wedge recovery (503 + deep-health probe + 60s
retry-after) as the other audio endpoints. Returns the raw JSON
upstream because Recap Relay is the consumer; no merging needed.
Response shape Recap Relay receives per chunk:
{
"duration": 300.0,
"segments": [{"start_s","end_s","speaker"}, ...], # LOCAL labels
"speakers_detected": ["Speaker_0","Speaker_1",...],
"fingerprints": {"Speaker_0":[192 floats], ...},
"models": {"diarization":"...","embedding":"..."}
}
Recap Relay's job:
1. Chunk audio (existing chunking infrastructure)
2. POST each chunk to /api/audio/diarize-chunk in parallel
3. Collect all fingerprints from all chunks
4. sklearn AgglomerativeClustering(distance_threshold=0.7, metric=cosine)
5. Re-label segments with global cluster IDs
6. Concatenate transcripts (from a separate parallel call to
/v1/audio/transcriptions) with timestamp offsets and merge with
re-labeled diar segments
After installing v0.13.0:1, click "Reapply patches" on the Speech Models
card to push the updated diarizer.py + main.py into the parakeet
container — TitaNet will download (~25 MB) on first call.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
After five hotfix iterations on the WhisperX install (v0.12.0:0–:4) we
never got a working docker build. The fundamental constraint isn't
patchable from outside NVIDIA: NGC PyTorch on ARM64 (the only base that
runs on Spark 2's GB10 Blackwell) ships a custom-versioned torch
2.10.0a0+b558c98 that has no pre-built torchaudio match anywhere.
WhisperX → pyannote → torchaudio is a hard dependency chain we couldn't
satisfy without rebuilding torchaudio against torch 2.10's alpha API.
Walking away cleanly is better than another night of chasing.
Removed from the codebase:
- image/whisperx_container/* (Dockerfile + requirements + app/main.py)
- image/app/whisperx_install.py (install manager + SSH ship-context logic)
- image/Dockerfile COPY whisperx_container
- WHISPERX_* config keys in config.py
- whisperx service entry in services.py
- WhisperX-preferred branch in audio_proxy.py
- /api/whisperx/* endpoints in server.py
- install banner + progress dialog in index.html
- render + handlers in app.js
- .whisperx-install styles in style.css
Spark 2 cleaned in tandem (user-authorized): container removed,
~/whisperx-build/ removed, 5.4 GB of dangling image layers + 1.3 GB of
builder cache reclaimed. parakeet-asr and magpie-tts unaffected and
healthy throughout.
The audio path is back to exactly what shipped in v0.11.0:3:
POST /api/audio/transcribe-with-speakers
→ Parakeet (transcription) + Sortformer (diarization) in parallel
→ merged by timestamp into speaker-labeled blocks
v0.13.0:1+ will add the actually-needed fixes that the WhisperX detour
was meant to address:
1. memory cap on the parakeet-asr container so a long-audio crash
can't swap-thrash Spark 2 again
2. a chunking proxy in /api/audio/transcribe-with-speakers that
splits inputs >10 min before Sortformer
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Build was crashing inside torchaudio's setup.py with:
ModuleNotFoundError: No module named 'torch'
PIP_CONSTRAINT was correctly pinning torch/torchvision in the install
target env, but pip's PEP 517 build isolation creates a SEPARATE fresh
Python env just for the build wheel step — and that env has no torch
in it. torchaudio's setup.py imports torch to discover CUDA flags, so
it crashes. Pip even printed a deprecation warning that this isolation
behavior is hardening, not relaxing.
Fix:
1. Pre-install torchaudio's build deps (setuptools, wheel, ninja,
pybind11) into the main env since we're disabling isolation.
2. Add --no-build-isolation to the torchaudio install so the build
uses NGC's torch directly.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
NGC PyTorch (the only base with working torch on Spark's ARM64 + sm_120
Blackwell) doesn't ship torchaudio. Stock pip wheels are amd64-only AND
ABI-incompatible with NGC's custom torch 2.10.0a anyway. Pip install
just fails or crashes at runtime.
