shrikanth-19/dhravani-mit-test

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Dataset Preparation Interface for Fine-tuning Whisper

A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication.

Features

• —🔐 User authentication via Pocketbase
• —☁️ Cloud storage support (Hugging Face Datasets)
• —🌐 Multi-language support with native names
• —🎤 Modern Material Design recording interface
• —📝 CSV transcript file support
• —🎯 Session-based recording workflow
• —🔄 Advanced recording controls
• —⌨️ Keyboard shortcuts for efficiency
• —📊 Progress tracking and navigation
• —💾 Local and cloud metadata management
• —🎨 Responsive, mobile-friendly UI

Getting Started

1. 1.Create a transcript CSV file with your content:

transcript
"First sentence to record"
"Second sentence to record"
# For multi-language support:
transcript_en,transcript_es
"English sentence","Spanish sentence"

1. 1.Start the Flask application:

python app.py

1. 1.Access the interface:

http://localhost:5000

Usage

1. 1.Authentication

• —Sign in using your Google account
• —Session Setup

• —Upload your transcript CSV
• —Select language and recording location
• —Enter speaker details
• —Click "Start Session"
• —Recording

• —Use on-screen controls or keyboard shortcuts:
• —R: Start recording / Stop recording
• —Space: Play recording
• —Enter: Save recording
• —Backspace: Re-record
• —←: Previous transcript
• —→: Skip current
• —Navigate using row numbers
• —Adjust transcript font size as needed

Data Storage

Recordings are stored in language-specific directories:

• —Storage:

  datasets/
  ├── en/
  │   ├── audio/
  │   │   ├── {user_prefix}_{YYYYMMDD_HHMMSS}.wav
  │   │   └── ...
  │   └── en.parquet         # English recordings metadata
  ├── es/
  │   ├── audio/
  │   │   ├── {user_prefix}_{YYYYMMDD_HHMMSS}.wav
  │   │   └── ...
  │   └── es.parquet         # Spanish recordings metadata
  │

Technical Details

Audio Recording

• —Browser Recording Format: 48kHz mono WebM
• —Storage Format: 16bit mono WAV
• —Maximum Duration: 30 seconds
• —Audio Processing: WebM -> WAV conversion with sample rate adjustment
• —Channels: 1 (mono)

Data Management

• —Metadata Organization:
• —stats.json: Global recording statistics
• —{language_code}.parquet: Language-specific metadata files
• —File Naming: {user_id_prefix}_{YYYYMMDD_HHMMSS}.{format}
• —Unicode Handling: NFC normalization for text

Authentication

• —Provider: Pocketbase with Google OAuth
• —Session Management: Server-side Flask sessions

Languages

• —Support: 74 languages with native names
• —Codes: ISO 639-1 standard
• —CSV Format:
• —Single language: transcript column
• —Multi-language: transcript_${lang_code} columns

Upload Management

• —Queue System: Background worker thread
• —Status Tracking: Real-time upload status polling
• —Error Handling: Automatic retries with timeout
• —Progress Updates: Toast notifications
• —Temporary Storage: ./temp folder for conversions

Frontend Features

• —Keyboard Shortcuts: Recording and navigation
• —Real-time Status: Progress tracking and notifications

Security

• —Authentication Required: All routes except static/login
• —File Validation: MIME type and extension checking
• —Secure Context: HTTPS recommended

Performance

• —Upload Queue: Asynchronous processing
• —Audio Conversion: Server-side processing
• —Session Caching: Browser storage optimization
• —Progress Tracking: Real-time websocket updates

Browser Support

• —Chrome (recommended)
• —Brave
• —Edge
• —Safari

Known Limitations

• —Requires microphone permissions
• —Internet connection needed
• —Maximum recording duration: 30 seconds
• —File size limits based on storage backend