Add source code

zen_translator/__init__.py

@@ -0,0 +1,26 @@
+"""
+Zen Translator - Real-time multimodal translation with lip sync and voice cloning.
+
+Built on:
+- Qwen3-Omni: Real-time speech understanding and translation
+- CosyVoice 2.0: Ultra-low latency voice cloning (150ms)
+- Wav2Lip: Accurate lip synchronization
+
+Features:
+- 18 input languages, 10 output languages
+- News anchor voice finetuning for accurate translation
+- Sub-second end-to-end latency
+- WebRTC streaming support
+"""
+
+__version__ = "0.1.0"
+__author__ = "Hanzo AI / Zen LM"
+
+from .config import TranslatorConfig
+from .pipeline import TranslationPipeline
+
+__all__ = [
+    "TranslationPipeline",
+    "TranslatorConfig",
+    "__version__",
+]

zen_translator/cli.py

@@ -0,0 +1,261 @@
+"""
+Zen Translator CLI.
+
+Commands:
+- translate: Translate audio/video files
+- serve: Start the translation server
+- train: Train/finetune models
+- dataset: Build training datasets
+- download: Download models
+"""
+
+import asyncio
+from pathlib import Path
+
+import typer
+from rich.console import Console
+from rich.progress import Progress, SpinnerColumn, TextColumn
+
+app = typer.Typer(
+    name="zen-translate",
+    help="Real-time multimodal translation with voice cloning and lip sync",
+)
+console = Console()
+
+
+@app.command()
+def translate(
+    input_path: Path = typer.Argument(..., help="Input audio or video file"),
+    output_path: Path | None = typer.Option(None, "-o", "--output", help="Output file path"),
+    source_lang: str | None = typer.Option(None, "-s", "--source", help="Source language"),
+    target_lang: str = typer.Option("en", "-t", "--target", help="Target language"),
+    speaker_id: str | None = typer.Option(None, "--speaker", help="Speaker ID for voice cloning"),
+    no_lip_sync: bool = typer.Option(False, "--no-lip-sync", help="Disable lip synchronization"),
+):
+    """Translate an audio or video file."""
+    from .config import TranslatorConfig
+    from .pipeline import TranslationPipeline
+
+    config = TranslatorConfig()
+    config.enable_lip_sync = not no_lip_sync
+
+    pipeline = TranslationPipeline(config)
+
+    with Progress(
+        SpinnerColumn(),
+        TextColumn("[progress.description]{task.description}"),
+        console=console,
+    ) as progress:
+        task = progress.add_task("Loading models...", total=None)
+        asyncio.run(pipeline.load())
+
+        progress.update(task, description="Translating...")
+
+        if input_path.suffix in [".mp4", ".avi", ".mov", ".mkv"]:
+            result = asyncio.run(
+                pipeline.translate_video(
+                    video=input_path,
+                    source_lang=source_lang,
+                    target_lang=target_lang,
+                    speaker_id=speaker_id,
+                    output_path=output_path,
+                )
+            )
+            console.print(
+                f"[green]✓[/green] Translated video saved to: {result.get('output_path')}"
+            )
+        else:
+            result = asyncio.run(
+                pipeline.translate_audio(
+                    audio=input_path,
+                    source_lang=source_lang,
+                    target_lang=target_lang,
+                    speaker_id=speaker_id,
+                )
+            )
+            console.print(f"[green]✓[/green] Translation: {result['text']}")
+
+    console.print(f"Source: {result['source_lang']} → Target: {result['target_lang']}")
+
+
+@app.command()
+def serve(
+    host: str = typer.Option("0.0.0.0", "--host", help="Host to bind to"),
+    port: int = typer.Option(8000, "--port", help="Port to listen on"),
+    reload: bool = typer.Option(False, "--reload", help="Enable auto-reload"),
+):
+    """Start the translation server."""
