portal-tts: DRAFT stack — Piper TTS (CPU, always-on) for portal-assistant
Draft (NOT applied) of a new infra stack deploying Piper as an in-cluster text-to-speech service for the portal-assistant Gateway (portal-assistant issue #3, ADR-0003). Bulgarian (bg_BG-dimitar-medium) + English (en_US-lessac-medium), voice chosen per request. Why this shape: - CPU-only, always-on (replicas=1, no GPU): Piper runs in real time on CPU, so this keeps TTS off the OOM-prone shared T4 that the two GPU siblings (tts/chatterbox, portal-stt) already contend for. Bulgarian isn't on chatterbox anyway (its langs exclude bg). - OpenAI-compatible image (openedai-speech-min, /v1/audio/speech) so the Gateway gets raw audio bytes per its tts.synthesize(text, lang) -> bytes contract and treats Piper + the future edge-tts fallback identically — same shape chatterbox already uses. - Voices on an NFS-SSD PVC, downloaded from rhasspy/piper-voices by an init container on first boot; a ConfigMap maps request voice bg/en -> .onnx model. - ClusterIP only (audio stays on the LAN; the Gateway is the only externally exposed component, ADR-0001). Mirrors the just-written portal-stt sibling stack's conventions. terraform fmt clean; terraform validate passes (only the codebase-wide kubernetes_namespace deprecation warnings). HITL: operator reviews + applies via GitOps; do not apply from a worktree. Open items flagged in main.tf (image choice on a frozen upstream; resource sizing to confirm with krr). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Viktor Barzin
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stacks/portal-tts/main.tf
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# =============================================================================
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# portal-tts — Piper TTS (CPU, always-on) for the portal-assistant Gateway
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# =============================================================================
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#
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# DRAFT for operator review (portal-assistant issue #3). HITL apply: an agent
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# drafts; the operator applies via GitOps and verifies the rollout. Do NOT
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# `terragrunt apply` this from a worktree.
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#
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# WHAT: a single ALWAYS-ON Piper deployment serving Bulgarian
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# (`bg_BG-dimitar-medium`) AND English (`en_US-lessac-medium`), with the voice
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# chosen PER REQUEST, behind a ClusterIP Service `portal-tts.portal-tts.svc:8000`.
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# CPU-ONLY — no GPU node selector / toleration / nvidia.com/gpu request (Piper
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# is a fast CPU neural TTS; ADR-0003). Audio never leaves the LAN; the
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# portal-assistant Gateway is the only externally exposed component (ADR-0001),
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# so there is no ingress / Authentik here.
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#
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# WHY CPU + ALWAYS-ON (contrast the two GPU siblings):
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# * tts/ (chatterbox) scales 0<->1 behind a free-VRAM CronJob gate — it is a
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# best-effort BATCH tenant on the shared T4.
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# * portal-stt/ (Speaches) is warm-resident on ONE T4 slice — interactive STT
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# that must not pay a cold model load.
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# Piper needs neither: it runs in real time on CPU (no GPU contention at all),
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# so the simplest correct design is replicas=1, always up. Keeping it off the
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# T4 also REMOVES one tenant from the OOM-prone shared card
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# (docs/post-mortems/2026-06-02-immich-ml-ttl-gpu-oom-recruiter.md) — Bulgarian
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# isn't on chatterbox anyway (its 23 langs exclude bg; ADR-0003).
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#
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# API SHAPE (the Gateway team's contract): openedai-speech is OpenAI-compatible.
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# POST /v1/audio/speech
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# Content-Type: application/json
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# { "model": "tts-1", "input": "<text>", "voice": "<bg|en>",
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# "response_format": "wav", "speed": 1.0 }
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# -> 200, body = raw audio bytes (wav/mp3/opus/flac/aac/pcm per response_format)
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# This matches the Gateway's tts.synthesize(text, lang) -> bytes interface
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# (portal-assistant gateway/app/pipeline.py): map lang "bg"->voice "bg",
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# anything else -> "en". Same OpenAI shape chatterbox already uses, so the
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# Gateway can treat Piper and the edge-tts fallback identically.
