fix: db migrations

app/cli.py

@@ -265,14 +265,16 @@ def start(config: str, host: Optional[str], port: Optional[int], build_frontend:
             click.echo("❌ npm not found. Please install Node.js and npm.", err=True)
             sys.exit(1)
 
-    # Run migrations before starting (unless explicitly skipped)
+    # Initialize DB tables and run migrations before starting (unless explicitly skipped)
     if not skip_migrations:
-        click.echo("🔄 Running database migrations before startup...")
+        click.echo("🔄 Initializing database and running migrations...")
+        from app.database import init_db
         from app.core.migrations import run_migrations, ensure_migrations_directory
         try:
+            init_db()
             ensure_migrations_directory()
             run_migrations()
-            click.echo("✅ Migrations completed successfully")
+            click.echo("✅ Database initialized and migrations completed")
         except Exception as e:
             click.echo(f"❌ Migration failed: {e}", err=True)
             click.echo("💡 You can skip migrations with --skip-migrations (not recommended)", err=True)
@@ -472,14 +474,16 @@ def signal_handler(sig, frame):
     signal.signal(signal.SIGINT, signal_handler)
     signal.signal(signal.SIGTERM, signal_handler)
 
-    # Run migrations before starting (unless explicitly skipped)
+    # Initialize DB tables and run migrations before starting (unless explicitly skipped)
     if not skip_migrations:
-        click.echo("🔄 Running database migrations before startup...")
+        click.echo("🔄 Initializing database and running migrations...")
+        from app.database import init_db
         from app.core.migrations import run_migrations, ensure_migrations_directory
         try:
+            init_db()
             ensure_migrations_directory()
             run_migrations()
-            click.echo("✅ Migrations completed successfully")
+            click.echo("✅ Database initialized and migrations completed")
         except Exception as e:
             click.echo(f"❌ Migration failed: {e}", err=True)
             click.echo("💡 You can skip migrations with --skip-migrations (not recommended)", err=True)

app/database.py

@@ -37,6 +37,11 @@ def get_db():
 
 def init_db():
     """Initialize database by creating all tables and run column migrations."""
+    # Import all models so SQLAlchemy registers them with Base.metadata
+    # before create_all is called. Without this, tables won't be created
+    # on a fresh database.
+    import app.models.database  # noqa: F401
+
     Base.metadata.create_all(bind=engine)
     _run_column_migrations()
 

app/migrations/001_add_default_agent_to_org_member.py

@@ -14,6 +14,15 @@
 def upgrade(db: Session):
     """Add default_agent_id column with foreign key to agents table."""
 
+    # Check if the table exists at all (on fresh DBs, create_all handles this)
+    table_check = db.execute(text("""
+        SELECT 1 FROM information_schema.tables
+        WHERE table_name = 'organization_members'
+    """))
+    if table_check.fetchone() is None:
+        print("Table organization_members does not exist yet (fresh DB, handled by create_all), skipping...")
+        return
+
     # Check if column already exists
     result = db.execute(text("""
         SELECT column_name 

app/services/reporting/voice_playground_report_service.py

@@ -1155,14 +1155,57 @@ def _threshold_to_pct(raw_threshold: Any) -> float | None:
             "report_options": normalized_options,
         }
 
+    _weasyprint_deps_checked = False
+
+    @staticmethod
+    def _ensure_weasyprint_system_deps():
+        """One-shot check/install of system libraries required by WeasyPrint."""
+        if VoicePlaygroundReportService._weasyprint_deps_checked:
+            return
+        VoicePlaygroundReportService._weasyprint_deps_checked = True
+
+        import ctypes.util
+        if ctypes.util.find_library("gobject-2.0"):
+            return
+
+        import shutil, subprocess, os
+        if shutil.which("apt-get") and os.geteuid() == 0:
+            from loguru import logger
+            logger.info("[PDF] WeasyPrint system libs missing – installing via apt-get...")
+            try:
+                subprocess.check_call(
+                    ["apt-get", "update", "-qq"],
+                    timeout=120,
+                    stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,
+                )
+                subprocess.check_call(
+                    [
+                        "apt-get", "install", "-y", "-qq",
+                        "libgobject-2.0-0", "libpango-1.0-0", "libpangocairo-1.0-0",
+                        "libcairo2", "libgdk-pixbuf-2.0-0", "libffi-dev", "shared-mime-info",
+                    ],
+                    timeout=120,
+                )
+                logger.info("[PDF] WeasyPrint system libs installed successfully")
+            except Exception as e:
+                logger.warning(f"[PDF] Auto-install of system libs failed: {e}")
+
     def render_pdf(self, payload: dict[str, Any]) -> bytes:
         """Render report payload to PDF bytes."""
+        self._ensure_weasyprint_system_deps()
+
         try:
             from weasyprint import HTML
         except ImportError as exc:
             raise RuntimeError(
                 "PDF generation requires weasyprint. Install dependencies and retry."
             ) from exc
+        except OSError as exc:
+            raise RuntimeError(
+                f"WeasyPrint system libraries missing: {exc}. "
+                "Install them with: apt-get install -y libgobject-2.0-0 libpango-1.0-0 "
+                "libpangocairo-1.0-0 libcairo2 libgdk-pixbuf-2.0-0 libffi-dev shared-mime-info"
+            ) from exc
 
