parakeet: rewrite on sherpa-onnx; own the wrapper end-to-end

The Shadowfita FastAPI wrapper hit two unfixed upstream bugs on the
first real /transcribe call — chunker return-shape mismatch (open
issue #16) and a `torchaudio.tensor` that doesn't exist (open #10).
Rather than babysit someone else's half-tested code, switched to
sherpa-onnx with the prebuilt int8 Parakeet-TDT tarball from k2-fsa,
and wrote our own ~60-line FastAPI wrapper.

Moving parts now owned in-tree:
  Dockerfile      CUDA 12.8 + cuDNN 9 runtime base, installs
                  sherpa-onnx==1.12.39+cuda12.cudnn9 + fastapi +
                  soundfile + libasound2 (sherpa-onnx links to ALSA
                  at load time even when we never touch a mic).
  app.py          OfflineRecognizer.from_transducer() once at startup;
                  /transcribe and /v1/audio/transcriptions both accept
                  multipart uploads and return {"text": ...}.
  entrypoint.sh   Idempotent model download to /models on first run
                  (~400 MB int8 tarball), then exec uvicorn.

Smoke test: 0.wav (bundled in the tarball, The House of the Seven
Gables excerpt) transcribes cleanly in ~1.2s on GPU.

PARAKEET_MODEL_URL in .env lets you swap to the v3 (25-language)
tarball without touching any other files. Wipe *.onnx + tokens.txt
from the models dir and the entrypoint re-downloads.

stacks/parakeet/compose.yaml

@@ -1,37 +1,28 @@
-# Parakeet ASR — NVIDIA Parakeet-TDT 0.6B v2 speech-to-text.
+# Parakeet ASR via sherpa-onnx + our own thin FastAPI wrapper.
 #
-# Wraps Shadowfita/parakeet-tdt-0.6b-v2-fastapi (FastAPI + Silero VAD +
-# WebSocket streaming). Upstream provides no prebuilt image, so we
-# build from a pinned git commit via docker buildx's git URL context
-# — no source files vendored into this repo.
+# We previously wrapped Shadowfita/parakeet-tdt-0.6b-v2-fastapi but hit two
+# unfixed upstream bugs (open issues #16 + #10) the first time we actually sent
+# a transcription request. Switched to sherpa-onnx — ONNX Runtime + CUDA, a
+# prebuilt int8 quantized Parakeet-TDT from k2-fsa — and wrote our own ~50-line
+# wrapper we own end-to-end.
 #
-# Runs on irv-ml1 (dual GPU). Both GPUs exposed; upstream respects
-# CUDA_VISIBLE_DEVICES if you want to pin later.
+# Model weights (~400 MB int8) download on first run via the entrypoint to
+# ${PARAKEET_MODELS_DIR}/ (persistent host bind mount). Subsequent starts skip
+# the download.
 #
-# HF weights (~2.5 GB for parakeet-tdt-0.6b-v2) cache to
-# ${PARAKEET_MODELS_DIR} via HF_HOME=/models, persistent across
-# container recreates.
-#
-# API routes (per upstream README):
-#   POST /transcribe          — batch transcription
-#   WS   /ws/transcribe       — streaming with Silero VAD
+# API:
+#   POST /transcribe                  — multipart file upload, returns {"text": "..."}
+#   POST /v1/audio/transcriptions     — same body, OpenAI-compatible path alias
 #   GET  /healthz
 #
-# Not a literal OpenAI `/v1/audio/transcriptions` path; point clients
-# at /transcribe directly, or add a reverse-proxy alias if drop-in
-# compat is needed later.
-#
-# First `up` triggers a fresh docker build (python:3.10-slim +
-# torch + NeMo ≈ 5–10 min). Subsequent starts reuse the cached
-# image unless PARAKEET_SHA changes.
-#
 # All tunables live in .env — edit that, not this file.
 
 services:
   parakeet:
-    image: local/parakeet:${PARAKEET_SHA}
+    image: local/parakeet:${PARAKEET_TAG}
     build:
-      context: https://github.com/Shadowfita/parakeet-tdt-0.6b-v2-fastapi.git#${PARAKEET_SHA}
+      context: .
+      dockerfile: Dockerfile
     container_name: parakeet
     restart: unless-stopped
     runtime: nvidia
@@ -39,14 +30,10 @@ services:
       - "${PARAKEET_BIND:-0.0.0.0}:${PARAKEET_PORT}:8000"
     environment:
       - NVIDIA_VISIBLE_DEVICES=all
-      - HF_HOME=/models
-      - DEVICE=cuda
-      - MODEL_PRECISION=${PARAKEET_MODEL_PRECISION:-fp16}
-      - BATCH_SIZE=${PARAKEET_BATCH_SIZE:-4}
-      - TARGET_SAMPLE_RATE=16000
-      - MAX_AUDIO_DURATION=${PARAKEET_MAX_AUDIO_DURATION:-30}
-      - VAD_THRESHOLD=${PARAKEET_VAD_THRESHOLD:-0.5}
-      - PROCESSING_TIMEOUT=${PARAKEET_PROCESSING_TIMEOUT:-60}
+      - MODEL_DIR=/models
+      - MODEL_URL=${PARAKEET_MODEL_URL}
+      - PROVIDER=${PARAKEET_PROVIDER:-cuda}
+      - NUM_THREADS=${PARAKEET_NUM_THREADS:-1}
       - LOG_LEVEL=${PARAKEET_LOG_LEVEL:-INFO}
     volumes:
       - ${PARAKEET_MODELS_DIR}:/models
@@ -55,13 +42,11 @@ services:
       interval: 30s
       timeout: 10s
       retries: 3
-      # First `up` may spend several minutes on torch/NeMo install
-      # during the image build phase; after the image exists, startup
-      # is ~30-60s (NeMo model load).
-      start_period: 180s
+      # First boot may include a ~400 MB model download.
+      start_period: 300s
     labels:
       - homepage.group=AI Systems
       - homepage.name=Parakeet ASR
       - homepage.icon=mdi-microphone
-      - homepage.description=Parakeet-TDT speech-to-text (irv-ml1)
+      - homepage.description=Parakeet-TDT speech-to-text via sherpa-onnx (irv-ml1)
       - homepage.href=http://10.100.79.3:${PARAKEET_PORT}