877 lines
43 KiB
YAML
877 lines
43 KiB
YAML
# PIPELINE DEFINITION
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# Name: voice-cloning-pipeline
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# Description: Extract a speaker from audio+transcript, fine-tune a Coqui VITS voice model, push to Gitea, and log metrics to MLflow.
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# Inputs:
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# base_model: str [Default: 'tts_models/en/ljspeech/vits']
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# batch_size: int [Default: 16.0]
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# gitea_owner: str [Default: 'daviestechlabs']
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# gitea_password: str [Default: '']
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# gitea_repo: str [Default: 'voice-models']
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# gitea_url: str [Default: 'http://gitea-http.gitea.svc.cluster.local:3000']
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# gitea_username: str [Default: '']
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# language: str [Default: 'en']
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# learning_rate: float [Default: 0.0001]
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# max_segment_duration_s: float [Default: 15.0]
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# min_segment_duration_s: float [Default: 1.0]
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# mlflow_tracking_uri: str [Default: 'http://mlflow.mlflow.svc.cluster.local:80']
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# num_epochs: int [Default: 100.0]
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# s3_bucket: str [Default: 'training-data']
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# s3_endpoint: str [Default: 'candlekeep.lab.daviestechlabs.io']
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# s3_key: str [Default: '']
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# target_speaker: str [Default: 'SPEAKER_0']
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# voice_name: str [Default: 'custom-voice']
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# whisper_url: str [Default: 'http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper']
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components:
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comp-extract-speaker-segments:
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executorLabel: exec-extract-speaker-segments
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inputDefinitions:
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parameters:
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audio_path:
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parameterType: STRING
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max_duration_s:
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defaultValue: 15.0
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isOptional: true
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parameterType: NUMBER_DOUBLE
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min_duration_s:
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defaultValue: 1.0
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isOptional: true
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parameterType: NUMBER_DOUBLE
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target_speaker:
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parameterType: STRING
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transcript_json:
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parameterType: STRING
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outputDefinitions:
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parameters:
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num_segments:
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parameterType: NUMBER_INTEGER
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segments_json:
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parameterType: STRING
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total_duration_s:
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parameterType: NUMBER_DOUBLE
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comp-log-training-metrics:
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executorLabel: exec-log-training-metrics
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inputDefinitions:
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parameters:
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batch_size:
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parameterType: NUMBER_INTEGER
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experiment_name:
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defaultValue: voice-cloning
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isOptional: true
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parameterType: STRING
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files_pushed:
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parameterType: NUMBER_INTEGER
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final_loss:
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parameterType: NUMBER_DOUBLE
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learning_rate:
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parameterType: NUMBER_DOUBLE
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mlflow_tracking_uri:
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defaultValue: http://mlflow.mlflow.svc.cluster.local:80
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isOptional: true
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parameterType: STRING
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num_epochs:
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parameterType: NUMBER_INTEGER
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num_segments:
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parameterType: NUMBER_INTEGER
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repo_url:
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parameterType: STRING
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total_duration_s:
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parameterType: NUMBER_DOUBLE