         template = self._jinja_env.get_template("reports/voice_playground_report.html")
         html_content = template.render(**payload, service=self)

app/services/voice_agent/voice_bundle.py

@@ -132,6 +132,9 @@ def _get_service(service_name: str):
     elif service_name == "SarvamTTSService":
         from efficientai.services.sarvam.tts import SarvamTTSService
         service_class = SarvamTTSService
+    elif service_name == "VoiceMakerTTSService":
+        from efficientai.services.voicemaker.tts import VoiceMakerTTSService
+        service_class = VoiceMakerTTSService
 
     # LLM Services
     elif service_name == "OpenAILLMService":
@@ -270,6 +273,16 @@ def _get_tts_providers():
                 model=model if model else "bulbul:v3",
             ),
         },
+        "voicemaker": {
+            "env_key": "VOICEMAKER_API_KEY",
+            "default_voice": "ai3-Jony",
+            "default_model": "neural",
+            "factory": lambda api_key, voice_id, model: _get_service("VoiceMakerTTSService")(
+                api_key=api_key,
+                voice_id=voice_id,
+                model=model if model else "neural",
+            ),
+        },
     }
 
 
@@ -448,6 +461,8 @@ async def run_voice_bundle_fastapi(
     call_start_time = time.time()
     s3_key_result = None
     duration_result = None
+    transcript_text = None
+    conversation_turns = []
 
     # Storage for audio data from the buffer processor
     recorded_audio_data = {"audio": None, "sample_rate": None, "num_channels": None}
@@ -643,6 +658,34 @@ async def on_client_disconnected(transport, client):
             # Calculate duration
             duration_result = time.time() - call_start_time
 
+            # Extract conversation transcript from the LLM context
+            try:
+                raw_messages = context.messages if hasattr(context, 'messages') else []
+                conversation_turns = []
+                transcript_parts = []
+                elapsed = 0.0
+                for msg in raw_messages:
+                    role = msg.get("role", "")
+                    content = msg.get("content", "")
+                    if not content or role == "system":
+                        continue
+                    speaker = "user" if role == "user" else "assistant"
+                    turn_duration = max(1.0, len(content.split()) * 0.4)
+                    conversation_turns.append({
+                        "speaker": speaker,
+                        "text": content,
+                        "start": round(elapsed, 2),
+                        "end": round(elapsed + turn_duration, 2),
+                    })
+                    transcript_parts.append(f"{speaker}: {content}")
+                    elapsed += turn_duration
+                transcript_text = "\n".join(transcript_parts) if transcript_parts else None
+                logger.info(f"Captured {len(conversation_turns)} conversation turns from live pipeline")
+            except Exception as ctx_err:
+                logger.warning(f"Failed to extract conversation context: {ctx_err}")
+                conversation_turns = []
+                transcript_text = None
+
             # Merge captured audio chunks
             total_input_audio = b''.join(input_audio_chunks) if input_audio_chunks else b''
             total_output_audio = b''.join(output_audio_chunks) if output_audio_chunks else b''
@@ -700,26 +743,23 @@ async def on_client_disconnected(transport, client):
             "agent_id": agent_id,
             "persona_id": persona_id,
             "scenario_id": scenario_id,
+            "transcription": transcript_text,
+            "speaker_segments": conversation_turns if conversation_turns else None,
             "error": str(e),
         }
 
-    if s3_key_result:
-        return {
-            "s3_key": s3_key_result,
-            "duration": duration_result,
-            "agent_id": agent_id,
-            "persona_id": persona_id,
-            "scenario_id": scenario_id,
-        }
-
-    return {
-        "s3_key": None,
+    metadata = {
+        "s3_key": s3_key_result,
         "duration": duration_result,
         "agent_id": agent_id,
         "persona_id": persona_id,
         "scenario_id": scenario_id,
-        "error": "No audio file was uploaded",
+        "transcription": transcript_text,
+        "speaker_segments": conversation_turns if conversation_turns else None,
     }
+    if not s3_key_result and not transcript_text:
+        metadata["error"] = "No audio file was uploaded and no transcript captured"
+    return metadata
 
 
 if __name__ == "__main__":

app/workers/tasks/tts_comparison.py

@@ -120,11 +120,35 @@ def _normalize_entities(text: str) -> str:
         }
 
 
+_nemo_install_attempted = False
+
+
+def _lazy_install_nemo():
+    """One-shot attempt to pip-install nemo_toolkit[asr] at runtime."""
+    global _nemo_install_attempted
+    if _nemo_install_attempted:
+        return False
+    _nemo_install_attempted = True
+
+    logger.info("[TTS Eval] NeMo not found – attempting auto-install (this may take a few minutes)...")
+    try:
+        import subprocess, sys
+        subprocess.check_call(
+            [sys.executable, "-m", "pip", "install", "--quiet", "nemo_toolkit[asr]>=1.20.0"],
+            timeout=600,
+        )
+        logger.info("[TTS Eval] nemo_toolkit[asr] installed successfully")
+        return True
+    except Exception as install_err:
+        logger.warning(f"[TTS Eval] Auto-install of nemo_toolkit[asr] failed: {install_err}")
+        return False
+
+
 def _get_nemo_asr_model():
     """Lazy-load NVIDIA NeMo Conformer CTC model for hallucination detection.
 