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#
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# PLUGGABLE FALLBACK (noted, NOT built here — a Gateway-side concern): ADR-0003
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# keeps TTS a swappable backend with Microsoft edge-tts as an online
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# Bulgarian-quality fallback. The Gateway selects Piper (this Service, on-LAN
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# default) vs edge-tts (cloud) per its own config; nothing in THIS stack needs
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# to change to add edge-tts. If a second in-cluster engine is ever wanted,
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# add a sibling Deployment+Service and let the Gateway choose.
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#
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# IMAGE CHOICE (OPEN ITEM — operator please confirm before apply):
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# Primary: ghcr.io/matatonic/openedai-speech-min — the CPU-only, piper-only
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# (~1 GiB) variant of openedai-speech. OpenAI /v1/audio/speech, multi-voice via
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# a voice_to_speaker.yaml map, returns raw audio bytes. Pre-built for
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# linux/amd64+arm64, pullable from ghcr (tags: latest, 0.18.2, 0.18.1, ...).
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# CAVEAT: the upstream repo was ARCHIVED 2026-01-04 (read-only, no further
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# updates / security patches). It is feature-complete and stable for this use,
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# but pinned (not Keel-tracked) precisely because it is frozen upstream.
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# Alternative if a maintained image is preferred: arkdevuk/Webpiper (FastAPI,
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# actively developed) — but it returns JSON {url} requiring a SECOND fetch to
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# retrieve the wav, a worse fit for the Gateway's bytes contract; it would
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# need a small Gateway adapter. The rhasspy/wyoming-piper HTTP server is NOT
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# suitable: it loads ONE voice per process (no per-request voice switch).
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# =============================================================================
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variable "nfs_server" {
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type = string
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description = "NFS server (Proxmox host). From config.tfvars (192.168.1.127)."
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}
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variable "piper_image" {
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type = string
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# CPU-only piper-only openedai-speech. Pinned to 0.18.2 (the newest published
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# tag; repo archived 2026-01-04 so this is effectively the final release) for
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# reproducibility — NOT :latest, which would also drift to the same frozen
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# build but loses the explicit version record. linux/amd64 confirmed.
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default = "ghcr.io/matatonic/openedai-speech-min:0.18.2"
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description = "openedai-speech CPU/piper-only image. See IMAGE CHOICE note in main.tf."
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}
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variable "bg_voice" {
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type = string
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# The single Bulgarian Piper voice (rhasspy/piper-voices). ADR-0003 names this
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# exact model; it was reviewed against edge-tts in the M0.2 bake-off.
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default = "bg_BG-dimitar-medium"
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description = "Bulgarian Piper voice model stem (rhasspy/piper-voices)."
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}
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variable "en_voice" {
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type = string
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# English Piper voice. lessac-medium is the canonical balanced en_US voice
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# (the upstream openedai-speech default-quality pick). Swappable to any
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# rhasspy/piper-voices en stem (e.g. en_US-amy-medium, en_GB-alba-medium).
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default = "en_US-lessac-medium"
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description = "English Piper voice model stem (rhasspy/piper-voices)."
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}
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locals {
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namespace = "portal-tts"
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labels = { app = "portal-tts" }
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# rhasspy/piper-voices HuggingFace layout:
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# resolve/main/<lang>/<locale>/<name>/<quality>/<locale>-<name>-<quality>.onnx
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# Each voice needs BOTH the .onnx and the matching .onnx.json (Piper reads the
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# sample rate / phoneme map from the .json next to the model). All 4 URLs were
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# HEAD-verified 200 on 2026-06-17. Derived from the voice stems so changing a
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# voice variable updates both the download URL and the config map together.
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hf_base = "https://huggingface.co/rhasspy/piper-voices/resolve/main"
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voice_models = {
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# voice stem => HF directory path "<lang>/<locale>/<name>/<quality>"
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(var.bg_voice) = "bg/bg_BG/dimitar/medium"
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(var.en_voice) = "en/en_US/lessac/medium"
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}
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# voice_to_speaker.yaml: the openedai-speech config that maps a REQUEST voice
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# name ("bg" / "en") to a Piper .onnx on the PVC. The Gateway sends
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# voice="bg" or "en"; the server resolves it here. (We expose short logical
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# names, not the long model stems, so the Gateway's lang->voice map is trivial
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# and stable even if the underlying model stem changes.) The default model
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# name is "tts-1" (openedai-speech's piper model id).
