fc-ttsreader: ship cluster-native fc-speech-align (faster-whisper) + bump web
- New ttsreader-align Deployment + Service + 5Gi PVC under apps/fc-ttsreader/. Wraps SYSTRAN/faster-whisper in a small FastAPI app exposing POST /align (fc-align contract used by Shared.Speech) AND POST /transcribe (audio-in feature consumed by ttsreader-web Lane G). Source: apps/fc-ttsreader/speech-align/ (Dockerfile + app.py + requirements.txt). Built locally (apt-get RUN steps need BLUEJAY-WS, not noc1) and ctr-imported to all 3 RKE2 nodes. - ttsreader-web env: flip Speech__Alignment__Enabled=true and point BaseUrl at http://ttsreader-align.fc-ttsreader.svc.cluster.local.:9200. Add new TtsReader__Transcription__* env triplet pointing at the same service (same /transcribe endpoint). - Bump ttsreader-web image to v202604251046 (carries the TranscriptionController + MCP tool + Quick.razor InputFile UI).
This commit is contained in:
Andrew Stoltz
@@ -112,6 +112,109 @@ spec:
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persistentVolumeClaim:
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claimName: ttsreader-piper-data
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---
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# fc-speech-align — cluster-native faster-whisper wrapper.
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# Exposes POST /align (fc-align contract used by FlowerCore.Shared.Speech) AND
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# POST /transcribe (audio-file-in feature). CPU model = base.en, int8 compute.
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# Source: bluejay-infra/apps/fc-ttsreader/speech-align/ (Dockerfile + app.py).
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apiVersion: v1
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kind: PersistentVolumeClaim
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metadata:
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name: ttsreader-align-models
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namespace: fc-ttsreader
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spec:
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accessModes:
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- ReadWriteOnce
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resources:
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requests:
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storage: 5Gi
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---
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: ttsreader-align
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namespace: fc-ttsreader
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labels:
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app.kubernetes.io/name: ttsreader-align
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app.kubernetes.io/part-of: flowercore
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spec:
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replicas: 1
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strategy:
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type: Recreate
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selector:
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matchLabels:
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app.kubernetes.io/name: ttsreader-align
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template:
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metadata:
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labels:
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app.kubernetes.io/name: ttsreader-align
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app.kubernetes.io/part-of: flowercore
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spec:
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securityContext:
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fsGroup: 1654
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runAsNonRoot: true
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runAsUser: 1654
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containers:
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- name: align
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image: localhost/fc-speech-align:v1
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imagePullPolicy: Never
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ports:
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- containerPort: 9200
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name: http
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env:
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- name: WHISPER_MODEL
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value: "Systran/faster-whisper-base.en"
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- name: WHISPER_DEVICE
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value: "cpu"
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- name: WHISPER_COMPUTE_TYPE
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value: "int8"
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- name: WHISPER_CACHE_DIR
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value: "/models"
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- name: DEFAULT_LANGUAGE
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value: "en"
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resources:
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requests:
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cpu: 250m
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memory: 512Mi
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limits:
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cpu: 2000m
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memory: 2Gi
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volumeMounts:
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- name: models
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mountPath: /models
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readinessProbe:
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httpGet:
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path: /health
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port: 9200
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initialDelaySeconds: 30
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periodSeconds: 10
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timeoutSeconds: 5
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failureThreshold: 18
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livenessProbe:
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httpGet:
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path: /health
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port: 9200
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initialDelaySeconds: 180
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periodSeconds: 30
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timeoutSeconds: 5
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failureThreshold: 3
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volumes:
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- name: models
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persistentVolumeClaim:
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claimName: ttsreader-align-models
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---
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apiVersion: v1
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kind: Service
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metadata:
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name: ttsreader-align
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namespace: fc-ttsreader
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spec:
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selector:
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app.kubernetes.io/name: ttsreader-align
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ports:
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- port: 9200
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targetPort: 9200
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name: http
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---
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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@@ -142,7 +245,7 @@ spec:
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fsGroupChangePolicy: OnRootMismatch
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containers:
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- name: web
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image: localhost/fc-ttsreader-web:v202604251018
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image: localhost/fc-ttsreader-web:v202604251046
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imagePullPolicy: Never
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ports:
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- containerPort: 5217
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@@ -173,20 +276,24 @@ spec:
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- name: TtsReader__Kokoro__TimeoutSeconds
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value: "120"
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- name: Speech__Alignment__Enabled
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# Off until either:
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# (a) a native /align backend is deployed inside the cluster, or
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# (b) the BLUEJAY-WS host exposes the speaches container on the
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# LAN-routable bind (10.0.56.20:9200, not just 127.0.0.1)
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# AND Common ships the openai-compatible Backend support
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# (currently on feat/shared-indexing, not on master).