+    import uvicorn
+
+    console.print(f"[bold blue]Starting Zen Translator server on {host}:{port}[/bold blue]")
+
+    uvicorn.run(
+        "zen_translator.streaming:create_app",
+        host=host,
+        port=port,
+        reload=reload,
+        factory=True,
+    )
+
+
+@app.command()
+def download(
+    model: str = typer.Argument(
+        "all", help="Model to download: qwen3-omni, cosyvoice, wav2lip, or all"
+    ),
+    cache_dir: Path = typer.Option(
+        Path("./models"), "--cache-dir", help="Directory to cache models"
+    ),
+):
+    """Download required models."""
+    from huggingface_hub import snapshot_download
+
+    models = {
+        "qwen3-omni": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+        "cosyvoice": "FunAudioLLM/CosyVoice2-0.5B",
+        "wav2lip": "numz/wav2lip_studio",
+    }
+
+    if model == "all":
+        to_download = list(models.items())
+    elif model in models:
+        to_download = [(model, models[model])]
+    else:
+        console.print(f"[red]Unknown model: {model}[/red]")
+        raise typer.Exit(1)
+
+    for name, repo_id in to_download:
+        console.print(f"[blue]Downloading {name}...[/blue]")
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            console=console,
+        ) as progress:
+            task = progress.add_task(f"Downloading {repo_id}...", total=None)
+
+            snapshot_download(
+                repo_id,
+                local_dir=cache_dir / name,
+                local_dir_use_symlinks=False,
+            )
+
+            progress.update(task, description=f"[green]✓ {name} downloaded[/green]")
+
+    console.print("[green]All models downloaded successfully![/green]")
+
+
+@app.command()
+def train(
+    config_file: Path | None = typer.Option(None, "--config", help="Training config YAML file"),
+    model_type: str = typer.Option(
+        "identity", "--type", help="Training type: identity, anchor, or translation"
+    ),
+    dataset_path: Path | None = typer.Option(None, "--dataset", help="Path to training dataset"),
+    output_dir: Path = typer.Option(
+        Path("./outputs"), "--output", help="Output directory for trained model"
+    ),
+):
+    """Train or finetune the translation model."""
+    from .training import NewsAnchorConfig, SwiftTrainingConfig, ZenIdentityConfig
+
+    # Select config type
+    if model_type == "identity":
+        config = ZenIdentityConfig()
+    elif model_type == "anchor":
+        config = NewsAnchorConfig()
+    else:
+        config = SwiftTrainingConfig()
+
+    if dataset_path:
+        config.dataset_path = str(dataset_path)
+    config.output_dir = str(output_dir)
+
+    # Save config
+    config_path = output_dir / "train_config.yaml"
+    output_dir.mkdir(parents=True, exist_ok=True)
+    config.to_yaml(config_path)
+
+    console.print(f"[blue]Training config saved to: {config_path}[/blue]")
+    console.print("[yellow]Run training with:[/yellow]")
+    console.print(f"  swift sft {' '.join(config.to_swift_args())}")
+
+
+@app.command()
+def dataset(
+    action: str = typer.Argument("build", help="Action: build, collect, or export"),
+    output_dir: Path = typer.Option(
+        Path("./data/news_anchors"), "--output", help="Output directory"
+    ),
+    channels: str | None = typer.Option(
+        None, "--channels", help="Comma-separated channel names (cnn,bbc,nhk,dw)"
+    ),
+    max_videos: int = typer.Option(10, "--max-videos", help="Max videos per channel"),
+):
+    """Build training datasets from news anchors."""
+    from .training import NEWS_CHANNELS, build_news_anchor_dataset
+
+    if action == "list":
+        console.print("[bold]Available news channels:[/bold]")
+        for name, url in NEWS_CHANNELS.items():
+            console.print(f"  {name}: {url}")
+        return
+
+    channel_list = channels.split(",") if channels else ["cnn", "bbc", "nhk", "dw"]
+
+    console.print(f"[blue]Building dataset from: {', '.join(channel_list)}[/blue]")
+
+    result_path = asyncio.run(
+        build_news_anchor_dataset(
+            output_dir=output_dir,
+            channels=channel_list,
+            max_videos_per_channel=max_videos,
+        )
+    )
+
+    console.print(f"[green]✓ Dataset created at: {result_path}[/green]")
+
+
+@app.command()
+def register_speaker(
+    speaker_id: str = typer.Argument(..., help="Unique speaker identifier"),
+    audio_file: Path = typer.Argument(..., help="Reference audio file (3+ seconds)"),
+):
+    """Register a speaker for voice cloning."""