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voice_name:
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parameterType: STRING
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outputDefinitions:
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parameters:
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run_id:
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parameterType: STRING
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comp-prepare-ljspeech-dataset:
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executorLabel: exec-prepare-ljspeech-dataset
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inputDefinitions:
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parameters:
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language:
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defaultValue: en
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isOptional: true
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parameterType: STRING
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segments_json:
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parameterType: STRING
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voice_name:
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parameterType: STRING
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outputDefinitions:
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parameters:
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dataset_dir:
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parameterType: STRING
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num_samples:
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parameterType: NUMBER_INTEGER
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comp-push-model-to-gitea:
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executorLabel: exec-push-model-to-gitea
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inputDefinitions:
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parameters:
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gitea_owner:
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defaultValue: daviestechlabs
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isOptional: true
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parameterType: STRING
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gitea_password:
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defaultValue: ''
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isOptional: true
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parameterType: STRING
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gitea_repo:
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defaultValue: voice-models
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isOptional: true
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parameterType: STRING
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gitea_url:
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defaultValue: http://gitea-http.gitea.svc.cluster.local:3000
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isOptional: true
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parameterType: STRING
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gitea_username:
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defaultValue: ''
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isOptional: true
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parameterType: STRING
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model_dir:
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parameterType: STRING
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voice_name:
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parameterType: STRING
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outputDefinitions:
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parameters:
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files_pushed:
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parameterType: NUMBER_INTEGER
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repo_url:
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parameterType: STRING
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comp-train-vits-voice:
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executorLabel: exec-train-vits-voice
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inputDefinitions:
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parameters:
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base_model:
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defaultValue: tts_models/en/ljspeech/vits
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isOptional: true
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parameterType: STRING
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batch_size:
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defaultValue: 16.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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dataset_dir:
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parameterType: STRING
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language:
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defaultValue: en
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isOptional: true
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parameterType: STRING
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learning_rate:
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defaultValue: 0.0001
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isOptional: true
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parameterType: NUMBER_DOUBLE
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num_epochs:
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defaultValue: 100.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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voice_name:
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parameterType: STRING
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outputDefinitions:
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parameters:
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best_checkpoint:
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parameterType: STRING
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final_loss:
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parameterType: NUMBER_DOUBLE
