Initial release: DictIA v0.8.14-alpha (fork de Speakr, AGPL-3.0)

src/models/user.py

@@ -0,0 +1,98 @@
+"""
+User and Speaker database models.
+
+This module defines the User model for authentication and user profiles,
+and the Speaker model for tracking speaker profiles used in diarization.
+"""
+
+from datetime import datetime
+from flask_login import UserMixin
+from src.database import db
+
+
+class User(db.Model, UserMixin):
+    """User model for authentication and profile management."""
+
+    id = db.Column(db.Integer, primary_key=True)
+    username = db.Column(db.String(20), unique=True, nullable=False)
+    email = db.Column(db.String(120), unique=True, nullable=False)
+    password = db.Column(db.String(60), nullable=True)
+    sso_provider = db.Column(db.String(100), nullable=True)
+    sso_subject = db.Column(db.String(255), unique=True, nullable=True)
+    is_admin = db.Column(db.Boolean, default=False)
+    can_share_publicly = db.Column(db.Boolean, default=True)  # Permission to create public share links
+    recordings = db.relationship('Recording', backref='owner', lazy=True)
+    transcription_language = db.Column(db.String(10), nullable=True)  # For ISO 639-1 codes
+    output_language = db.Column(db.String(50), nullable=True)  # For full language names like "Spanish"
+    ui_language = db.Column(db.String(10), nullable=True, default='en')  # For UI language preference (en, es, fr, zh)
+    summary_prompt = db.Column(db.Text, nullable=True)
+    extract_events = db.Column(db.Boolean, default=False)  # Enable event extraction from transcripts
+    name = db.Column(db.String(100), nullable=True)
+    job_title = db.Column(db.String(100), nullable=True)
+    company = db.Column(db.String(100), nullable=True)
+    diarize = db.Column(db.Boolean, default=False)
+
+    # Default naming template for title generation
+    default_naming_template_id = db.Column(db.Integer, db.ForeignKey('naming_template.id', ondelete='SET NULL'), nullable=True)
+    default_naming_template = db.relationship('NamingTemplate', foreign_keys=[default_naming_template_id])
+
+    # Token budget (None = unlimited)
+    monthly_token_budget = db.Column(db.Integer, nullable=True)
+
+    # Transcription budget in seconds (None = unlimited)
+    monthly_transcription_budget = db.Column(db.Integer, nullable=True)
+
+    # Email verification fields
+    email_verified = db.Column(db.Boolean, default=False)
+    email_verification_token = db.Column(db.String(200), nullable=True, index=True)
+    email_verification_sent_at = db.Column(db.DateTime, nullable=True)
+
+    # Password reset fields
+    password_reset_token = db.Column(db.String(200), nullable=True, index=True)
+    password_reset_sent_at = db.Column(db.DateTime, nullable=True)
+
+    # Auto speaker labelling settings
+    auto_speaker_labelling = db.Column(db.Boolean, default=False)  # Enable auto-labelling when voice confidence exceeds threshold
+    auto_speaker_labelling_threshold = db.Column(db.String(10), nullable=True, default='medium')  # 'low', 'medium', 'high'
+
+    # Auto summarization setting (user can disable if admin hasn't globally disabled)
+    auto_summarization = db.Column(db.Boolean, default=True)
+
+    # Transcription hints (hotwords and initial prompt for improving ASR accuracy)
+    transcription_hotwords = db.Column(db.Text, nullable=True)
+    transcription_initial_prompt = db.Column(db.Text, nullable=True)
+
+    def __repr__(self):
+        return f"User('{self.username}', '{self.email}')"
+
+
+class Speaker(db.Model):
+    """Speaker model for tracking voice profiles used in diarization."""
+
+    id = db.Column(db.Integer, primary_key=True)
+    name = db.Column(db.String(100), nullable=False)
+    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
+    created_at = db.Column(db.DateTime, default=datetime.utcnow)
+    last_used = db.Column(db.DateTime, default=datetime.utcnow)
+    use_count = db.Column(db.Integer, default=1)
+
+    # Voice embedding fields (256 dimensions from WhisperX)
+    average_embedding = db.Column(db.LargeBinary, nullable=True)  # Binary numpy array (256 × 4 bytes = 1024 bytes)
+    embeddings_history = db.Column(db.JSON, nullable=True)  # List of metadata: [{recording_id, timestamp, similarity}, ...]
+    embedding_count = db.Column(db.Integer, default=0)  # Number of embeddings collected
+    confidence_score = db.Column(db.Float, nullable=True)  # 0-1 score based on embedding consistency
+
+    # Relationship to user
+    user = db.relationship('User', backref=db.backref('speakers', lazy=True, cascade='all, delete-orphan'))
+
+    def to_dict(self):
+        """Convert model to dictionary representation."""
+        return {
+            'id': self.id,
+            'name': self.name,
+            'created_at': self.created_at,
+            'last_used': self.last_used,
+            'use_count': self.use_count,
+            'embedding_count': self.embedding_count,
+            'confidence_score': self.confidence_score
+        }