+    from .config import TranslatorConfig
+    from .voice_clone import CosyVoiceCloner
+
+    config = TranslatorConfig()
+    cloner = CosyVoiceCloner(config)
+
+    with Progress(
+        SpinnerColumn(),
+        TextColumn("[progress.description]{task.description}"),
+        console=console,
+    ) as progress:
+        task = progress.add_task("Loading voice cloner...", total=None)
+        cloner.load()
+
+        progress.update(task, description="Registering speaker...")
+        result = asyncio.run(
+            cloner.register_speaker(
+                speaker_id=speaker_id,
+                reference_audio=audio_file,
+            )
+        )
+
+    console.print(f"[green]✓ Speaker registered: {speaker_id}[/green]")
+    console.print(f"  Duration: {result['duration']:.1f}s")
+
+
+@app.command()
+def version():
+    """Show version information."""
+    from . import __version__
+
+    console.print(f"Zen Translator v{__version__}")
+    console.print("Built on Qwen3-Omni, CosyVoice 2.0, and Wav2Lip")
+    console.print("Created by Hanzo AI / Zen LM")
+
+
+if __name__ == "__main__":
+    app()

zen_translator/config.py

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+"""Configuration for Zen Translator pipeline."""
+
+from pathlib import Path
+from typing import Literal
+
+from pydantic import Field
+from pydantic_settings import BaseSettings
+
+
+class TranslatorConfig(BaseSettings):
+    """Configuration for the translation pipeline."""
+
+    # Model paths
+    qwen3_omni_model: str = Field(
+        default="Qwen/Qwen3-Omni-30B-A3B-Instruct", description="Qwen3-Omni model for translation"
+    )
+    cosyvoice_model: str = Field(
+        default="FunAudioLLM/CosyVoice2-0.5B", description="CosyVoice model for voice cloning"
+    )
+    wav2lip_model: str = Field(
+        default="numz/wav2lip_studio", description="Wav2Lip model for lip sync"
+    )
+
+    # Local model cache
+    model_cache_dir: Path = Field(
+        default=Path("./models"), description="Directory to cache downloaded models"
+    )
+
+    # Translation settings
+    source_language: str = Field(default="auto", description="Source language (auto-detect)")
+    target_language: str = Field(default="en", description="Target language for translation")
+
+    # Supported languages
+    # Input: 18 languages + 6 dialects
+    supported_input_languages: list[str] = [
+        "en",
+        "zh",
+        "ja",
+        "ko",
+        "es",
+        "fr",
+        "de",
+        "it",
+        "pt",
+        "ru",
+        "ar",
+        "hi",
+        "th",
+        "vi",
+        "id",
+        "ms",
+        "tr",
+        "pl",
+        # Dialects
+        "yue",  # Cantonese
+        "wuu",  # Shanghainese
+        "hsn",  # Xiang
+        "nan",  # Min Nan
+        "hak",  # Hakka
+        "cdo",  # Min Dong
+    ]
+    # Output: 10 languages
+    supported_output_languages: list[str] = [
+        "en",
+        "zh",
+        "ja",
+        "ko",
+        "es",
+        "fr",
+        "de",
+        "it",
+        "pt",
+        "ru",
+    ]
+
+    # Voice cloning settings
+    voice_reference_seconds: float = Field(
+        default=3.0, description="Minimum seconds of reference audio for voice cloning"
+    )
+    preserve_emotion: bool = Field(
+        default=True, description="Preserve speaker emotion in cloned voice"
+    )
+    preserve_inflection: bool = Field(
+        default=True, description="Preserve speaker inflection patterns"
+    )
+
+    # Lip sync settings
+    enable_lip_sync: bool = Field(default=True, description="Enable lip synchronization")
+    lip_sync_quality: Literal["fast", "balanced", "quality"] = Field(
