Feat/mlflow models from code migration audio translation with nemo

[gabisponciano]

MLflow 3.1.0 Models-from-Code Migration for Audio Translation with NeMo Blueprint

Overview

Successfully migrated the Audio Translation with NeMo blueprint from MLflow's legacy serialization-based model logging (python_model) to the modern models-from-code approach (loader_module + data_path). This comprehensive architectural refactoring resolves critical MLflow 3.1.0 compatibility issues and adopts the universal structure standardization that synchronizes shared loader/logger implementation with the canonical Vanilla-RAG blueprint (PR #208).

🎯 LATEST UPDATE: Applied universal structure standardization following PR #208 pattern with generic class names (Model, Logger) and synchronized shared loader/logger implementation, eliminating blueprint-specific prefixes for better maintainability across all AI blueprints.

Technical Changes

Universal Structure Standardization ✨

File Structure Migration

BEFORE (Legacy Structure):
core/audio_translation_service/
├── audio_translation_model.py      # Class: AudioTranslationModel
├── audio_translation_loader.py     # References: audio_translation_model, AudioTranslationModel  
└── audio_translation_service.py    # Class: AudioTranslationService

AFTER (Universal Structure):
src/mlflow/
├── __init__.py          # Exports: Model, Logger
├── model.py             # Class: Model (was AudioTranslationModel)
├── loader.py            # References: src.mlflow.model, Model (exact copy from PR #208)
└── logger.py            # Class: Logger (exact copy from PR #208)

Generic Class Names & Synchronized Implementation

• Model (formerly AudioTranslationModel): Framework-agnostic NeMo audio translation business logic layer
• Logger (exact copy from PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208): Universal MLflow registration and artifact management layer
• loader.py (exact copy from PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208): Universal MLflow models-from-code entry point
• Universal imports: from src.mlflow import Model, Logger
• Standardized loader module: loader_module="src.mlflow.loader"

New Architecture Components

loader.py (Synchronized with PR #208)

• Source: Byte-exact copy from vanilla-rag blueprint PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208
• Purpose: Universal MLflow models-from-code entry point implementing _load_pyfunc() function
• Functionality:
  • Loads configuration, secrets, documents, and optional model files from MLflow artifacts
  • Handles proper artifact directory structure validation
  • Returns initialized Model instance for prediction
• Integration: Called automatically by MLflow during model loading and deployment

model.py (Refactored for Universal Pattern + NeMo Specialization)

• Purpose: Standalone business logic layer with zero MLflow dependencies
• Architecture: Framework-agnostic NeMo audio translation model class designed for PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 compatibility
• Constructor Updated: Now follows PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 pattern: Model(config, docs_path, model_path, secrets)
• NeMo-Specific Features:
  • End-to-end audio translation pipeline: Speech-to-Text (Citrinet) → Machine Translation (MarianMT) → Text-to-Speech (FastPitch + HiFiGAN)
  • Multi-modal processing: Handles both audio files and text inputs with base64 audio serialization
  • GPU-accelerated inference: CUDA optimization for all NeMo and Transformers models
  • ONNX export capabilities: Built-in support for converting NeMo models to ONNX format
  • Artifact management: Intelligent NeMo model resolution from MLflow artifacts or configuration
• API Compatibility: Maintains identical predict(model_input, params) signature for backward compatibility

Refactored Service Architecture

logger.py (Synchronized with PR #208)

• Source: Byte-exact copy from vanilla-rag blueprint PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208
• Role: Universal MLflow registration layer with models-from-code approach
• Signature Handling: Signature creation moved to notebook as per PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 pattern
• Artifact Management:

  /artifacts/data/
    ├── config.yaml          # Model configuration with NeMo model paths
    ├── data/                 # Documents directory (audio samples, etc.)
    ├── demo/                 # UI components (Streamlit, HTML)
    ├── models/               # NeMo model files (.nemo artifacts)
    │   ├── enc_dec_CTC.nemo     # ASR model
    │   ├── fast_pitch.nemo      # TTS spectrogram generator
    │   └── hifi_gan.nemo        # TTS vocoder
  

Package Structure Enhancement

• src/mlflow/__init__.py (Synchronized with PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208): Universal module initialization with generic exports
• Notebooks Updated: Changed imports from core.audio_translation_service.audio_translation_service to src.mlflow
• Signature in Notebook: Signature creation moved to notebook: ModelSignature built and passed to Logger.log_model(signature, ...)