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model_dir:
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parameterType: STRING
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comp-transcribe-and-diarise:
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executorLabel: exec-transcribe-and-diarise
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inputDefinitions:
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parameters:
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s3_bucket:
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parameterType: STRING
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s3_endpoint:
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parameterType: STRING
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s3_key:
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parameterType: STRING
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whisper_url:
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defaultValue: http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper
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isOptional: true
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parameterType: STRING
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outputDefinitions:
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parameters:
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audio_path:
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parameterType: STRING
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speakers:
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parameterType: STRING
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transcript_json:
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parameterType: STRING
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deploymentSpec:
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executors:
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exec-extract-speaker-segments:
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container:
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args:
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- --executor_input
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- '{{$}}'
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- --function_to_execute
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- extract_speaker_segments
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command:
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- sh
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- -c
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- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
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\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
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\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
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\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' && \"\
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$0\" \"$@\"\n"
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- sh
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- -ec
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- 'program_path=$(mktemp -d)
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printf "%s" "$0" > "$program_path/ephemeral_component.py"
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_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
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'
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- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
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\ *\n\ndef extract_speaker_segments(\n transcript_json: str,\n audio_path:\
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\ str,\n target_speaker: str,\n min_duration_s: float = 1.0,\n \
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\ max_duration_s: float = 15.0,\n) -> NamedTuple(\"SpeakerSegments\", [(\"\
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segments_json\", str), (\"num_segments\", int), (\"total_duration_s\", float)]):\n\
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\ \"\"\"Slice the audio into per-utterance WAV files for the target speaker.\"\
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\"\"\n import json\n import os\n import subprocess\n import\
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\ tempfile\n\n out = NamedTuple(\"SpeakerSegments\", [(\"segments_json\"\
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, str), (\"num_segments\", int), (\"total_duration_s\", float)])\n work\
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\ = tempfile.mkdtemp()\n wavs_dir = os.path.join(work, \"wavs\")\n \
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\ os.makedirs(wavs_dir, exist_ok=True)\n\n # Install ffmpeg\n subprocess.run([\"\
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apt-get\", \"update\", \"-qq\"], capture_output=True)\n subprocess.run([\"\
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apt-get\", \"install\", \"-y\", \"-qq\", \"ffmpeg\"], capture_output=True,\
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\ check=True)\n\n segments = json.loads(transcript_json)\n\n # Filter\
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\ by speaker \u2014 fuzzy match (case-insensitive, partial)\n target_lower\
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\ = target_speaker.lower()\n matched = []\n for seg in segments:\n\
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\ spk = seg.get(\"speaker\", \"\").lower()\n if target_lower\
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\ in spk or spk in target_lower:\n matched.append(seg)\n\n \
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\ # If no speaker labels matched, the user may have given a name\n #\
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\ that doesn't appear. Fall back to using ALL segments.\n if not matched:\n\
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\ print(f\"WARNING: No segments matched speaker '{target_speaker}'.\
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\ \"\n f\"Using all {len(segments)} segments.\")\n matched\
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\ = segments\n\n print(f\"Matched {len(matched)} segments for speaker\
