daviestechlabs/kubeflow
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
45996a8dbfa0b12aad2a8f29c1793ff45a4ea32d
kubeflow/dvd_transcription_pipeline.yaml
Billy D. 45996a8dbf feat: add DVD/video transcription pipeline 
5-step KFP pipeline:
1. extract_audio: ffmpeg extracts 16kHz mono WAV from DVD/video
2. chunk_audio: splits into 5-minute segments for Whisper
3. transcribe_chunks: sends each chunk to Whisper STT endpoint
4. format_transcript: produces SRT, VTT, or TXT with timestamps
5. log_metrics: logs run to MLflow (dvd-transcription experiment)
2026-02-13 09:22:56 -05:00

550 lines
26 KiB
YAML
Raw Blame History

# PIPELINE DEFINITION
# Name: dvd-video-transcription
# Description: Extract audio from a DVD or video file, transcribe it via Whisper STT, and produce a timestamped transcript (SRT/VTT/TXT).
# Inputs:
# chunk_duration_s: int [Default: 300.0]
# language: str [Default: 'en']
# mlflow_tracking_uri: str [Default: 'http://mlflow.mlflow.svc.cluster.local:80']
# output_format: str [Default: 'srt']
# source_path: str [Default: '/data/dvd/movie.mkv']
# whisper_url: str [Default: 'http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper']
components:
comp-chunk-audio:
executorLabel: exec-chunk-audio
inputDefinitions:
parameters:
chunk_duration_s:
defaultValue: 300.0
description: Seconds per chunk (default 5 minutes).
isOptional: true
parameterType: NUMBER_INTEGER
wav_path:
description: ' Path to the mono 16 kHz WAV.'
parameterType: STRING
outputDefinitions:
parameters:
chunk_paths:
parameterType: LIST
num_chunks:
parameterType: NUMBER_INTEGER
comp-extract-audio:
executorLabel: exec-extract-audio
inputDefinitions:
parameters:
mono:
defaultValue: true
description: ' Down-mix to mono (Whisper expects single-channel).'
isOptional: true
parameterType: BOOLEAN
sample_rate:
defaultValue: 16000.0
description: Target sample rate (16 kHz is optimal for Whisper).
isOptional: true
parameterType: NUMBER_INTEGER
source_path:
description: 'Path to DVD ISO, VOB, MKV, MP4, or any ffmpeg-
supported file. Can also be a /dev/sr0 device.'
parameterType: STRING
outputDefinitions:
parameters:
duration_s:
parameterType: NUMBER_DOUBLE
wav_path:
parameterType: STRING
comp-format-transcript:
executorLabel: exec-format-transcript
inputDefinitions:
parameters:
full_text:
description: ' Full concatenated text.'
parameterType: STRING
output_format:
defaultValue: srt
description: ' ''srt'', ''vtt'', or ''txt''.'
isOptional: true
parameterType: STRING
segments:
description: ' List of segment dicts with start/end/text.'
parameterType: LIST
total_duration_s:
description: Total audio duration in seconds.
parameterType: NUMBER_DOUBLE
outputDefinitions:
parameters:
output_path:
parameterType: STRING
transcript:
parameterType: STRING
comp-log-transcription-metrics:
executorLabel: exec-log-transcription-metrics
inputDefinitions:
parameters:
experiment_name:
defaultValue: dvd-transcription
isOptional: true
parameterType: STRING
full_text:
parameterType: STRING
mlflow_tracking_uri:
defaultValue: http://mlflow.mlflow.svc.cluster.local:80
isOptional: true
parameterType: STRING
source_path:
parameterType: STRING
total_duration_s:
parameterType: NUMBER_DOUBLE
comp-transcribe-chunks:
executorLabel: exec-transcribe-chunks
inputDefinitions:
parameters:
chunk_paths:
description: ' List of WAV file paths to transcribe.'
parameterType: LIST
language:
defaultValue: en
description: ' Language code for Whisper (None for auto-detect).'
isOptional: true
parameterType: STRING
response_format:
defaultValue: verbose_json
description: '''json'', ''verbose_json'', or ''text''.'
isOptional: true
parameterType: STRING
whisper_url:
defaultValue: http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper
description: ' In-cluster Whisper endpoint URL.'