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voice_to_speaker = yamlencode({
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"tts-1" = {
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bg = {
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model = "voices/${var.bg_voice}.onnx"
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speaker = null # single-speaker model -> default
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}
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en = {
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model = "voices/${var.en_voice}.onnx"
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speaker = null
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}
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}
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})
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# Init-container provisioning script. Downloads each voice's .onnx + .onnx.json
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# into the PVC's voices/ dir IF MISSING (idempotent — re-runs skip
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# already-present files), then copies the config map's voice_to_speaker.yaml
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# into the PVC's config/ dir. Pure POSIX + wget (busybox has wget).
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download_script = <<-EOT
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set -eu
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mkdir -p /data/voices /data/config
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fetch() { # $1 = url, $2 = dest
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if [ -s "$2" ]; then echo "have $2"; return 0; fi
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echo "get $1 -> $2"
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wget -q -O "$2.tmp" "$1"
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mv "$2.tmp" "$2"
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}
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%{for stem, dir in local.voice_models~}
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fetch "${local.hf_base}/${dir}/${stem}.onnx" "/data/voices/${stem}.onnx"
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fetch "${local.hf_base}/${dir}/${stem}.onnx.json" "/data/voices/${stem}.onnx.json"
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%{endfor~}
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cp /config-src/voice_to_speaker.yaml /data/config/voice_to_speaker.yaml
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echo "voices:"; ls -la /data/voices
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echo "config:"; cat /data/config/voice_to_speaker.yaml
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EOT
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}
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resource "kubernetes_namespace" "portal_tts" {
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metadata {
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name = local.namespace
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labels = {
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tier = local.tiers.aux # CPU-only best-effort helper, not a GPU tenant
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"istio-injection" = "disabled"
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"keel.sh/enrolled" = "true"
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}
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}
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lifecycle {
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# KYVERNO_LIFECYCLE_V1: goldilocks-vpa-auto-mode ClusterPolicy stamps this label on every namespace
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ignore_changes = [metadata[0].labels["goldilocks.fairwinds.com/vpa-update-mode"]]
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}
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}
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# Voice models + config on NFS-SSD: fast first-load, persists across restarts,
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# and RWX so a future seed/inspect pod can touch it. Path /srv/nfs-ssd/portal-tts
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# on the Proxmox host. Small — two medium Piper voices are ~60-120 MiB each.
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# Mirrors portal-stt's nfs_models pattern.
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module "nfs_models" {
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source = "../../modules/kubernetes/nfs_volume"
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name = "portal-tts-models"
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namespace = kubernetes_namespace.portal_tts.metadata[0].name
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nfs_server = var.nfs_server
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nfs_path = "/srv/nfs-ssd/portal-tts"
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storage = "2Gi" # 2 medium Piper voices + headroom for more
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}
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# One-shot bootstrap: /srv/nfs-ssd is exported whole-tree but the portal-tts
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# SUBDIR must exist before kubelet can bind-mount it (chatterbox/portal-stt both
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# hit exit 32 on a missing subdir). Mount the export ROOT (which exists) and
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# mkdir the subtree; kubelet's mount retry then heals the main pod. Idempotent;
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# immutable-once-created.
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resource "kubernetes_job" "models_dir_init" {
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metadata {
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name = "portal-tts-models-dir-init"
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namespace = kubernetes_namespace.portal_tts.metadata[0].name
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labels = local.labels
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}
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spec {
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backoff_limit = 3
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ttl_seconds_after_finished = 86400
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template {
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metadata { labels = local.labels }
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spec {
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restart_policy = "Never"
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container {
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name = "mkdir"
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image = "busybox:1.37"
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command = ["sh", "-c", "mkdir -p /mnt/portal-tts/voices /mnt/portal-tts/config && ls -la /mnt/portal-tts"]
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volume_mount {
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name = "nfs-ssd-root"
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mount_path = "/mnt"
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}
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}
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volume {
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name = "nfs-ssd-root"
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nfs {
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server = var.nfs_server
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path = "/srv/nfs-ssd"
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}
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}
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}
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}
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}
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wait_for_completion = true
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timeouts { create = "3m" }
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}
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# The voice_to_speaker.yaml map, mounted into the init container which copies it
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# onto the PVC (openedai-speech reads config from a writable dir). Checksum drives
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# a rollout when the voice map changes.