Configuration & Environment Changes

NeMo Model Integration

• Artifact Structure: NeMo .nemo files stored in models/ subdirectory following PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 pattern
• Model Resolution: Intelligent path resolution supports both artifact and configuration contexts
• GPU Optimization: Automatic CUDA device detection and model placement
• Environment Setup: Proxy configuration and secrets management for NeMo model downloads

Universal Loader/Logger Synchronization

• Loader Implementation: Exact copy ensures consistent behavior across all blueprints
• Logger Implementation: Exact copy ensures consistent MLflow integration patterns
• Signature Pattern: Notebook-based signature creation exactly matches PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 approach
• NeMo Compatibility: Universal structure seamlessly handles NeMo model artifacts and configuration

Implementation Details

Architecture Impact

• Design Pattern: Clean layered architecture with separation of concerns following PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208
  • Registration Layer: Logger (universal MLflow integration)
  • Business Logic Layer: Model (NeMo-specific audio translation functionality)
  • Loader Layer: loader (universal MLflow deployment interface)
• NeMo Integration: Seamless integration with NVIDIA NeMo framework while maintaining universal structure
• Multi-Modal Support: Handles audio and text inputs with base64 serialization for web deployment
• Shared Implementation: Loader and logger synchronized with PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 for consistency

Code Organization

• File Structure Changes:
  • Added: src/mlflow/ package with universal structure synchronized with PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208
  • Added: Standalone model class with comprehensive NeMo audio translation functionality
  • Removed: Legacy core/audio_translation_service/ blueprint-specific structure
  • Modified: Notebooks updated for universal imports and signature pattern from PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208
• Signature Handling: Moved to notebook exactly as in PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 (ModelSignature creation + pass to Logger.log_model)
• Module Interactions: Clean imports with explicit dependency management and generic class names
• NeMo Artifacts: Proper handling of large .nemo model files in MLflow artifact structure

NeMo-Specific Enhancements

Model Pipeline Architecture

Audio Input (base64) → ASR (Citrinet) → Text Translation (MarianMT) → TTS (FastPitch + HiFiGAN) → Audio Output (base64)

GPU Memory Management

• Dynamic Model Loading: Models loaded on-demand to GPU memory
• Memory Optimization: Proper cleanup and garbage collection for large NeMo models
• Device Management: Automatic CUDA/CPU detection and model placement

ONNX Export Integration

• Built-in Export: Native support for converting NeMo models to ONNX format
• Multi-Model Export: Handles ASR, MT, and TTS model conversion simultaneously
• Export Configuration: Structured export configuration through get_onnx_export_config()

Universal Synchronization Benefits

• Consistency: Identical loader/logger implementation across all blueprints
• Maintainability: Single source of truth for shared MLflow integration patterns
• NeMo Compatibility: Universal structure works seamlessly with specialized NeMo workflows
• Debugging: Predictable behavior eliminates blueprint-specific variations
• Future Updates: Changes to loader/logger can be applied universally

Quality Assurance

Code Quality

• Code Style: Consistent with repository standards and PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 patterns
• Documentation: Clear architectural layer responsibilities with universal structure
• Error Handling: Robust exception management inherited from PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 loader/logger
• NeMo Integration: Proper handling of NeMo model lifecycle and GPU memory management
• Universal Structure: Standardized layout improves maintainability across blueprints

Performance Impact

• Model Loading: Faster initialization due to eliminated serialization overhead
• Memory Usage: Optimized GPU memory usage for large NeMo models
• Inference Speed: GPU-accelerated prediction pipeline with CUDA optimization
• Deployment Time: Improved deployment reliability with proven models-from-code approach from PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208

Review Guidelines

Critical Review Areas

1. MLflow Integration: Verify loader.py is byte-exact copy from PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 with universal imports
2. API Compatibility: Confirm Model.predict() maintains identical signature and behavior
3. Signature Pattern: Validate signature creation in notebook matches PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 exactly
4. Artifact Handling: Validate proper NeMo model organization matches PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 patterns
5. Universal Structure: Confirm generic class names and imports work correctly
6. NeMo Model Resolution: Verify intelligent path resolution works in both artifact and config contexts
7. GPU Integration: Confirm CUDA optimization and memory management work correctly
8. ONNX Export: Validate ONNX conversion functionality for all NeMo models
9. Loader/Logger Sync: Verify exact synchronization with PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 implementation