isOptional: true
parameterType: STRING
outputDefinitions:
parameters:
full_text:
parameterType: STRING
segments:
parameterType: LIST
total_duration_s:
parameterType: NUMBER_DOUBLE
deploymentSpec:
executors:
exec-chunk-audio:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- chunk_audio
command:
- sh
- -c
- "\nif ! [ -x \"$(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\"' && \"\
$0\" \"$@\"\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 chunk_audio(\n wav_path: str,\n chunk_duration_s: int =\
\ 300,\n) -> NamedTuple(\"ChunkOutput\", [(\"chunk_paths\", list), (\"num_chunks\"\
, int)]):\n \"\"\"Split a WAV file into fixed-duration chunks.\n\n \
\ Args:\n wav_path: Path to the mono 16 kHz WAV.\n \
\ chunk_duration_s: Seconds per chunk (default 5 minutes).\n \"\"\"\
\n import os\n import subprocess\n import glob\n\n subprocess.run(\n\
\ [\"apt-get\", \"update\", \"-qq\"],\n check=True, capture_output=True,\n\
\ )\n subprocess.run(\n [\"apt-get\", \"install\", \"-y\",\
\ \"-qq\", \"ffmpeg\"],\n check=True, capture_output=True,\n )\n\
\n out_dir = \"/tmp/dvd_chunks\"\n os.makedirs(out_dir, exist_ok=True)\n\
\ pattern = os.path.join(out_dir, \"chunk_%04d.wav\")\n\n cmd = [\n\
\ \"ffmpeg\", \"-y\",\n \"-i\", wav_path,\n \"-f\"\
, \"segment\",\n \"-segment_time\", str(chunk_duration_s),\n \
\ \"-c\", \"copy\",\n pattern,\n ]\n print(f\"Chunking:\
\ {' '.join(cmd)}\")\n result = subprocess.run(cmd, capture_output=True,\
\ text=True, timeout=3600)\n if result.returncode != 0:\n raise\
\ RuntimeError(f\"ffmpeg chunk failed:\\n{result.stderr}\")\n\n chunks\
\ = sorted(glob.glob(os.path.join(out_dir, \"chunk_*.wav\")))\n print(f\"\
Created {len(chunks)} chunks of ~{chunk_duration_s}s each\")\n\n from\
\ collections import namedtuple\n ChunkOutput = namedtuple(\"ChunkOutput\"\
, [\"chunk_paths\", \"num_chunks\"])\n return ChunkOutput(chunk_paths=chunks,\
\ num_chunks=len(chunks))\n\n"
image: python:3.13-slim
exec-extract-audio:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- extract_audio
command:
- sh
- -c
- "\nif ! [ -x \"$(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 'requests' &&\
\ \"$0\" \"$@\"\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 extract_audio(\n source_path: str,\n sample_rate: int =\
\ 16000,\n mono: bool = True,\n) -> NamedTuple(\"AudioOutput\", [(\"\
wav_path\", str), (\"duration_s\", float)]):\n \"\"\"Extract audio from\
\ a video/DVD file using ffmpeg.\n\n Args:\n source_path: Path\
\ to DVD ISO, VOB, MKV, MP4, or any ffmpeg-\n supported\
\ file. Can also be a /dev/sr0 device.\n sample_rate: Target sample\
\ rate (16 kHz is optimal for Whisper).\n mono: Down-mix to\
\ mono (Whisper expects single-channel).\n \"\"\"\n import os\n \
\ import subprocess\n import json\n\n # Install ffmpeg inside the\
\ container\n subprocess.run(\n [\"apt-get\", \"update\", \"-qq\"\
],\n check=True, capture_output=True,\n )\n subprocess.run(\n\
\ [\"apt-get\", \"install\", \"-y\", \"-qq\", \"ffmpeg\"],\n \
\ check=True, capture_output=True,\n )\n\n out_dir = \"/tmp/dvd_audio\"\
\n os.makedirs(out_dir, exist_ok=True)\n wav_path = os.path.join(out_dir,\
\ \"full_audio.wav\")\n\n # Build ffmpeg command\n cmd = [\"ffmpeg\"\
, \"-y\"]\n\n # Handle DVD ISOs \u2013 mount via concat demuxer or direct\
\ input\n if source_path.lower().endswith(\".iso\"):\n # For ISOs,\
\ ffmpeg can read via dvdread protocol\n cmd += [\"-i\", f\"dvd://{source_path}\"\
]\n else:\n cmd += [\"-i\", source_path]\n\n # Audio extraction\
\ options\n cmd += [\n \"-vn\", # drop\
\ video\n \"-acodec\", \"pcm_s16le\", # 16-bit WAV\n \
\ \"-ar\", str(sample_rate), # resample\n ]\n if mono:\n \
\ cmd += [\"-ac\", \"1\"] # down-mix to mono\n\n cmd +=\
\ [wav_path]\n\n print(f\"Running: {' '.join(cmd)}\")\n result = subprocess.run(cmd,\