Real fix:
- apt install git cmake build-essential ninja-build
- pip install git+https://github.com/pytorch/audio.git@v2.5.1
with TORCH_CUDA_ARCH_LIST="9.0;10.0;12.0" (sm_120 for Blackwell GB10)
- this compiles torchaudio against the torch already in the image, so
ABI matches by construction
Then constraints.txt locks torch + torchvision + torchaudio so the later
`pip install whisperx` can't swap any of them.
Cost: +3-5 min to the first install. Docker layer cache reuses the
built torchaudio on every subsequent rebuild.
Torchaudio v2.5.1 is the last tag that builds cleanly against
torch 2.5-2.10 — main branch is too volatile against NGC's alpha torch.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
WhisperX docker build was crashing at the model-prewarm step:
OSError: undefined symbol: torch_library_impl
Root cause: the NGC PyTorch base ships custom builds of torch +
torchaudio + torchvision matched together for Blackwell (sm_120). When
pip installed whisperx, it pulled the latest stock torchaudio wheel as
a transitive dep, which was compiled against a different libtorch and
won't load against NGC's.
Fix: at build time, capture NGC's actual torch/torchaudio/torchvision
versions into /tmp/torch-constraints.txt, then `pip install -c` that
constraint for all subsequent installs. pip can't swap torch out, so
the ABI stays consistent. whisperx and pyannote are happy with
torch>=2.0 — NGC's 2.10.0a0 satisfies that easily.
The pinned versions print to the build log so you can see them.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
expand inside shlex.quote()
Symptom: "Failed to ship Dockerfile — bash: line 1: ~/whisperx-build/
Dockerfile: No such file or directory"
Same bug pattern as v0.8.1:1 (disk probe). shlex.quote() wraps in single
quotes, and the remote shell doesn't do tilde expansion inside single
quotes — so it tries to write to a literal directory named "~".
Fix: use $HOME in double-quoted shell context, which the remote shell
expands correctly. The file names (Dockerfile, requirements.txt, etc.)
are hardcoded so they're safe to embed unquoted.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Replaces the manual rsync+build+run with a proper spark-control feature.
First in the audio path that doesn't require shell access on Spark 2.
What's in the box
─────────────────
* image/whisperx_container/ - the build context (Dockerfile, requirements,
app/main.py FastAPI wrapper). Mainline pipeline: faster-whisper for STT +
pyannote 3.1 for diarization + wav2vec2 forced alignment. Single endpoint
/v1/audio/transcribe-with-speakers returns the exact same shape spark-
control's existing endpoint does, so the recap-relay PR spec needs no
changes when we cut over.
* image/app/whisperx_install.py - install manager. ships build context to
Spark 2 over SSH, runs `docker build`, runs `docker run` with 40 GB
memory cap (vs Sortformer's unbounded which thrashed Spark 2 on a 90-min
file), polls /health until both Whisper + pyannote report loaded.
* Audio proxy: /api/audio/transcribe-with-speakers now prefers WhisperX
when its /health reports diarizer_loaded=true, falls back to the legacy
Parakeet + Sortformer path otherwise. Same response shape either way.
Clean cutover, easy rollback (`docker rm whisperx-asr`).
* Dashboard (Audio / Speech tab):
- "Add WhisperX" banner appears when not installed, with a primary
"Install WhisperX" button. One click triggers the install.
- Build progress dialog with phase + elapsed timer + live build log via
SSE (`/api/whisperx/install/{job_id}/stream`).
- After install, WhisperX auto-registers as a managed service alongside
Parakeet and Magpie (Start/Restart/Stop, deep-check, auto-restart).
- Banner self-hides once /api/whisperx/status reports healthy.
New endpoints
─────────────
GET /api/whisperx/status
POST /api/whisperx/install
GET /api/whisperx/install/{job_id}
GET /api/whisperx/install/{job_id}/stream (SSE phase + log)
Config additions (env)
──────────────────────
WHISPERX_HOST (defaults to spark2_host)
WHISPERX_USER (defaults to spark2_user)
WHISPERX_CONTAINER (default: whisperx-asr)
WHISPERX_PORT (default: 8002)
WHISPERX_MODEL (default: medium; tiny/base/small/medium/large-v3)
Dockerfile
──────────
Added COPY whisperx_container /app/whisperx_container so the runtime
install manager can read the build context from inside the spark-control
image and ship it over SSH.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
User feedback: every action button OUTSIDE the parakeet/magpie service
cards looked too big. Specifically called out: "Reapply patches",
"Restart container", "Switch to this", "Download". The ones on the
service cards (Start/Restart/Stop) were the size he liked.
