# PIPELINE DEFINITION
# Name: text-to-speech-pipeline
# Description: Simple text to speech pipeline
# Inputs:
#    text: str
components:
  comp-synthesize-speech:
    executorLabel: exec-synthesize-speech
    inputDefinitions:
      parameters:
        text:
          parameterType: STRING
        tts_url:
          defaultValue: http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/tts
          isOptional: true
          parameterType: STRING
    outputDefinitions:
      parameters:
        audio_b64:
          parameterType: STRING
        latency_s:
          parameterType: NUMBER_DOUBLE
deploymentSpec:
  executors:
    exec-synthesize-speech:
      container:
        args:
        - --executor_input
        - '{{$}}'
        - --function_to_execute
        - synthesize_speech
        command:
        - sh
        - -c
        - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
          \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
          \ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
          \ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"'  &&\
          \  python3 -m pip install --quiet --no-warn-script-location 'httpx' && \"\
          $0\" \"$@\"\n"
        - sh
        - -ec
        - 'program_path=$(mktemp -d)


          printf "%s" "$0" > "$program_path/ephemeral_component.py"

          _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"

          '
        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
          \ *\n\ndef synthesize_speech(\n    text: str,\n    tts_url: str = \"http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/tts\"\
          \n) -> NamedTuple(\"TTSResult\", [(\"audio_b64\", str), (\"latency_s\",\
          \ float)]):\n    \"\"\"Convert text to speech using TTS service.\"\"\"\n\
          \    import base64\n    import time\n    import httpx\n    from collections\
          \ import namedtuple\n\n    start = time.perf_counter()\n    with httpx.Client(timeout=120.0)\
          \ as client:\n        response = client.post(\n            f\"{tts_url}/v1/audio/speech\"\
          ,\n            json={\n                \"input\": text,\n              \
          \  \"voice\": \"en_US-lessac-high\",\n                \"response_format\"\
          : \"wav\"\n            }\n        )\n        audio_b64 = base64.b64encode(response.content).decode(\"\
          utf-8\")\n    latency = time.perf_counter() - start\n\n    TTSResult = namedtuple(\"\
          TTSResult\", [\"audio_b64\", \"latency_s\"])\n    return TTSResult(audio_b64,\
          \ latency)\n\n"
        image: python:3.13-slim
pipelineInfo:
  description: Simple text to speech pipeline
  name: text-to-speech-pipeline
root:
  dag:
    tasks:
      synthesize-speech:
        cachingOptions:
          enableCache: true
        componentRef:
          name: comp-synthesize-speech
        inputs:
          parameters:
            text:
              componentInputParameter: text
        taskInfo:
          name: synthesize-speech
  inputDefinitions:
    parameters:
      text:
        parameterType: STRING
schemaVersion: 2.1.0
sdkVersion: kfp-2.12.1