\ capture_output=True, text=True, timeout=7200)\n if result.returncode\
\ != 0:\n raise RuntimeError(f\"ffmpeg failed:\\n{result.stderr}\"\
)\n\n # Get duration via ffprobe\n probe = subprocess.run(\n \
\ [\n \"ffprobe\", \"-v\", \"quiet\",\n \"-show_entries\"\
, \"format=duration\",\n \"-of\", \"json\", wav_path,\n \
\ ],\n capture_output=True, text=True,\n )\n duration_s =\
\ float(json.loads(probe.stdout)[\"format\"][\"duration\"])\n file_size_mb\
\ = os.path.getsize(wav_path) / (1024 * 1024)\n print(f\"Extracted: {wav_path}\
\ ({file_size_mb:.1f} MB, {duration_s:.1f}s)\")\n\n from collections\
\ import namedtuple\n AudioOutput = namedtuple(\"AudioOutput\", [\"wav_path\"\
, \"duration_s\"])\n return AudioOutput(wav_path=wav_path, duration_s=duration_s)\n\
\n"
image: python:3.13-slim
exec-format-transcript:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- format_transcript
command:
- sh
- -c
- "\nif ! [ -x \"$(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\"' && \"\
$0\" \"$@\"\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 format_transcript(\n segments: list,\n full_text: str,\n\
\ total_duration_s: float,\n output_format: str = \"srt\",\n) -> NamedTuple(\"\
FormattedOutput\", [(\"transcript\", str), (\"output_path\", str)]):\n \
\ \"\"\"Format the transcript as SRT, VTT, or plain text.\n\n Args:\n\
\ segments: List of segment dicts with start/end/text.\n \
\ full_text: Full concatenated text.\n total_duration_s:\
\ Total audio duration in seconds.\n output_format: 'srt', 'vtt',\
\ or 'txt'.\n \"\"\"\n import os\n\n def _fmt_ts_srt(seconds: float)\
\ -> str:\n h = int(seconds // 3600)\n m = int((seconds %\
\ 3600) // 60)\n s = int(seconds % 60)\n ms = int((seconds\
\ % 1) * 1000)\n return f\"{h:02d}:{m:02d}:{s:02d},{ms:03d}\"\n\n\
\ def _fmt_ts_vtt(seconds: float) -> str:\n h = int(seconds //\
\ 3600)\n m = int((seconds % 3600) // 60)\n s = int(seconds\
\ % 60)\n ms = int((seconds % 1) * 1000)\n return f\"{h:02d}:{m:02d}:{s:02d}.{ms:03d}\"\
\n\n out_dir = \"/tmp/dvd_transcript\"\n os.makedirs(out_dir, exist_ok=True)\n\
\n if output_format == \"srt\":\n lines = []\n for i, seg\
\ in enumerate(segments, 1):\n start_ts = _fmt_ts_srt(seg[\"\
start\"])\n end_ts = _fmt_ts_srt(seg[\"end\"])\n text\
\ = seg.get(\"text\", \"\").strip()\n lines.append(f\"{i}\\n{start_ts}\
\ --> {end_ts}\\n{text}\\n\")\n transcript = \"\\n\".join(lines)\n\
\ ext = \"srt\"\n\n elif output_format == \"vtt\":\n lines\
\ = [\"WEBVTT\\n\"]\n for seg in segments:\n start_ts\
\ = _fmt_ts_vtt(seg[\"start\"])\n end_ts = _fmt_ts_vtt(seg[\"\
end\"])\n text = seg.get(\"text\", \"\").strip()\n \
\ lines.append(f\"{start_ts} --> {end_ts}\\n{text}\\n\")\n transcript\
\ = \"\\n\".join(lines)\n ext = \"vtt\"\n\n else: # txt\n \
\ transcript = full_text\n ext = \"txt\"\n\n out_path = os.path.join(out_dir,\
\ f\"transcript.{ext}\")\n with open(out_path, \"w\", encoding=\"utf-8\"\
) as f:\n f.write(transcript)\n\n h = int(total_duration_s //\
\ 3600)\n m = int((total_duration_s % 3600) // 60)\n print(f\"Transcript\
\ saved: {out_path}\")\n print(f\"Audio duration: {h}h {m}m, Segments:\
\ {len(segments)}\")\n print(f\"Format: {output_format.upper()}, Size:\
\ {len(transcript)} chars\")\n\n from collections import namedtuple\n\
\ FormattedOutput = namedtuple(\"FormattedOutput\", [\"transcript\",\
\ \"output_path\"])\n return FormattedOutput(transcript=transcript, output_path=out_path)\n\
\n"
image: python:3.13-slim
exec-log-transcription-metrics:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- log_transcription_metrics
command:
- sh
- -c
- "\nif ! [ -x \"$(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 'mlflow==2.22.0'\