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# While disabled, /preview-with-timings still returns word timings
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# via EstimatedAlignmentClient — slightly less accurate, but the
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# UI can still drive word-level highlight playback.
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value: "false"
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# Cluster-native faster-whisper (Lane F, 2026-04-25). The
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# ttsreader-align deployment in this manifest wraps
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# SYSTRAN/faster-whisper with a /align endpoint matching the
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# FlowerCore.Shared.Speech master contract.
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value: "true"
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- name: Speech__Alignment__BaseUrl
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value: "http://10.0.56.20:9200"
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value: "http://ttsreader-align.fc-ttsreader.svc.cluster.local.:9200"
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- name: Speech__Alignment__TimeoutSeconds
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value: "120"
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# Cluster-native transcription endpoint shares the same pod
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# (POST /transcribe). Lane G consumes this from the
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# FlowerCore.TtsReader.Web AudioImport feature.
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- name: TtsReader__Transcription__Enabled
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value: "true"
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- name: TtsReader__Transcription__BaseUrl
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value: "http://ttsreader-align.fc-ttsreader.svc.cluster.local.:9200"
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- name: TtsReader__Transcription__TimeoutSeconds
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value: "300"
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- name: TtsReader__Ollama__BaseUrl
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value: "http://10.0.57.17:11434"
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- name: TtsReader__Ollama__DefaultModel
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47
apps/fc-ttsreader/speech-align/Dockerfile
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47
apps/fc-ttsreader/speech-align/Dockerfile
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@@ -0,0 +1,47 @@
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# FlowerCore speech-align — wraps SYSTRAN/faster-whisper with /align +
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# /transcribe endpoints used by FlowerCore.TtsReader. CPU-only image; the
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# default int8 compute type runs base.en at ~real-time on a single core.
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#
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# Build: podman build -t localhost/fc-speech-align:<ver> .
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# Run: podman run --rm -p 9200:9200 -v fc-speech-align-models:/models localhost/fc-speech-align:<ver>
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FROM python:3.12-slim AS base
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ENV PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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PIP_DISABLE_PIP_VERSION_CHECK=1 \
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PIP_NO_CACHE_DIR=1 \
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WHISPER_MODEL=Systran/faster-whisper-base.en \
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WHISPER_CACHE_DIR=/models \
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WHISPER_DEVICE=cpu \
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WHISPER_COMPUTE_TYPE=int8 \
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DEFAULT_LANGUAGE=en \
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MAX_AUDIO_BYTES=52428800
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# faster-whisper depends on libsndfile1 + libgomp1 (OpenMP runtime). ffmpeg is
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# pulled in for non-WAV inputs (transcribe accepts any container).
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RUN apt-get update \
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&& apt-get install -y --no-install-recommends \
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libsndfile1 \
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libgomp1 \
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ffmpeg \
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ca-certificates \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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COPY requirements.txt /app/
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RUN pip install --no-cache-dir -r requirements.txt
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COPY app.py /app/
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# Run as a non-root user to satisfy K8s securityContext.runAsNonRoot.