Root cause: the base .btn used font: inherit, so it picked up 15px from
body. .service-actions .btn was the only place with an explicit
font-size: 12px + padding: 6px 12px override.
Fix: change .btn base directly to font-size: 12px + padding: 6px 12px.
Every button across the dashboard now matches the service-card button
footprint. The existing per-context overrides become redundant but
remain in place; they no longer create visible differences.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
User feedback: every pill outside the Always-On Services cards was rendering
visually taller than the "Healthy" status pill they liked. Root cause was
the .tag additions in 0.11.0:1 (line-height: 1.5, display: inline-block)
that didn't match the .status pill on service cards (which has neither).
Dropped both additions, bumped font-size from 11px → 12px so .tag is now
pixel-identical to .status:
font-size: 12px;
padding: 2px 8px;
border-radius: 999px;
background: var(--surface-2);
border: 1px solid var(--border);
Every pill on the dashboard (mode-cluster/mode-solo/cap/on-disk/not-on-disk/
custom-pill/.tag.ok/.tag.warn/.tag.bad) now renders at the same footprint
as the Healthy/Unhealthy/Starting pills on the service cards.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three UX improvements, all client-side; no backend or behavior changes.
1. LLM / Audio tabs under the hardware section. The single long column got
split into two tabbed views:
* LLM -> model swap + download panel + spark-vllm-docker updates
* Audio -> Parakeet/Magpie services + speech-model patches
Selection persists in localStorage; default is LLM. The swap-panel
(in-flight LLM swap) sits ABOVE the tab strip so it stays visible
regardless of which tab is active.
2. Collapsible OpenAI-compatible Endpoint card. New chevron in the card
header collapses everything except the title. State persists per browser
via localStorage. Defaults to collapsed since you rarely need the URL/
model details visible (and the same info is one tab swap away).
3. Unified pill sizing. The .sm-pill class in speech-models was rendering
subtly larger than .tag pills on model cards. Dropped .sm-pill entirely
and reused .tag with semantic color modifiers (.tag.ok / .tag.warn /
.tag.bad). Same 11px / 2px×8px footprint everywhere now. Also added
explicit line-height: 1.5 + display: inline-block to .tag to lock down
vertical sizing.
No new endpoints, no new dependencies. Tested locally with node --check
and ast.parse(). Verified the tab DOM structure wraps the right sections
and the speech-models panel still self-shows/hides on data load.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Folds the image/parakeet_patches/apply.sh script into a one-click
dashboard action and adds drift detection so you can see at a glance
whether the parakeet-asr container has the latest Sortformer overlays
that spark-control ships.
Backend:
* image/app/speech_models.py - SpeechModelsManager: reads /health from
Parakeet, sha256s the local overlay files inside spark-control's
Docker image (/app/parakeet_patches), sha256s the same files inside
the parakeet-asr container via `docker exec ... sha256sum`, surfaces
in_sync / drift / missing status per file.
* GET /api/speech-models - status payload
* POST /api/speech-models/reapply - copies overlays into container,
verifies python syntax, restarts,
polls /health for ~120s, returns
step-by-step result
* POST /api/speech-models/restart - plain `docker restart parakeet-asr`
Dockerfile: now COPY parakeet_patches into the image at /app/parakeet_patches
so the runtime can read them. Future spark-control releases auto-carry
newer overlay versions; the panel surfaces drift after upgrade.
Frontend: new "Speech model patches" section on the dashboard with
* Status pill (in sync / drift / missing)
* Per-file SHA comparison (local vs container)
* Loaded-models pills (ASR + diarizer)
* Reapply + Restart buttons (both with confirmation modals)
* Live progress display during reapply with per-step ✓/✗
Verified post-install against the running cluster:
GET /api/speech-models shows both files in_sync (SHAs match) and both
models loaded ready on Spark 2.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
Keysat