+        default="balanced", description="Lip sync quality/speed tradeoff"
+    )
+
+    # Streaming settings
+    streaming_chunk_ms: int = Field(
+        default=200, description="Audio chunk size in milliseconds for streaming"
+    )
+    buffer_size_ms: int = Field(default=500, description="Buffer size for smoother playback")
+
+    # Hardware settings
+    device: Literal["cuda", "cpu", "mps"] = Field(
+        default="cuda", description="Device to run models on"
+    )
+    dtype: Literal["float16", "bfloat16", "float32"] = Field(
+        default="bfloat16", description="Model precision"
+    )
+
+    # Performance tuning
+    use_flash_attention: bool = Field(default=True, description="Use Flash Attention 2")
+    compile_model: bool = Field(default=False, description="Use torch.compile")
+
+    # Finetuning settings (for news anchor voices)
+    finetune_enabled: bool = Field(default=False, description="Enable finetuning mode")
+    finetune_output_dir: Path = Field(
+        default=Path("./outputs/finetune"), description="Output directory for finetuned models"
+    )
+    lora_rank: int = Field(default=64, description="LoRA rank for finetuning")
+    lora_alpha: int = Field(default=128, description="LoRA alpha")
+
+    model_config = {
+        "env_prefix": "ZEN_TRANSLATOR_",
+        "env_file": ".env",
+    }
+
+
+class NewsAnchorConfig(BaseSettings):
+    """Configuration for news anchor voice training."""
+
+    # Dataset settings
+    dataset_dir: Path = Field(
+        default=Path("./data/news_anchors"),
+        description="Directory containing news anchor audio/video data",
+    )
+    min_clip_duration: float = Field(default=5.0, description="Minimum clip duration in seconds")
+    max_clip_duration: float = Field(default=30.0, description="Maximum clip duration in seconds")
+
+    # Target anchors (examples)
+    target_anchors: list[str] = [
+        "anderson_cooper",
+        "rachel_maddow",
+        "tucker_carlson",
+        "don_lemon",
+        "wolf_blitzer",
+        "bbc_news",
+        "cnn_international",
+        "sky_news",
+        "nhk_world",
+        "dw_news",
+    ]
+
+    # Training settings
+    batch_size: int = Field(default=4, description="Training batch size")
+    gradient_accumulation_steps: int = Field(default=8, description="Gradient accumulation")
+    learning_rate: float = Field(default=2e-5, description="Learning rate")
+    num_epochs: int = Field(default=3, description="Number of training epochs")
+    warmup_ratio: float = Field(default=0.1, description="Warmup ratio")
+
+    # Data augmentation
+    augment_noise: bool = Field(default=True, description="Add background noise augmentation")
+    augment_speed: bool = Field(default=True, description="Speed variation augmentation")
+    noise_levels: list[float] = [0.01, 0.02, 0.05]
+    speed_factors: list[float] = [0.9, 0.95, 1.0, 1.05, 1.1]
+
+    model_config = {
+        "env_prefix": "ZEN_ANCHOR_",
+    }

zen_translator/lip_sync/__init__.py

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+"""Lip synchronization module using Wav2Lip."""
+
+from .wav2lip import Wav2LipSync
+
+__all__ = ["Wav2LipSync"]

zen_translator/lip_sync/wav2lip.py

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+"""
+Wav2Lip lip synchronization module.
+
+Generates accurate lip movements synchronized with translated audio.
+Optimized for real-time video dubbing applications.
+"""
+
+import logging
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+
+from ..config import TranslatorConfig
+
+logger = logging.getLogger(__name__)
+
+
+class Wav2LipSync:
+    """Lip synchronization using Wav2Lip."""