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\ '{target_speaker}'\")\n\n kept = []\n total_dur = 0.0\n for i,\
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\ seg in enumerate(matched):\n start = float(seg.get(\"start\", 0))\n\
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\ end = float(seg.get(\"end\", start + 5))\n duration = end\
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\ - start\n text = seg.get(\"text\", \"\").strip()\n\n if\
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\ duration < min_duration_s or not text:\n continue\n \
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\ if duration > max_duration_s:\n end = start + max_duration_s\n\
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\ duration = max_duration_s\n\n wav_name = f\"utt_{i:04d}.wav\"\
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\n wav_out = os.path.join(wavs_dir, wav_name)\n subprocess.run(\n\
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\ [\"ffmpeg\", \"-y\", \"-i\", audio_path,\n \"-ss\"\
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, str(start), \"-to\", str(end),\n \"-ac\", \"1\", \"-ar\",\
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\ \"22050\", \"-sample_fmt\", \"s16\",\n wav_out],\n \
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\ capture_output=True, check=True,\n )\n\n kept.append({\n\
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\ \"wav\": wav_name,\n \"text\": text,\n \
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\ \"start\": start,\n \"end\": end,\n \"duration\"\
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: round(duration, 2),\n })\n total_dur += duration\n\n \
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\ print(f\"Extracted {len(kept)} utterances, total {total_dur:.1f}s\")\n\
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\n return out(\n segments_json=json.dumps({\"wavs_dir\": wavs_dir,\
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\ \"utterances\": kept}),\n num_segments=len(kept),\n total_duration_s=round(total_dur,\
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\ 2),\n )\n\n"
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image: python:3.13-slim
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exec-log-training-metrics:
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container:
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args:
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- --executor_input
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- '{{$}}'
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- --function_to_execute
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- log_training_metrics
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command:
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- sh
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- -c
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- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
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\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
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\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
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\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
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\ python3 -m pip install --quiet --no-warn-script-location 'mlflow>=2.10.0'\
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\ 'requests' && \"$0\" \"$@\"\n"
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- sh
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- -ec
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- 'program_path=$(mktemp -d)
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printf "%s" "$0" > "$program_path/ephemeral_component.py"
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_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
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'
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- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
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\ *\n\ndef log_training_metrics(\n voice_name: str,\n num_segments:\
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\ int,\n total_duration_s: float,\n final_loss: float,\n num_epochs:\
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\ int,\n batch_size: int,\n learning_rate: float,\n repo_url: str,\n\
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\ files_pushed: int,\n mlflow_tracking_uri: str = \"http://mlflow.mlflow.svc.cluster.local:80\"\
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,\n experiment_name: str = \"voice-cloning\",\n) -> NamedTuple(\"LogOutput\"\
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, [(\"run_id\", str)]):\n \"\"\"Log training run to MLflow.\"\"\"\n \
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\ import mlflow\n from datetime import datetime\n\n out = NamedTuple(\"\
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LogOutput\", [(\"run_id\", str)])\n\n mlflow.set_tracking_uri(mlflow_tracking_uri)\n\
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\ mlflow.set_experiment(experiment_name)\n\n with mlflow.start_run(run_name=f\"\
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voice-clone-{voice_name}-{datetime.now():%Y%m%d-%H%M}\") as run:\n \
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\ mlflow.log_params({\n \"voice_name\": voice_name,\n \
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\ \"base_model\": \"tts_models/en/ljspeech/vits\",\n \"\
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model_type\": \"coqui-vits\",\n \"num_epochs\": num_epochs,\n\
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\ \"batch_size\": batch_size,\n \"learning_rate\"\
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: learning_rate,\n \"sample_rate\": 22050,\n })\n \
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\ mlflow.log_metrics({\n \"num_training_segments\": num_segments,\n\
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\ \"total_audio_duration_s\": total_duration_s,\n \
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\ \"final_loss\": final_loss,\n \"files_pushed\": files_pushed,\n\
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\ })\n mlflow.set_tags({\n \"pipeline\": \"voice-cloning\"\
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,\n \"gitea_repo\": repo_url,\n \"voice_name\": voice_name,\n\