Testing Instructions

1. Register Model: Run notebooks/register-model.ipynb to validate new logging approach with universal structure
2. Load and Test: Verify model loads correctly in MLflow UI and responds to API calls
3. Audio Translation: Test end-to-end pipeline with sample audio files
4. Multi-Modal Input: Validate both text-only and audio+text translation modes
5. GPU Performance: Confirm CUDA acceleration works for all model components
6. ONNX Export: Test ONNX model conversion and deployment
7. Deploy Validation: Confirm audio translation endpoint functionality with base64 audio handling
8. Migration Comparison: Compare before/after behavior for identical input scenarios
9. Sync Validation: Verify loader/logger behavior matches vanilla-rag blueprint exactly

Breaking Changes

None - This migration maintains complete API compatibility:

• ✅ Identical model signature and parameter schema for audio translation
• ✅ Unchanged notebook interfaces (imports updated to universal structure)
• ✅ Same API behavior and response formats for multi-modal translation
• ✅ Compatible demo and UI components (Streamlit, HTML)
• ✅ Universal structure provides consistency with PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208
• ✅ NeMo model handling preserved through intelligent artifact resolution

Universal Synchronization Details

Shared Implementation Strategy

• loader.py: Exact byte-for-byte copy from PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 ensures identical behavior
• logger.py: Exact byte-for-byte copy from PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 ensures consistent MLflow integration
• __init__.py: Exact copy from PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 with universal export pattern
• Signature Pattern: Notebook-based creation exactly matches PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 methodology
• NeMo Compatibility: Universal structure seamlessly accommodates NeMo-specific requirements

NeMo-Specific Considerations

• Large Model Artifacts: Proper handling of multi-gigabyte .nemo model files
• GPU Dependencies: Framework requirements preserved through universal structure
• Multi-Model Pipeline: Complex workflow maintained through clean separation of concerns
• ONNX Integration: Export capabilities preserved and enhanced through universal pattern

Future Blueprint Migrations

This implementation provides a reusable migration pattern following PR #208 for specialized frameworks:

1. Copy universal files (loader.py, logger.py, __init__.py) from PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208
2. Develop Model class following PR feat: MLflow 3.1.0 Models-from-Code Migration for Vanilla RAG Blueprint #208 constructor pattern in src/mlflow/model.py
3. Preserve framework-specific features (NeMo, TensorFlow, PyTorch, etc.) within universal structure
4. Update notebooks to create signature and use from src.mlflow import Logger
5. Maintain API compatibility through identical method signatures
6. Apply universal structure with generic class names and standardized layout
7. Handle specialized artifacts (models, datasets, etc.) through universal artifact management

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Migration Status: ✅ Complete with Universal Structure Synchronized with PR #208 and NeMo Integration Ready for Review

This comprehensive architectural migration successfully modernizes the Audio Translation with NeMo blueprint for MLflow 3.1.0 while adopting the universal structure standard and synchronizing shared components with PR #208. The migration preserves all NeMo-specific functionality including GPU acceleration, ONNX export capabilities, and multi-modal audio processing while improving consistency and maintainability across all AI blueprints.

NeMo-Specific Migration Highlights

Advanced Features Preserved

• 🎙️ End-to-End Audio Pipeline: Complete STT → Translation → TTS workflow maintained
• ⚡ GPU Acceleration: CUDA optimization preserved for all NeMo and Transformers models
• 🔄 ONNX Export: Built-in conversion capabilities for production deployment
• 📡 Multi-Modal API: Seamless text-only and audio+text processing modes
• 🎯 Base64 Audio Handling: Web-compatible audio serialization for deployment
• 🧠 Intelligent Model Resolution: Artifact-aware NeMo model path management
• 💾 Memory Optimization: Efficient GPU memory usage for large model inference

Framework Integration Excellence

• NVIDIA NeMo Framework: Full compatibility with Citrinet, FastPitch, and HiFiGAN models
• Hugging Face Transformers: MarianMT integration for machine translation
• PyTorch Integration: Seamless tensor operations and CUDA acceleration
• MLflow Artifacts: Proper handling of large .nemo model files in deployment context

This migration sets the standard for integrating specialized AI frameworks within the MLflow structure while maintaining full feature compatibility and performance optimization.

Printed page of the Streamlit web app showing evidence of successful local deployment and API testing:

Streamlit for Audio Translation with Nemo.pdf

[ata-turhan]

Looks great 🚀