\ && \"$0\" \"$@\"\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 log_transcription_metrics(\n total_duration_s: float,\n \
\ full_text: str,\n source_path: str,\n mlflow_tracking_uri: str\
\ = \"http://mlflow.mlflow.svc.cluster.local:80\",\n experiment_name:\
\ str = \"dvd-transcription\",\n):\n \"\"\"Log transcription run metrics\
\ to MLflow.\"\"\"\n import mlflow\n\n mlflow.set_tracking_uri(mlflow_tracking_uri)\n\
\ mlflow.set_experiment(experiment_name)\n\n with mlflow.start_run(run_name=f\"\
transcribe-{source_path.split('/')[-1]}\"):\n mlflow.log_params({\n\
\ \"source_path\": source_path,\n \"model\": \"whisper-large-v3\"\
,\n })\n mlflow.log_metrics({\n \"audio_duration_s\"\
: total_duration_s,\n \"transcript_chars\": float(len(full_text)),\n\
\ })\n\n"
image: python:3.13-slim
exec-transcribe-chunks:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- transcribe_chunks
command:
- sh
- -c
- "\nif ! [ -x \"$(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 'requests' &&\
\ \"$0\" \"$@\"\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 transcribe_chunks(\n chunk_paths: list,\n whisper_url:\
\ str = \"http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper\"\
,\n language: str = \"en\",\n response_format: str = \"verbose_json\"\
,\n) -> NamedTuple(\"TranscriptOutput\", [(\"segments\", list), (\"full_text\"\
, str), (\"total_duration_s\", float)]):\n \"\"\"Send each audio chunk\
\ to the Whisper STT endpoint.\n\n Args:\n chunk_paths: List\
\ of WAV file paths to transcribe.\n whisper_url: In-cluster\
\ Whisper endpoint URL.\n language: Language code for Whisper\
\ (None for auto-detect).\n response_format: 'json', 'verbose_json',\
\ or 'text'.\n \"\"\"\n import base64\n import json\n import\
\ time\n import requests\n\n all_segments = []\n full_text_parts\
\ = []\n total_audio_duration = 0.0\n time_offset = 0.0 # cumulative\
\ offset for absolute timestamps\n\n for i, path in enumerate(chunk_paths):\n\
\ print(f\"Transcribing chunk {i + 1}/{len(chunk_paths)}: {path}\"\
)\n\n # Read and base64-encode the chunk\n with open(path,\
\ \"rb\") as f:\n audio_b64 = base64.b64encode(f.read()).decode(\"\
utf-8\")\n\n payload = {\n \"audio\": audio_b64,\n \
\ \"audio_format\": \"wav\",\n \"language\": language,\n\
\ \"task\": \"transcribe\",\n \"response_format\"\
: response_format,\n \"word_timestamps\": False,\n }\n\
\n start = time.time()\n resp = requests.post(whisper_url,\
\ json=payload, timeout=600)\n elapsed = time.time() - start\n \
\ resp.raise_for_status()\n data = resp.json()\n\n chunk_duration\
\ = data.get(\"duration\", 0.0)\n total_audio_duration += chunk_duration\n\
\n if \"segments\" in data:\n for seg in data[\"segments\"\
]:\n # Offset timestamps to be absolute within the full audio\n\
\ seg[\"start\"] += time_offset\n seg[\"end\"\
] += time_offset\n all_segments.append(seg)\n\n chunk_text\
\ = data.get(\"text\", \"\")\n full_text_parts.append(chunk_text)\n\
\ time_offset += chunk_duration\n rtf = elapsed / chunk_duration\
\ if chunk_duration > 0 else 0\n print(f\" \u2192 {len(chunk_text)}\
\ chars, {chunk_duration:.1f}s audio in {elapsed:.1f}s (RTF={rtf:.2f})\"\
)\n\n full_text = \"\\n\".join(full_text_parts)\n print(f\"\\nTotal:\
\ {len(all_segments)} segments, {total_audio_duration:.1f}s audio\")\n \
\ print(f\"Transcript length: {len(full_text)} characters\")\n\n from\
\ collections import namedtuple\n TranscriptOutput = namedtuple(\"TranscriptOutput\"\
, [\"segments\", \"full_text\", \"total_duration_s\"])\n return TranscriptOutput(\n\
\ segments=all_segments,\n full_text=full_text.strip(),\n\
\ total_duration_s=total_audio_duration,\n )\n\n"
image: python:3.13-slim
pipelineInfo:
description: Extract audio from a DVD or video file, transcribe it via Whisper STT,
and produce a timestamped transcript (SRT/VTT/TXT).