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RUN useradd --create-home --shell /usr/sbin/nologin --uid 1654 align \
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&& mkdir -p /models \
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&& chown -R 1654:1654 /models
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USER 1654
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EXPOSE 9200
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HEALTHCHECK --interval=30s --timeout=5s --start-period=120s --retries=3 \
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CMD python -c "import urllib.request,sys; urllib.request.urlopen('http://127.0.0.1:9200/health',timeout=3); sys.exit(0)" || exit 1
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "9200", "--workers", "1"]
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174
apps/fc-ttsreader/speech-align/app.py
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174
apps/fc-ttsreader/speech-align/app.py
Normal file
@@ -0,0 +1,174 @@
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"""FlowerCore speech-align service.
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Wraps SYSTRAN/faster-whisper (https://github.com/SYSTRAN/faster-whisper) in a
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small FastAPI app exposing two endpoints:
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* POST /align — fc-align contract used by FlowerCore.Shared.Speech's
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FasterWhisperAlignmentClient on master. Multipart form
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(`audio`, `language`) returns
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`{text, words: [{word, startSeconds, endSeconds, confidence}],
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durationMs, language}`.
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* POST /transcribe — audio-file-in transcription used by the new TtsReader
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audio-import feature. Multipart form (`audio`, optional
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`language`) returns `{text, language, durationMs,
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segments: [{startSeconds, endSeconds, text}]}` so the
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UI can preview the transcript before piping it into
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Quick Read or saving as a project.
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Both endpoints share the same WhisperModel instance (loaded once at startup).
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Model is pinned by the WHISPER_MODEL env var (defaults to base.en) and cached
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under WHISPER_CACHE_DIR (defaults to /models, backed by a PVC in K8s).
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Health: GET /health → {status: ok, model, device, computeType}.
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"""
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from __future__ import annotations
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import io
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import logging
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import os
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import time
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from contextlib import asynccontextmanager
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from typing import Optional
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from fastapi import FastAPI, File, Form, HTTPException, UploadFile
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from fastapi.responses import JSONResponse
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from faster_whisper import WhisperModel
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LOG = logging.getLogger("speech_align")
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logging.basicConfig(
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level=os.environ.get("LOG_LEVEL", "INFO"),
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format="%(asctime)s %(levelname)s %(name)s %(message)s",
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)
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MODEL_NAME = os.environ.get("WHISPER_MODEL", "Systran/faster-whisper-base.en")
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DEVICE = os.environ.get("WHISPER_DEVICE", "cpu")
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COMPUTE_TYPE = os.environ.get("WHISPER_COMPUTE_TYPE", "int8")
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CACHE_DIR = os.environ.get("WHISPER_CACHE_DIR", "/models")
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MAX_BYTES = int(os.environ.get("MAX_AUDIO_BYTES", str(50 * 1024 * 1024))) # 50 MB
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DEFAULT_LANGUAGE = os.environ.get("DEFAULT_LANGUAGE", "en")
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_state: dict[str, object] = {}
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@asynccontextmanager
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async def lifespan(_app: FastAPI):
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LOG.info("Loading faster-whisper model %s (device=%s compute=%s cache=%s)", MODEL_NAME, DEVICE, COMPUTE_TYPE, CACHE_DIR)
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started = time.time()
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model = WhisperModel(MODEL_NAME, device=DEVICE, compute_type=COMPUTE_TYPE, download_root=CACHE_DIR)
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_state["model"] = model
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LOG.info("Model loaded in %.2fs", time.time() - started)
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yield
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_state.clear()
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app = FastAPI(title="FlowerCore speech-align", version="1.0.0", lifespan=lifespan)
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def _get_model() -> WhisperModel:
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model = _state.get("model")
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if model is None:
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raise HTTPException(status_code=503, detail="Model not loaded yet")
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return model # type: ignore[return-value]
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async def _read_upload(upload: UploadFile) -> bytes:
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payload = await upload.read()
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if not payload:
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raise HTTPException(status_code=400, detail="audio is empty")
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if len(payload) > MAX_BYTES:
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raise HTTPException(
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status_code=413,
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detail=f"audio exceeds {MAX_BYTES} byte limit ({len(payload)} bytes received)",