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resource "kubernetes_config_map" "voices" {
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metadata {
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name = "portal-tts-voices"
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namespace = kubernetes_namespace.portal_tts.metadata[0].name
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labels = local.labels
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}
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data = {
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"voice_to_speaker.yaml" = local.voice_to_speaker
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}
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}
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# Always-on Piper. replicas=1, never scaled to zero (no off-peak gate). CPU-only:
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# NO node_selector / toleration / nvidia.com/gpu — it runs on any worker. The
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# init container downloads the voices to the PVC and seeds the config before the
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# server starts; openedai-speech then serves both voices, selectable per request.
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resource "kubernetes_deployment" "portal_tts" {
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metadata {
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name = "portal-tts"
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namespace = kubernetes_namespace.portal_tts.metadata[0].name
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labels = merge(local.labels, { tier = local.tiers.aux })
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}
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# wait_for_rollout left default (true): a plain apply SHOULD block until the
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# voices download + the server reports healthy, surfacing a bad image/voice
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# early. First boot pulls ~2 voices from HuggingFace onto the PVC.
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spec {
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replicas = 1
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# NFS PVC is RWX so RollingUpdate would be safe, but Recreate keeps it simple
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# and avoids two pods racing the same voices/ dir on first download.
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strategy { type = "Recreate" }
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selector {
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match_labels = { app = "portal-tts" }
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}
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template {
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metadata {
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labels = { app = "portal-tts" }
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annotations = {
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"checksum/voices" = sha256(local.voice_to_speaker)
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}
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}
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spec {
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# Download voices + seed config onto the PVC before the server starts.
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init_container {
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name = "fetch-voices"
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image = "busybox:1.37"
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command = ["sh", "-c", local.download_script]
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volume_mount {
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name = "models"
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mount_path = "/data"
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}
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volume_mount {
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name = "config-src"
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mount_path = "/config-src"
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}
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resources {
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requests = { cpu = "20m", memory = "32Mi" }
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limits = { memory = "64Mi" }
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}
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}
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container {
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name = "portal-tts"
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image = var.piper_image
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# openedai-speech serves /v1/audio/speech on :8000. It reads
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# config/voice_to_speaker.yaml and voices/*.onnx relative to its
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# workdir (/app); we mount the PVC at /app/voices and /app/config so
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# the init-seeded files are found.
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port {
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container_port = 8000
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name = "http"
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}
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env {
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name = "OPENEDAI_LOG_LEVEL"
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value = "INFO" # image default is INFO; explicit for legibility
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}
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volume_mount {
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name = "models"
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mount_path = "/app/voices"
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sub_path = "voices"
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}
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volume_mount {
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name = "models"
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mount_path = "/app/config"
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sub_path = "config"
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}
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# openedai-speech has no /health on its OpenAI surface. Use a TCP probe
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# — the uvicorn socket binds only after the app (and the piper voices
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# index) is ready. The init container already downloaded the voices, so
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# process start is fast.
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startup_probe {
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tcp_socket { port = 8000 }
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period_seconds = 5
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failure_threshold = 24 # ~2 min
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}
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readiness_probe {
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tcp_socket { port = 8000 }
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period_seconds = 15
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failure_threshold = 4
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}
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liveness_probe {
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tcp_socket { port = 8000 }
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initial_delay_seconds = 20
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period_seconds = 30
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failure_threshold = 5
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}
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resources {
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# Piper is light on CPU. No CPU limit (cluster policy: CFS throttling
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# avoided). Memory sized for the python runtime + 2 loaded onnx voices
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# (each medium model ~60-120 MiB + onnxruntime arena). VERIFY with krr
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# after a few days of real traffic and tighten.
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requests = {
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cpu = "100m"
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memory = "512Mi"
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}
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limits = {
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memory = "1Gi"
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}
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}
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}
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volume {
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name = "models"
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persistent_volume_claim {
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claim_name = module.nfs_models.claim_name
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}
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}
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volume {
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name = "config-src"
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config_map {
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name = kubernetes_config_map.voices.metadata[0].name
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}
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}
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}
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}
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}
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lifecycle {
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ignore_changes = [
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spec[0].template[0].spec[0].dns_config, # KYVERNO_LIFECYCLE_V1
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# image is TF-OWNED (pinned tag on a FROZEN upstream) — do NOT let Keel
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# churn it. Ignore keel's annotation noise but keep the image pin applying.