+
+    # Quality presets
+    QUALITY_PRESETS = {
+        "fast": {
+            "resize_factor": 2,
+            "face_det_batch_size": 16,
+            "wav2lip_batch_size": 128,
+        },
+        "balanced": {
+            "resize_factor": 1,
+            "face_det_batch_size": 8,
+            "wav2lip_batch_size": 64,
+        },
+        "quality": {
+            "resize_factor": 1,
+            "face_det_batch_size": 4,
+            "wav2lip_batch_size": 32,
+        },
+    }
+
+    def __init__(self, config: TranslatorConfig):
+        self.config = config
+        self.model = None
+        self.face_detector = None
+        self._loaded = False
+
+        self.preset = self.QUALITY_PRESETS[config.lip_sync_quality]
+
+    def load(self) -> None:
+        """Load Wav2Lip model and face detector."""
+        if self._loaded:
+            return
+
+        logger.info(f"Loading Wav2Lip from {self.config.wav2lip_model}")
+
+        try:
+            # Load face detection model
+            self._load_face_detector()
+
+            # Load Wav2Lip model
+            self._load_wav2lip_model()
+
+            self._loaded = True
+            logger.info("Wav2Lip loaded successfully")
+
+        except Exception as e:
+            logger.error(f"Failed to load Wav2Lip: {e}")
+            raise
+
+    def _load_face_detector(self) -> None:
+        """Load face detection model."""
+        try:
+            import face_alignment
+
+            self.face_detector = face_alignment.FaceAlignment(
+                face_alignment.LandmarksType.TWO_D,
+                device=self.config.device,
+                flip_input=False,
+            )
+        except ImportError:
+            logger.warning("face_alignment not installed, using OpenCV fallback")
+            self.face_detector = cv2.CascadeClassifier(
+                cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+            )
+
+    def _load_wav2lip_model(self) -> None:
+        """Load Wav2Lip synthesis model."""
+        from huggingface_hub import hf_hub_download
+
+        # Download model checkpoint
+        model_path = hf_hub_download(
+            repo_id=self.config.wav2lip_model,
+            filename="wav2lip.pth",
+            cache_dir=self.config.model_cache_dir,
+        )
+
+        # Load model architecture
+        from .wav2lip_model import Wav2Lip as Wav2LipModel
+
+        self.model = Wav2LipModel()
+        checkpoint = torch.load(model_path, map_location=self.config.device)
+
+        # Handle different checkpoint formats
+        if "state_dict" in checkpoint:
+            state_dict = checkpoint["state_dict"]
+        else:
+            state_dict = checkpoint
+
+        self.model.load_state_dict(state_dict)
+        self.model = self.model.to(self.config.device)
+        self.model.eval()
+
+    def unload(self) -> None:
+        """Unload models to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+        if self.face_detector is not None:
+            del self.face_detector
+            self.face_detector = None
+        self._loaded = False
+        torch.cuda.empty_cache()
+
+    async def sync_video(
+        self,
+        video: Path | str | np.ndarray,
+        audio: Path | str | np.ndarray,
+        output_path: Path | None = None,
+        audio_sample_rate: int = 16000,
+    ) -> dict:
+        """
+        Synchronize video lip movements with audio.
+
+        Args:
+            video: Input video (path or frames array)
+            audio: Translated audio (path or numpy array)
+            output_path: Optional output video path
+            audio_sample_rate: Sample rate of audio
+
+        Returns:
+            dict with output_path or video_frames
+        """
+        if not self._loaded:
+            self.load()
+
+        logger.info("Starting lip synchronization...")