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\ })\n print(f\"Logged to MLflow run: {run.info.run_id}\"\
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)\n return out(run_id=run.info.run_id)\n\n"
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image: python:3.13-slim
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exec-prepare-ljspeech-dataset:
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container:
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args:
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- --executor_input
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- '{{$}}'
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- --function_to_execute
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- prepare_ljspeech_dataset
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command:
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- sh
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- -c
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- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
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\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
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\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
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\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' && \"\
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$0\" \"$@\"\n"
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- sh
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- -ec
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- 'program_path=$(mktemp -d)
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printf "%s" "$0" > "$program_path/ephemeral_component.py"
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_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
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'
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- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
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\ *\n\ndef prepare_ljspeech_dataset(\n segments_json: str,\n voice_name:\
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\ str,\n language: str = \"en\",\n) -> NamedTuple(\"DatasetOutput\",\
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\ [(\"dataset_dir\", str), (\"num_samples\", int)]):\n \"\"\"Create metadata.csv\
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\ + wavs/ in LJSpeech format.\"\"\"\n import json\n import os\n \
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\ import shutil\n\n out = NamedTuple(\"DatasetOutput\", [(\"dataset_dir\"\
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, str), (\"num_samples\", int)])\n\n data = json.loads(segments_json)\n\
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\ wavs_src = data[\"wavs_dir\"]\n utterances = data[\"utterances\"\
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]\n\n dataset_dir = os.path.join(os.path.dirname(wavs_src), \"dataset\"\
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)\n wavs_dst = os.path.join(dataset_dir, \"wavs\")\n os.makedirs(wavs_dst,\
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\ exist_ok=True)\n\n lines = []\n for utt in utterances:\n \
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\ src = os.path.join(wavs_src, utt[\"wav\"])\n dst = os.path.join(wavs_dst,\
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\ utt[\"wav\"])\n shutil.copy2(src, dst)\n stem = os.path.splitext(utt[\"\
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wav\"])[0]\n # LJSpeech format: id|text|text\n text = utt[\"\
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text\"].replace(\"|\", \" \")\n lines.append(f\"{stem}|{text}|{text}\"\
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)\n\n metadata_path = os.path.join(dataset_dir, \"metadata.csv\")\n \
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\ with open(metadata_path, \"w\", encoding=\"utf-8\") as f:\n f.write(\"\
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\\n\".join(lines))\n\n # Dataset config for reference\n import json\
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\ as _json\n config = {\n \"name\": voice_name,\n \"language\"\
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: language,\n \"num_samples\": len(lines),\n \"format\": \"\
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ljspeech\",\n \"sample_rate\": 22050,\n }\n with open(os.path.join(dataset_dir,\
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\ \"dataset_config.json\"), \"w\") as f:\n _json.dump(config, f,\
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\ indent=2)\n\n print(f\"LJSpeech dataset ready: {len(lines)} samples\"\
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)\n return out(dataset_dir=dataset_dir, num_samples=len(lines))\n\n"
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image: python:3.13-slim
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exec-push-model-to-gitea:
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container:
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args:
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- --executor_input
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- '{{$}}'
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- --function_to_execute
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- push_model_to_gitea
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command:
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- sh
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- -c
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- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
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\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
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\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
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\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
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\ python3 -m pip install --quiet --no-warn-script-location 'requests' &&\
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\ \"$0\" \"$@\"\n"
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- sh
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- -ec
