[Feature Request] Add Whisper Fine-tuning with LoRA Integration

[scwall]

Hello, proposal this

Feature Request: Add Whisper Fine-tuning with LoRA Integration

Summary

I would like to propose integrating Whisper fine-tuning capabilities with LoRA (Low-Rank Adaptation) directly into Speech Note. This would allow users to create personalized speech recognition models that better understand their specific vocabulary, accent, and speaking patterns.

Motivation

While Speech Note already provides excellent speech recognition with various Whisper models, users often encounter accuracy issues with:

• Personal names and proper nouns
• Technical terminology specific to their profession
• Regional accents or speaking patterns
• Domain-specific vocabulary (medical, legal, technical, etc.)

Fine-tuning with LoRA would enable users to create personalized models that significantly improve recognition accuracy for their specific use cases.

Proposed Solution

Core Features

1. Built-in Fine-tuning Interface

   • Simple UI to upload audio recordings + transcriptions
   • Dataset preparation and validation tools
   • Progress monitoring during training

2. LoRA-based Training

   • Efficient fine-tuning using Low-Rank Adaptation
   • Reduced computational requirements (compatible with consumer GPUs)
   • Fast training times (hours instead of days)
   • Small adapter files (50-200MB vs full model retraining)

3. Model Management

   • Multiple LoRA adapters per base model
   • Easy switching between adapters for different contexts
   • Export/import functionality for sharing adapters

Technical Implementation

User Workflow

1. Record Training Data: User records 30-60 minutes of audio with accurate transcriptions
2. Start Fine-tuning: Speech Note processes the data and trains LoRA adapters
3. Switch Models: User can select between original models and their personalized versions
4. Improved Recognition: Significantly better accuracy for user's specific vocabulary

Benefits

For Users

• Dramatically improved accuracy for personal vocabulary
• Professional terminology recognition
• Accent adaptation for better transcription
• Privacy-focused: Training happens locally, no data leaves the device

For Speech Note

• Competitive advantage: First offline speech-to-text app with built-in fine-tuning
• User retention: Personalized models create strong user lock-in
• Professional market: Appeal to professionals needing domain-specific recognition

Technical Feasibility

Proven Technology Stack

• LoRA: Well-established technique, used in production by major AI companies
• Existing Libraries:
  • Whisper-Finetune (production-ready)
  • ASR-whisper-finetuning (educational)
  • HuggingFace PEFT library

Hardware Requirements

• Compatible with existing Speech Note requirements
• RTX 4060/4070 sufficient for training
• Training time: 2-6 hours for typical datasets
• Storage: +200MB per adapter (minimal impact)

Integration Points

1. New "Fine-tuning" tab in the main interface
2. Model selector with LoRA adapter options
3. Training progress UI with real-time updates
4. GGML export compatibility for optimized inference

Implementation Phases

Phase 1: Core Infrastructure

• LoRA training pipeline integration
• Basic UI for dataset upload
• Progress monitoring

Phase 2: User Experience

• Enhanced dataset preparation tools
• Model management interface
• Export/import functionality

Phase 3: Advanced Features

• Multi-language fine-tuning support
• Collaborative training (team adapters)
• Cloud training option for limited hardware

Similar Projects Reference

• OpenAI GPT Fine-tuning: Successful commercial implementation of LoRA
• Stable Diffusion LoRA: Widely adopted in the creative community
• Chinese Whisper-Finetune: 60% error reduction with minimal training data

Expected Impact

Based on research and existing projects:

• 50-70% reduction in word error rate for domain-specific vocabulary
• Professional users: Doctors, lawyers, engineers could see near-perfect recognition
• Accessibility: Better support for accented speech and speech impediments

Code Integration Approach

The feature could be implemented as:

1. Optional module: Users can enable/disable fine-tuning features
2. Separate binary: Keep core Speech Note lightweight
3. Plugin architecture: Community-contributed fine-tuning implementations

Community Benefit

This feature would position Speech Note as:

• The first consumer-friendly app with local speech model fine-tuning
• A research platform for the speech recognition community
• An accessibility tool for users with unique speech patterns

Request for Feedback

I'd love to hear the maintainers' thoughts on:

1. Technical feasibility within Speech Note's architecture
2. UI/UX integration preferences
3. Potential implementation timeline
4. Community interest in contributing to this feature

I'm willing to contribute code, testing, and documentation to help make this feature a reality.

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Thank's 😄

Ref: https://github.com/Theodb/ASR-whisper-finetuning Ref: https://github.com/yeyupiaoling/Whisper-Finetune/blob/master/README_en.md#%E5%AE%89%E8%A3%85%E7%8E%AF%E5%A2%83

[mkiol]

Is this proposal AI generated? :)

Thanks for the idea. It sounds like a very interesting feature, but training/fine-tuning the model is outside the scope of Speech Note at the moment. Implementing this would require a lot of effort and slow down the implementation of many other features.

[scwall]

Hello, I actually use chatgpt to put my messy text back into a more comprehensible context. I sometimes have trouble structuring my text so that it is comprehensible, or an LLM performs well.

No problem thank's for response :)