+
+        # Load video frames
+        if isinstance(video, (str, Path)):
+            frames, video_fps = self._load_video(str(video))
+        else:
+            frames = video
+            video_fps = 25  # Default FPS
+
+        # Load audio
+        if isinstance(audio, (str, Path)):
+            audio_array = self._load_audio(str(audio), audio_sample_rate)
+        else:
+            audio_array = audio
+
+        # Detect faces in frames
+        face_coords = self._detect_faces(frames)
+
+        # Generate mel spectrogram from audio
+        mel = self._audio_to_mel(audio_array, audio_sample_rate)
+
+        # Generate lip-synced frames
+        synced_frames = self._generate_lip_sync(frames, face_coords, mel)
+
+        # Save or return result
+        if output_path:
+            self._save_video(synced_frames, audio_array, audio_sample_rate, video_fps, output_path)
+            return {"output_path": str(output_path), "frame_count": len(synced_frames)}
+        else:
+            return {"video_frames": synced_frames, "fps": video_fps}
+
+    async def sync_frame(
+        self,
+        frame: np.ndarray,
+        audio_chunk: np.ndarray,
+        face_coords: tuple | None = None,
+    ) -> np.ndarray:
+        """
+        Synchronize a single frame with audio chunk.
+
+        For real-time streaming applications.
+        """
+        if not self._loaded:
+            self.load()
+
+        # Detect face if coords not provided
+        if face_coords is None:
+            face_coords = self._detect_face_single(frame)
+
+        if face_coords is None:
+            return frame  # No face detected, return original
+
+        # Generate mel for audio chunk
+        mel = self._audio_to_mel(audio_chunk, sample_rate=16000)
+
+        # Sync single frame
+        synced_frame = self._sync_single_frame(frame, face_coords, mel)
+
+        return synced_frame
+
+    def _load_video(self, video_path: str) -> tuple[list[np.ndarray], float]:
+        """Load video frames."""
+        cap = cv2.VideoCapture(video_path)
+        fps = cap.get(cv2.CAP_PROP_FPS)
+
+        frames = []
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frames.append(frame)
+
+        cap.release()
+        return frames, fps
+
+    def _load_audio(self, audio_path: str, target_sr: int) -> np.ndarray:
+        """Load audio file."""
+        import librosa
+
+        audio, _ = librosa.load(audio_path, sr=target_sr)
+        return audio
+
+    def _detect_faces(self, frames: list[np.ndarray]) -> list[tuple | None]:
+        """Detect faces in all frames."""
+        face_coords = []
+
+        for frame in frames:
+            coords = self._detect_face_single(frame)
+            face_coords.append(coords)
+
+        # Interpolate missing detections
+        face_coords = self._interpolate_missing_faces(face_coords)
+
+        return face_coords
+
+    def _detect_face_single(self, frame: np.ndarray) -> tuple | None:
+        """Detect face in a single frame."""
+        if hasattr(self.face_detector, "get_landmarks"):
+            # face_alignment library
+            landmarks = self.face_detector.get_landmarks(frame)
+            if landmarks is None or len(landmarks) == 0:
+                return None
+
+            # Get bounding box from landmarks
+            landmarks = landmarks[0]
+            x_min, y_min = landmarks.min(axis=0).astype(int)
+            x_max, y_max = landmarks.max(axis=0).astype(int)
+
+            # Add padding
+            padding = int(0.2 * (x_max - x_min))
+            x_min = max(0, x_min - padding)
+            y_min = max(0, y_min - padding)
+            x_max = min(frame.shape[1], x_max + padding)
+            y_max = min(frame.shape[0], y_max + padding)
+
+            return (x_min, y_min, x_max, y_max)
+        else:
+            # OpenCV fallback
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            faces = self.face_detector.detectMultiScale(gray, 1.1, 4)
+
+            if len(faces) == 0:
+                return None
+
+            x, y, w, h = faces[0]
+            return (x, y, x + w, y + h)
+
+    def _interpolate_missing_faces(
+        self,
+        face_coords: list[tuple | None],
+    ) -> list[tuple | None]:
+        """Interpolate missing face detections."""