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|
- 'program_path=$(mktemp -d)
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printf "%s" "$0" > "$program_path/ephemeral_component.py"
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_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
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'
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- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
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\ *\n\ndef push_model_to_gitea(\n model_dir: str,\n voice_name: str,\n\
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\ gitea_url: str = \"http://gitea-http.gitea.svc.cluster.local:3000\"\
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,\n gitea_owner: str = \"daviestechlabs\",\n gitea_repo: str = \"\
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voice-models\",\n gitea_username: str = \"\",\n gitea_password: str\
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\ = \"\",\n) -> NamedTuple(\"PushOutput\", [(\"repo_url\", str), (\"files_pushed\"\
|
|
, int)]):\n \"\"\"Package and push the trained model to a Gitea repository.\"\
|
|
\"\"\n import base64\n import glob\n import json\n import os\n\
|
|
\ import requests\n\n out = NamedTuple(\"PushOutput\", [(\"repo_url\"\
|
|
, str), (\"files_pushed\", int)])\n session = requests.Session()\n \
|
|
\ session.auth = (gitea_username, gitea_password) if gitea_username else\
|
|
\ None\n\n api = f\"{gitea_url}/api/v1\"\n repo_url = f\"{gitea_url}/{gitea_owner}/{gitea_repo}\"\
|
|
\n\n # \u2500\u2500 Ensure repo exists \u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n r = session.get(f\"{api}/repos/{gitea_owner}/{gitea_repo}\"\
|
|
, timeout=30)\n if r.status_code == 404:\n print(f\"Creating repository:\
|
|
\ {gitea_owner}/{gitea_repo}\")\n r = session.post(\n \
|
|
\ f\"{api}/orgs/{gitea_owner}/repos\",\n json={\n \
|
|
\ \"name\": gitea_repo,\n \"description\": \"Trained\
|
|
\ voice models from voice cloning pipeline\",\n \"private\"\
|
|
: False,\n \"auto_init\": True,\n },\n \
|
|
\ timeout=30,\n )\n if r.status_code not in (200, 201):\n\
|
|
\ r = session.post(\n f\"{api}/user/repos\",\n\
|
|
\ json={\"name\": gitea_repo, \"description\": \"Trained\
|
|
\ voice models\", \"auto_init\": True},\n timeout=30,\n \
|
|
\ )\n r.raise_for_status()\n print(\"Repository\
|
|
\ created\")\n\n # \u2500\u2500 Collect model files \u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n files_to_push = []\n\
|
|
\n # Best model checkpoint\n for pattern in [\"**/best_model*.pth\"\
|
|
, \"**/*.pth\"]:\n found = glob.glob(os.path.join(model_dir, pattern),\
|
|
\ recursive=True)\n if found:\n found.sort(key=os.path.getmtime,\
|
|
\ reverse=True)\n files_to_push.append(found[0])\n \
|
|
\ break\n\n # Config\n for pattern in [\"**/config.json\"]:\n \
|
|
\ found = glob.glob(os.path.join(model_dir, pattern), recursive=True)\n\
|
|
\ if found:\n files_to_push.append(found[0])\n\n #\
|
|
\ Model info\n model_info = {\n \"name\": voice_name,\n \
|
|
\ \"type\": \"coqui-vits\",\n \"base_model\": \"tts_models/en/ljspeech/vits\"\
|
|
,\n \"sample_rate\": 22050,\n }\n info_path = os.path.join(model_dir,\
|
|
\ \"model_info.json\")\n with open(info_path, \"w\") as f:\n json.dump(model_info,\
|
|
\ f, indent=2)\n files_to_push.append(info_path)\n\n # \u2500\u2500\
|
|
\ Push each file \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\n pushed = 0\n for fpath in files_to_push:\n\
|
|
\ rel = os.path.relpath(fpath, model_dir)\n gitea_path = f\"\
|
|
{voice_name}/{rel}\"\n print(f\"Pushing: {gitea_path} ({os.path.getsize(fpath)}\
|
|
\ bytes)\")\n\n with open(fpath, \"rb\") as f:\n content_b64\
|
|
\ = base64.b64encode(f.read()).decode()\n\n # Check if file exists\n\
|
|
\ r = session.get(\n f\"{api}/repos/{gitea_owner}/{gitea_repo}/contents/{gitea_path}\"\
|
|
,\n timeout=30,\n )\n\n payload = {\n \
|
|
\ \"content\": content_b64,\n \"message\": f\"Upload {voice_name}:\
|
|
\ {rel}\",\n }\n\n if r.status_code == 200:\n sha\
|
|
\ = r.json().get(\"sha\", \"\")\n payload[\"sha\"] = sha\n \
|
|
\ r = session.put(\n f\"{api}/repos/{gitea_owner}/{gitea_repo}/contents/{gitea_path}\"\
|
|
,\n json=payload, timeout=120,\n )\n else:\n\
|
|
\ r = session.post(\n f\"{api}/repos/{gitea_owner}/{gitea_repo}/contents/{gitea_path}\"\
|
|
,\n json=payload, timeout=120,\n )\n\n \
|
|
\ if r.status_code in (200, 201):\n pushed += 1\n \
|
|
\ print(f\" \u2713 Pushed\")\n else:\n print(f\" \u2717\
|
|
\ Failed ({r.status_code}): {r.text[:200]}\")\n\n print(f\"\\nPushed\
|
|
\ {pushed}/{len(files_to_push)} files to {repo_url}\")\n return out(repo_url=repo_url,\
|
|
\ files_pushed=pushed)\n\n"
|
|
image: python:3.13-slim
|
|
exec-train-vits-voice:
|
|
container:
|
|
args:
|
|
- --executor_input
|
|
- '{{$}}'
|
|
- --function_to_execute
|
|
- train_vits_voice
|
|
command:
|
|
- sh
|
|
- -c
|
|
- "\nif ! [ -x \"$(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 train_vits_voice(\n dataset_dir: str,\n voice_name: str,\n\
|
|
\ language: str = \"en\",\n base_model: str = \"tts_models/en/ljspeech/vits\"\
|
|
,\n num_epochs: int = 100,\n batch_size: int = 16,\n learning_rate:\
|
|
\ float = 0.0001,\n) -> NamedTuple(\"TrainOutput\", [(\"model_dir\", str),\
|
|
\ (\"best_checkpoint\", str), (\"final_loss\", float)]):\n \"\"\"Fine-tune\
|
|
\ a VITS model on the speaker dataset.\"\"\"\n import os\n import\
|
|
\ json\n import glob\n\n out = NamedTuple(\"TrainOutput\", [(\"model_dir\"\
|
|
, str), (\"best_checkpoint\", str), (\"final_loss\", float)])\n\n OUTPUT_DIR\
|
|
\ = \"/tmp/vits_output\"\n os.makedirs(OUTPUT_DIR, exist_ok=True)\n\n\
|
|
\ print(f\"=== Coqui VITS Voice Training ===\")\n print(f\"Voice name\
|
|
\ : {voice_name}\")\n print(f\"Base model : {base_model}\")\n print(f\"\
|
|
Dataset : {dataset_dir}\")\n print(f\"Epochs : {num_epochs}\"\
|
|
)\n print(f\"Batch size : {batch_size}\")\n print(f\"LR :\
|
|
\ {learning_rate}\")\n\n # \u2500\u2500 Download base model checkpoint\
|
|
\ \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\n restore_path = None\n if base_model and base_model != \"none\"\
|
|
:\n from TTS.utils.manage import ModelManager\n manager =\
|
|
\ ModelManager()\n model_path, config_path, _ = manager.download_model(base_model)\n\
|
|
\ restore_path = model_path\n print(f\"Base model checkpoint:\
|
|
\ {restore_path}\")\n\n # \u2500\u2500 Configure and train \u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n from trainer\