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)
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return payload
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def _normalize_language(value: Optional[str]) -> Optional[str]:
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if not value or not value.strip():
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return DEFAULT_LANGUAGE
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return value.strip().lower()
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def _transcribe_bytes(audio_bytes: bytes, language: Optional[str], word_timestamps: bool):
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model = _get_model()
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started = time.time()
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segments_iter, info = model.transcribe(
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io.BytesIO(audio_bytes),
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language=language,
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word_timestamps=word_timestamps,
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beam_size=1,
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vad_filter=True,
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)
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segments = list(segments_iter)
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elapsed_ms = int((time.time() - started) * 1000)
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return segments, info, elapsed_ms
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@app.get("/health")
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def health():
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return {
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"status": "ok" if _state.get("model") is not None else "loading",
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"model": MODEL_NAME,
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"device": DEVICE,
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"computeType": COMPUTE_TYPE,
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"defaultLanguage": DEFAULT_LANGUAGE,
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"maxBytes": MAX_BYTES,
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}
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@app.post("/align")
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async def align(audio: UploadFile = File(...), language: str = Form(DEFAULT_LANGUAGE)):
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"""fc-align contract — used by FlowerCore.Shared.Speech.FasterWhisperAlignmentClient."""
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payload = await _read_upload(audio)
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lang = _normalize_language(language)
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segments, info, elapsed_ms = _transcribe_bytes(payload, lang, word_timestamps=True)
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text_parts: list[str] = []
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words: list[dict] = []
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for segment in segments:
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text_parts.append(segment.text.strip())
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for word in (segment.words or []):
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words.append({
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"word": word.word.strip(),
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"startSeconds": float(word.start or 0.0),
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"endSeconds": float(word.end or 0.0),
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"confidence": float(getattr(word, "probability", 0.0) or 0.0),
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})
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duration_ms = int((info.duration or 0.0) * 1000)
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return JSONResponse({
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"text": " ".join(p for p in text_parts if p).strip(),
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"words": words,
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"durationMs": duration_ms,
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"language": info.language or lang,
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"elapsedMs": elapsed_ms,
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})
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@app.post("/transcribe")
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async def transcribe(audio: UploadFile = File(...), language: Optional[str] = Form(None)):
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"""Audio-in transcription contract — used by the new TtsReader audio-import feature.
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Returns full segments (no per-word timestamps) so the UI can preview the
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transcript before piping it into Quick Read or saving as a project.
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"""
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payload = await _read_upload(audio)
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lang = _normalize_language(language)
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segments, info, elapsed_ms = _transcribe_bytes(payload, lang, word_timestamps=False)
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out_segments = [
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{
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"startSeconds": float(segment.start or 0.0),
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"endSeconds": float(segment.end or 0.0),
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"text": segment.text.strip(),
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}
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for segment in segments
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]
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return JSONResponse({
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"text": " ".join(s["text"] for s in out_segments if s["text"]).strip(),
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"segments": out_segments,
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"language": info.language or lang,
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"durationMs": int((info.duration or 0.0) * 1000),
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"elapsedMs": elapsed_ms,
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})
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4
apps/fc-ttsreader/speech-align/requirements.txt
Normal file
4
apps/fc-ttsreader/speech-align/requirements.txt
Normal file
@@ -0,0 +1,4 @@
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faster-whisper==1.0.3
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fastapi==0.115.0
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uvicorn[standard]==0.30.6
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python-multipart==0.0.10
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Reference in New Issue
Block a user