name: dvd-video-transcription
root:
dag:
tasks:
chunk-audio:
cachingOptions:
enableCache: true
componentRef:
name: comp-chunk-audio
dependentTasks:
- extract-audio
inputs:
parameters:
chunk_duration_s:
componentInputParameter: chunk_duration_s
wav_path:
taskOutputParameter:
outputParameterKey: wav_path
producerTask: extract-audio
taskInfo:
name: chunk-audio
extract-audio:
cachingOptions:
enableCache: true
componentRef:
name: comp-extract-audio
inputs:
parameters:
mono:
runtimeValue:
constant: true
sample_rate:
runtimeValue:
constant: 16000.0
source_path:
componentInputParameter: source_path
taskInfo:
name: extract-audio
format-transcript:
cachingOptions:
enableCache: true
componentRef:
name: comp-format-transcript
dependentTasks:
- transcribe-chunks
inputs:
parameters:
full_text:
taskOutputParameter:
outputParameterKey: full_text
producerTask: transcribe-chunks
output_format:
componentInputParameter: output_format
segments:
taskOutputParameter:
outputParameterKey: segments
producerTask: transcribe-chunks
total_duration_s:
taskOutputParameter:
outputParameterKey: total_duration_s
producerTask: transcribe-chunks
taskInfo:
name: format-transcript
log-transcription-metrics:
cachingOptions:
enableCache: true
componentRef:
name: comp-log-transcription-metrics
dependentTasks:
- transcribe-chunks
inputs:
parameters:
full_text:
taskOutputParameter:
outputParameterKey: full_text
producerTask: transcribe-chunks
mlflow_tracking_uri:
componentInputParameter: mlflow_tracking_uri
source_path:
componentInputParameter: source_path
total_duration_s:
taskOutputParameter:
outputParameterKey: total_duration_s
producerTask: transcribe-chunks
taskInfo:
name: log-transcription-metrics
transcribe-chunks:
cachingOptions:
enableCache: true
componentRef:
name: comp-transcribe-chunks
dependentTasks:
- chunk-audio
inputs:
parameters:
chunk_paths:
taskOutputParameter:
outputParameterKey: chunk_paths
producerTask: chunk-audio
language:
componentInputParameter: language
response_format:
runtimeValue:
constant: verbose_json
whisper_url:
componentInputParameter: whisper_url
taskInfo:
name: transcribe-chunks
inputDefinitions:
parameters:
chunk_duration_s:
defaultValue: 300.0
isOptional: true
parameterType: NUMBER_INTEGER
language:
defaultValue: en
isOptional: true
parameterType: STRING
mlflow_tracking_uri:
defaultValue: http://mlflow.mlflow.svc.cluster.local:80
isOptional: true
parameterType: STRING
output_format:
defaultValue: srt
isOptional: true
parameterType: STRING
source_path:
defaultValue: /data/dvd/movie.mkv
isOptional: true
parameterType: STRING
whisper_url:
defaultValue: http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper
isOptional: true
parameterType: STRING
schemaVersion: 2.1.0
sdkVersion: kfp-2.12.1
Reference in New Issue View Git Blame Copy Permalink