|
|
\ import Trainer, TrainerArgs\n from TTS.tts.configs.vits_config import\
|
|
\ VitsConfig\n from TTS.tts.configs.shared_configs import BaseDatasetConfig\n\
|
|
\ from TTS.tts.datasets import load_tts_samples\n from TTS.tts.models.vits\
|
|
\ import Vits\n from TTS.tts.utils.text.tokenizer import TTSTokenizer\n\
|
|
\ from TTS.utils.audio import AudioProcessor\n\n dataset_config =\
|
|
\ BaseDatasetConfig(\n formatter=\"ljspeech\",\n meta_file_train=\"\
|
|
metadata.csv\",\n path=dataset_dir,\n language=language,\n\
|
|
\ )\n\n config = VitsConfig(\n run_name=voice_name,\n \
|
|
\ output_path=OUTPUT_DIR,\n datasets=[dataset_config],\n \
|
|
\ batch_size=batch_size,\n eval_batch_size=max(1, batch_size //\
|
|
\ 2),\n num_loader_workers=4,\n num_eval_loader_workers=2,\n\
|
|
\ run_eval=True,\n test_delay_epochs=5,\n epochs=num_epochs,\n\
|
|
\ text_cleaner=\"phoneme_cleaners\",\n use_phonemes=True,\n\
|
|
\ phoneme_language=language,\n phoneme_cache_path=os.path.join(OUTPUT_DIR,\
|
|
\ \"phoneme_cache\"),\n compute_input_seq_cache=True,\n print_step=25,\n\
|
|
\ print_eval=False,\n mixed_precision=True,\n save_step=500,\n\
|
|
\ save_n_checkpoints=3,\n save_best_after=1000,\n lr=learning_rate,\n\
|
|
\ audio={\n \"sample_rate\": 22050,\n \"resample\"\
|
|
: True,\n \"do_trim_silence\": True,\n \"trim_db\"\
|
|
: 45,\n },\n )\n\n ap = AudioProcessor.init_from_config(config)\n\
|
|
\ tokenizer, config = TTSTokenizer.init_from_config(config)\n\n train_samples,\
|
|
\ eval_samples = load_tts_samples(\n dataset_config,\n eval_split=True,\n\
|
|
\ eval_split_max_size=config.eval_split_max_size,\n eval_split_size=config.eval_split_size,\n\
|
|
\ )\n print(f\"Training samples: {len(train_samples)}\")\n print(f\"\
|
|
Eval samples: {len(eval_samples)}\")\n\n model = Vits(config, ap,\
|
|
\ tokenizer, speaker_manager=None)\n\n trainer_args = TrainerArgs(\n\
|
|
\ restore_path=restore_path,\n skip_train_epoch=False,\n \
|
|
\ )\n\n trainer = Trainer(\n trainer_args,\n config,\n\
|
|
\ output_path=OUTPUT_DIR,\n model=model,\n train_samples=train_samples,\n\
|
|
\ eval_samples=eval_samples,\n )\n\n trainer.fit()\n\n #\
|
|
\ \u2500\u2500 Find best checkpoint \u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\n best_files = glob.glob(os.path.join(OUTPUT_DIR,\
|
|
\ \"**/best_model*.pth\"), recursive=True)\n if not best_files:\n \
|
|
\ best_files = glob.glob(os.path.join(OUTPUT_DIR, \"**/*.pth\"), recursive=True)\n\
|
|
\ best_files.sort(key=os.path.getmtime, reverse=True)\n best_checkpoint\
|
|
\ = best_files[0] if best_files else \"\"\n\n # Try to read final loss\
|
|
\ from trainer\n final_loss = 0.0\n try:\n final_loss = float(trainer.keep_avg_train[\"\
|
|
avg_loss\"])\n except Exception:\n pass\n\n print(f\"Training\
|
|
\ complete. Best checkpoint: {best_checkpoint}\")\n print(f\"Final loss:\
|
|
\ {final_loss:.4f}\")\n\n return out(model_dir=OUTPUT_DIR, best_checkpoint=best_checkpoint,\
|
|
\ final_loss=final_loss)\n\n"
|
|
image: ghcr.io/coqui-ai/tts:latest
|
|
resources:
|
|
accelerator:
|
|
resourceCount: '1'
|
|
resourceType: gpu
|
|
resourceCpuLimit: '8'
|
|
resourceCpuRequest: '4'
|
|
resourceMemoryLimit: 32Gi
|
|
resourceMemoryRequest: 16Gi
|
|
exec-transcribe-and-diarise:
|
|
container:
|
|
args:
|
|
- --executor_input
|
|
- '{{$}}'
|
|
- --function_to_execute
|
|
- transcribe_and_diarise
|
|
command:
|
|
- sh
|
|
- -c
|
|
- "\nif ! [ -x \"$(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' 'boto3'\
|
|
\ && \"$0\" \"$@\"\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_and_diarise(\n s3_endpoint: str,\n s3_bucket:\
|
|
\ str,\n s3_key: str,\n whisper_url: str = \"http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper\"\
|
|
,\n) -> NamedTuple(\"TranscriptOutput\", [(\"transcript_json\", str), (\"\
|
|
speakers\", str), (\"audio_path\", str)]):\n \"\"\"Download audio from\
|
|
\ Quobjects S3, transcribe via Whisper with timestamps.\"\"\"\n import\
|
|
\ json\n import os\n import subprocess\n import tempfile\n import\
|
|
\ base64\n import boto3\n import requests\n\n out = NamedTuple(\"\
|
|
TranscriptOutput\", [(\"transcript_json\", str), (\"speakers\", str), (\"\
|
|
audio_path\", str)])\n work = tempfile.mkdtemp()\n\n # \u2500\u2500\
|
|
\ Download audio from Quobjects S3 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\n ext = os.path.splitext(s3_key)[-1] or \".wav\"\n audio_path\
|
|
\ = os.path.join(work, f\"audio_raw{ext}\")\n\n client = boto3.client(\n\
|
|
\ \"s3\",\n endpoint_url=f\"http://{s3_endpoint}\",\n \
|
|
\ aws_access_key_id=\"\",\n aws_secret_access_key=\"\",\n \
|
|
\ config=boto3.session.Config(signature_version=\"UNSIGNED\"),\n )\n\
|
|
\ print(f\"Downloading s3://{s3_bucket}/{s3_key} from {s3_endpoint}\"\
|
|
)\n client.download_file(s3_bucket, s3_key, audio_path)\n print(f\"\
|
|
Downloaded {os.path.getsize(audio_path)} bytes\")\n\n # \u2500\u2500\
|
|
\ Normalise to 16 kHz mono WAV \u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\n wav_path = os.path.join(work,\
|
|
\ \"audio.wav\")\n subprocess.run(\n [\"apt-get\", \"update\"\
|
|
, \"-qq\"],\n capture_output=True,\n )\n subprocess.run(\n\
|
|
\ [\"apt-get\", \"install\", \"-y\", \"-qq\", \"ffmpeg\"],\n \
|
|
\ capture_output=True, check=True,\n )\n subprocess.run(\n \
|
|
\ [\"ffmpeg\", \"-y\", \"-i\", audio_path, \"-ac\", \"1\",\n \
|
|
\ \"-ar\", \"16000\", \"-sample_fmt\", \"s16\", wav_path],\n capture_output=True,\
|
|
\ check=True,\n )\n\n # \u2500\u2500 Send to Whisper for timestamped\
|
|
\ transcription \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n\
|
|
\ with open(wav_path, \"rb\") as f:\n audio_b64 = base64.b64encode(f.read()).decode()\n\
|
|
\n payload = {\n \"audio\": audio_b64,\n \"response_format\"\
|
|
: \"verbose_json\",\n \"timestamp_granularities\": [\"segment\"],\n\
|
|
\ }\n resp = requests.post(whisper_url, json=payload, timeout=600)\n\
|
|
\ resp.raise_for_status()\n result = resp.json()\n\n segments =\
|
|
\ result.get(\"segments\", [])\n print(f\"Whisper returned {len(segments)}\
|
|
\ segments\")\n\n # \u2500\u2500 Group segments by speaker if diarisation\
|
|
\ is present \u2500\u2500\u2500\n # Whisper may not diarise, but we still\
|
|
\ produce segments with\n # start/end timestamps that the next step can\
|
|
\ use.\n speakers = set()\n for i, seg in enumerate(segments):\n \
|
|
\ spk = seg.get(\"speaker\", f\"SPEAKER_{i // 10}\")\n seg[\"\