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metadata[0].annotations["keel.sh/policy"],
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metadata[0].annotations["keel.sh/trigger"],
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metadata[0].annotations["keel.sh/pollSchedule"], # KYVERNO_LIFECYCLE_V2
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metadata[0].annotations["keel.sh/match-tag"],
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metadata[0].annotations["kubernetes.io/change-cause"],
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metadata[0].annotations["deployment.kubernetes.io/revision"],
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spec[0].template[0].metadata[0].annotations["keel.sh/update-time"], # KEEL_LIFECYCLE_V1
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]
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}
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}
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# ClusterIP — in-cluster only (the Gateway calls this; audio stays on the LAN).
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# No ingress, no Authentik: the Gateway is the only externally exposed component
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# (ADR-0001) and holds the edge auth. OpenAI speech path:
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# http://portal-tts.portal-tts.svc.cluster.local:8000/v1/audio/speech
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resource "kubernetes_service" "portal_tts" {
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metadata {
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||||
name = "portal-tts"
|
||||
namespace = kubernetes_namespace.portal_tts.metadata[0].name
|
||||
labels = local.labels
|
||||
annotations = {
|
||||
# openedai-speech has no /metrics endpoint; annotation-based scrape kept on
|
||||
# a liveness path so the Service stays in the scrape set (Ready-endpoint
|
||||
# relabeling filters non-Ready pods). Probes the OpenAI models list.
|
||||
"prometheus.io/scrape" = "true"
|
||||
"prometheus.io/path" = "/v1/models"
|
||||
"prometheus.io/port" = "8000"
|
||||
}
|
||||
}
|
||||
spec {
|
||||
type = "ClusterIP"
|
||||
selector = { app = "portal-tts" }
|
||||
port {
|
||||
name = "http"
|
||||
port = 8000
|
||||
target_port = 8000
|
||||
}
|
||||
}
|
||||
}
|
||||
33
stacks/portal-tts/terragrunt.hcl
Normal file
33
stacks/portal-tts/terragrunt.hcl
Normal file
|
|
@ -0,0 +1,33 @@
|
|||
include "root" {
|
||||
path = find_in_parent_folders()
|
||||
}
|
||||
|
||||
dependency "platform" {
|
||||
config_path = "../platform"
|
||||
skip_outputs = true
|
||||
}
|
||||
|
||||
# portal-tts: in-cluster text-to-speech for the portal-assistant Gateway
|
||||
# (portal-assistant issue #3, ADR-0003). One ALWAYS-ON Deployment of Piper
|
||||
# (ghcr.io/matatonic/openedai-speech-min, OpenAI-compatible /v1/audio/speech)
|
||||
# serving Bulgarian `bg_BG-dimitar-medium` + English `en_US-lessac-medium`, voice
|
||||
# chosen PER REQUEST, behind a single ClusterIP Service
|
||||
# `portal-tts.portal-tts.svc:8000`. Speech path: /v1/audio/speech.
|
||||
#
|
||||
# CPU-ONLY: Piper is a fast CPU neural TTS — NO GPU node selector / toleration /
|
||||
# nvidia.com/gpu request. This deliberately keeps TTS off the OOM-prone shared
|
||||
# T4 (the two GPU siblings tts/chatterbox + portal-stt already contend for it);
|
||||
# Bulgarian isn't available on chatterbox anyway (ADR-0003). replicas=1, never
|
||||
# scaled to zero — no off-peak gate needed when there's no GPU to free.
|
||||
#
|
||||
# Voices live on an NFS-SSD PVC, downloaded from rhasspy/piper-voices by an init
|
||||
# container on first boot (both .onnx + .onnx.json), then persist. A ConfigMap
|
||||
# supplies voice_to_speaker.yaml mapping request voice "bg"/"en" -> .onnx model.
|
||||
#
|
||||
# PLUGGABLE: ADR-0003 keeps TTS a swappable backend with edge-tts as an online
|
||||
# Bulgarian fallback — that switch is Gateway-side; nothing here changes for it.
|
||||
#
|
||||
# nfs_server comes from config.tfvars (192.168.1.127) via the root inputs.
|
||||
#
|
||||
# HITL: agent drafts; operator applies via GitOps, then verifies the rollout +
|
||||
# a bg/en /v1/audio/speech smoke test (curl returns audio bytes).
|
||||
Loading…
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Reference in a new issue