-    Requires: pip install efficientai[nemo-asr]
-    Returns the model instance, or None if NeMo is not installed.
+    On first call, if NeMo is not installed, attempts a one-time pip install.
+    Returns the model instance, or None if unavailable.
     """
     global _nemo_asr_model
 
@@ -133,30 +157,28 @@ def _get_nemo_asr_model():
 
     try:
         import nemo.collections.asr as nemo_asr
+    except ImportError:
+        if _lazy_install_nemo():
+            try:
+                import nemo.collections.asr as nemo_asr
+            except ImportError:
+                logger.warning("[TTS Eval] NeMo still not importable after install – WER/CER will be skipped")
+                return None
+        else:
+            logger.warning(
+                "[TTS Eval] NeMo is not installed – WER/CER hallucination metrics will be skipped. "
+                "To install manually: pip install 'nemo_toolkit[asr]'"
+            )
+            return None
 
+    try:
         logger.info("[TTS Eval] Loading NeMo ASR model (stt_en_conformer_ctc_large)...")
         _nemo_asr_model = nemo_asr.models.ASRModel.from_pretrained("stt_en_conformer_ctc_large")
         logger.info("[TTS Eval] NeMo ASR model loaded successfully")
         return _nemo_asr_model
-    except ImportError as e:
-        logger.warning(
-            f"[TTS Eval] NeMo import failed: {e} – "
-            "WER/CER hallucination metrics will be skipped. "
-            "To enable, run:\n"
-            "  pip install 'nemo_toolkit[asr]'\n"
-            "  python -c \"import nemo.collections.asr as nemo_asr; "
-            "nemo_asr.models.ASRModel.from_pretrained('stt_en_conformer_ctc_large')\""
-        )
     except Exception as e:
-        logger.error(
-            f"[TTS Eval] NeMo ASR model failed to load: {e} – "
-            "The model may not be cached yet. To download it manually, run:\n"
-            "  python -c \"import nemo.collections.asr as nemo_asr; "
-            "nemo_asr.models.ASRModel.from_pretrained('stt_en_conformer_ctc_large')\"",
-            exc_info=True,
-        )
-
-    return None
+        logger.error(f"[TTS Eval] NeMo ASR model failed to load: {e}", exc_info=True)
+        return None
 
 
 def _transcribe_audio_for_eval(audio_path: str) -> str | None:

docker/Dockerfile.worker

@@ -4,12 +4,21 @@ FROM python:3.11-slim
 ARG INSTALL_EXTRAS="qualitative-voice,reports"
 ARG TORCH_CPU_ONLY="true"
 
-# Install system dependencies (git needed for torch.hub clone, build-essential +
-# cmake needed for packages like praat-parselmouth on arm64/Apple Silicon)
+# Install system dependencies:
+#   - git: needed for torch.hub clone
+#   - build-essential + cmake: needed for packages like praat-parselmouth on arm64
+#   - libgobject/libpango/libcairo/libgdk-pixbuf/libffi/shared-mime-info: WeasyPrint PDF rendering
 RUN apt-get update && apt-get install -y \
     git \
     build-essential \
     cmake \
+    libgobject-2.0-0 \
+    libpango-1.0-0 \
+    libpangocairo-1.0-0 \
+    libcairo2 \
+    libgdk-pixbuf-2.0-0 \
+    libffi-dev \
+    shared-mime-info \
     && rm -rf /var/lib/apt/lists/*
 
 # Install uv
@@ -67,6 +76,9 @@ RUN mkdir -p /app/uploads
 # Notes:
 # - CPU-only PyTorch is installed by default (TORCH_CPU_ONLY=true), saving ~1.5GB
 #   Set TORCH_CPU_ONLY=false if you need GPU support
+# - NeMo ASR (WER/CER hallucination metrics) is NOT included by default (~2GB).
+#   It will be auto-installed on first use, or you can bake it in at build time:
+#     docker compose build --build-arg INSTALL_EXTRAS="qualitative-voice,reports,nemo-asr" worker
 # - ML models (emotion classifier, valence/arousal, UTMOS, NeMo ASR) are
 #   downloaded lazily on first use, keeping the image smaller
 # - For persistent model caching across container restarts, mount volumes:

src/efficientai/services/voicemaker/__init__.py

@@ -6,4 +6,7 @@ def __getattr__(name):
     if name == "synthesize_voicemaker_bytes":
         from .http_tts import synthesize_voicemaker_bytes
         return synthesize_voicemaker_bytes
+    if name == "VoiceMakerTTSService":
+        from .tts import VoiceMakerTTSService
+        return VoiceMakerTTSService
     raise AttributeError(f"module 'efficientai.services.voicemaker' has no attribute '{name}'")