|
|
speaker\"] = spk\n speakers.add(spk)\n\n speakers_list = sorted(speakers)\n\
|
|
\ print(f\"Detected speakers: {speakers_list}\")\n\n return out(\n\
|
|
\ transcript_json=json.dumps(segments),\n speakers=json.dumps(speakers_list),\n\
|
|
\ audio_path=wav_path,\n )\n\n"
|
|
image: python:3.13-slim
|
|
pipelineInfo:
|
|
description: Extract a speaker from audio+transcript, fine-tune a Coqui VITS voice
|
|
model, push to Gitea, and log metrics to MLflow.
|
|
name: voice-cloning-pipeline
|
|
root:
|
|
dag:
|
|
tasks:
|
|
extract-speaker-segments:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-extract-speaker-segments
|
|
dependentTasks:
|
|
- transcribe-and-diarise
|
|
inputs:
|
|
parameters:
|
|
audio_path:
|
|
taskOutputParameter:
|
|
outputParameterKey: audio_path
|
|
producerTask: transcribe-and-diarise
|
|
max_duration_s:
|
|
componentInputParameter: max_segment_duration_s
|
|
min_duration_s:
|
|
componentInputParameter: min_segment_duration_s
|
|
target_speaker:
|
|
componentInputParameter: target_speaker
|
|
transcript_json:
|
|
taskOutputParameter:
|
|
outputParameterKey: transcript_json
|
|
producerTask: transcribe-and-diarise
|
|
taskInfo:
|
|
name: extract-speaker-segments
|
|
log-training-metrics:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-log-training-metrics
|
|
dependentTasks:
|
|
- extract-speaker-segments
|
|
- push-model-to-gitea
|
|
- train-vits-voice
|
|
inputs:
|
|
parameters:
|
|
batch_size:
|
|
componentInputParameter: batch_size
|
|
files_pushed:
|
|
taskOutputParameter:
|
|
outputParameterKey: files_pushed
|
|
producerTask: push-model-to-gitea
|
|
final_loss:
|
|
taskOutputParameter:
|
|
outputParameterKey: final_loss
|
|
producerTask: train-vits-voice
|
|
learning_rate:
|
|
componentInputParameter: learning_rate
|
|
mlflow_tracking_uri:
|
|
componentInputParameter: mlflow_tracking_uri
|
|
num_epochs:
|
|
componentInputParameter: num_epochs
|
|
num_segments:
|
|
taskOutputParameter:
|
|
outputParameterKey: num_segments
|
|
producerTask: extract-speaker-segments
|
|
repo_url:
|
|
taskOutputParameter:
|
|
outputParameterKey: repo_url
|
|
producerTask: push-model-to-gitea
|
|
total_duration_s:
|
|
taskOutputParameter:
|
|
outputParameterKey: total_duration_s
|
|
producerTask: extract-speaker-segments
|
|
voice_name:
|
|
componentInputParameter: voice_name
|
|
taskInfo:
|
|
name: log-training-metrics
|
|
prepare-ljspeech-dataset:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-prepare-ljspeech-dataset
|
|
dependentTasks:
|
|
- extract-speaker-segments
|
|
inputs:
|
|
parameters:
|
|
language:
|
|
componentInputParameter: language
|
|
segments_json:
|
|
taskOutputParameter:
|
|
outputParameterKey: segments_json
|
|
producerTask: extract-speaker-segments
|
|
voice_name:
|
|
componentInputParameter: voice_name
|
|
taskInfo:
|
|
name: prepare-ljspeech-dataset
|
|
push-model-to-gitea:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-push-model-to-gitea
|
|
dependentTasks:
|
|
- train-vits-voice
|
|
inputs:
|
|
parameters:
|
|
gitea_owner:
|
|
componentInputParameter: gitea_owner
|
|
gitea_password:
|
|
componentInputParameter: gitea_password
|
|
gitea_repo:
|
|
componentInputParameter: gitea_repo
|
|
gitea_url:
|
|
componentInputParameter: gitea_url
|
|
gitea_username:
|
|
componentInputParameter: gitea_username
|
|
model_dir:
|
|
taskOutputParameter:
|
|
outputParameterKey: model_dir
|
|
producerTask: train-vits-voice
|
|
voice_name:
|
|
componentInputParameter: voice_name
|
|
taskInfo:
|
|
name: push-model-to-gitea
|
|
train-vits-voice:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-train-vits-voice
|
|
dependentTasks:
|
|
- prepare-ljspeech-dataset
|
|
inputs:
|
|
parameters:
|
|
base_model:
|
|
componentInputParameter: base_model
|
|
batch_size:
|
|
componentInputParameter: batch_size
|
|
dataset_dir:
|
|
taskOutputParameter:
|
|
outputParameterKey: dataset_dir
|
|
producerTask: prepare-ljspeech-dataset
|
|
language:
|
|
componentInputParameter: language
|
|
learning_rate:
|
|
componentInputParameter: learning_rate
|
|
num_epochs:
|
|
componentInputParameter: num_epochs
|
|
voice_name:
|
|
componentInputParameter: voice_name
|
|
taskInfo:
|
|
name: train-vits-voice
|
|
transcribe-and-diarise:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-transcribe-and-diarise
|
|
inputs:
|
|
parameters:
|
|
s3_bucket:
|
|
componentInputParameter: s3_bucket
|
|
s3_endpoint:
|
|
componentInputParameter: s3_endpoint
|
|
s3_key:
|
|
componentInputParameter: s3_key
|
|
whisper_url:
|
|
componentInputParameter: whisper_url
|
|
taskInfo:
|
|
name: transcribe-and-diarise
|
|
inputDefinitions:
|
|
parameters:
|
|
base_model:
|
|
defaultValue: tts_models/en/ljspeech/vits
|
|
isOptional: true
|
|
parameterType: STRING
|
|
batch_size:
|
|
defaultValue: 16.0
|
|
isOptional: true
|
|
parameterType: NUMBER_INTEGER
|
|
gitea_owner:
|
|
defaultValue: daviestechlabs
|
|
isOptional: true
|
|
parameterType: STRING
|
|
gitea_password:
|
|
defaultValue: ''
|
|
isOptional: true
|
|
parameterType: STRING
|
|
gitea_repo:
|
|
defaultValue: voice-models
|
|
isOptional: true
|
|
parameterType: STRING
|
|
gitea_url:
|
|
defaultValue: http://gitea-http.gitea.svc.cluster.local:3000
|
|
isOptional: true
|
|
parameterType: STRING
|
|
gitea_username:
|
|
defaultValue: ''
|
|
isOptional: true
|
|
parameterType: STRING
|
|
language:
|
|
defaultValue: en
|
|
isOptional: true
|
|
parameterType: STRING
|
|
learning_rate:
|
|
defaultValue: 0.0001
|
|
isOptional: true
|
|
parameterType: NUMBER_DOUBLE
|
|
max_segment_duration_s:
|
|
defaultValue: 15.0
|
|
isOptional: true
|
|
parameterType: NUMBER_DOUBLE
|
|
min_segment_duration_s:
|
|
defaultValue: 1.0
|
|
isOptional: true
|
|
parameterType: NUMBER_DOUBLE
|
|
mlflow_tracking_uri:
|
|
defaultValue: http://mlflow.mlflow.svc.cluster.local:80
|
|
isOptional: true
|
|
parameterType: STRING
|
|
num_epochs:
|
|
defaultValue: 100.0
|
|
isOptional: true
|
|
parameterType: NUMBER_INTEGER
|
|
s3_bucket:
|
|
defaultValue: training-data
|
|
isOptional: true
|
|
parameterType: STRING
|
|
s3_endpoint:
|
|
defaultValue: candlekeep.lab.daviestechlabs.io
|
|
isOptional: true
|
|
parameterType: STRING
|
|
s3_key:
|
|
defaultValue: ''
|
|
isOptional: true
|
|
parameterType: STRING
|
|
target_speaker:
|
|
defaultValue: SPEAKER_0
|
|
isOptional: true
|
|
parameterType: STRING
|
|
voice_name:
|
|
defaultValue: custom-voice
|
|
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
|