+        # Find first and last valid detection
+        valid_indices = [i for i, c in enumerate(face_coords) if c is not None]
+
+        if not valid_indices:
+            return face_coords
+
+        result = face_coords.copy()
+
+        # Forward fill
+        last_valid = None
+        for i, coords in enumerate(result):
+            if coords is not None:
+                last_valid = coords
+            elif last_valid is not None:
+                result[i] = last_valid
+
+        return result
+
+    def _audio_to_mel(self, audio: np.ndarray, sample_rate: int) -> np.ndarray:
+        """Convert audio to mel spectrogram."""
+        import librosa
+
+        mel = librosa.feature.melspectrogram(
+            y=audio,
+            sr=sample_rate,
+            n_mels=80,
+            n_fft=800,
+            hop_length=200,
+            win_length=800,
+        )
+        mel = librosa.power_to_db(mel, ref=np.max)
+
+        return mel.T  # Transpose for time-first format
+
+    def _generate_lip_sync(
+        self,
+        frames: list[np.ndarray],
+        face_coords: list[tuple],
+        mel: np.ndarray,
+    ) -> list[np.ndarray]:
+        """Generate lip-synced frames using Wav2Lip."""
+        batch_size = self.preset["wav2lip_batch_size"]
+        synced_frames = []
+
+        # Calculate mel frames per video frame
+        mel_idx_multiplier = len(mel) / len(frames)
+
+        for batch_start in range(0, len(frames), batch_size):
+            batch_end = min(batch_start + batch_size, len(frames))
+            batch_frames = frames[batch_start:batch_end]
+            batch_coords = face_coords[batch_start:batch_end]
+
+            # Get corresponding mel frames
+            mel_batch = []
+            for i in range(batch_start, batch_end):
+                mel_idx = int(i * mel_idx_multiplier)
+                mel_window = mel[max(0, mel_idx - 8) : mel_idx + 8]
+
+                # Pad if necessary
+                if len(mel_window) < 16:
+                    padding = np.zeros((16 - len(mel_window), mel.shape[1]))
+                    mel_window = np.vstack([mel_window, padding])
+
+                mel_batch.append(mel_window[:16])
+
+            # Process batch
+            batch_synced = self._process_batch(batch_frames, batch_coords, mel_batch)
+            synced_frames.extend(batch_synced)
+
+        return synced_frames
+
+    def _process_batch(
+        self,
+        frames: list[np.ndarray],
+        coords: list[tuple],
+        mel_batch: list[np.ndarray],
+    ) -> list[np.ndarray]:
+        """Process a batch of frames through Wav2Lip."""
+        img_size = 96  # Wav2Lip face size
+
+        # Prepare face crops
+        face_crops = []
+        for frame, coord in zip(frames, coords):
+            if coord is None:
+                face_crops.append(np.zeros((img_size, img_size, 3), dtype=np.uint8))
+            else:
+                x1, y1, x2, y2 = coord
+                face = frame[y1:y2, x1:x2]
+                face = cv2.resize(face, (img_size, img_size))
+                face_crops.append(face)
+
+        # Convert to tensors
+        face_tensor = torch.FloatTensor(np.array(face_crops)).permute(0, 3, 1, 2) / 255.0
+        mel_tensor = torch.FloatTensor(np.array(mel_batch))
+
+        face_tensor = face_tensor.to(self.config.device)
+        mel_tensor = mel_tensor.to(self.config.device)
+
+        # Generate synced faces
+        with torch.no_grad():
+            synced_faces = self.model(mel_tensor, face_tensor)
+
+        synced_faces = synced_faces.permute(0, 2, 3, 1).cpu().numpy() * 255
+        synced_faces = synced_faces.astype(np.uint8)
+
+        # Paste synced faces back into frames
+        result_frames = []
+        for i, (frame, coord) in enumerate(zip(frames, coords)):
+            if coord is None:
+                result_frames.append(frame)
+                continue
+
+            x1, y1, x2, y2 = coord
+            synced_face = cv2.resize(synced_faces[i], (x2 - x1, y2 - y1))
+
+            result = frame.copy()
+            result[y1:y2, x1:x2] = synced_face
+            result_frames.append(result)
+
+        return result_frames
+
+    def _sync_single_frame(
+        self,
+        frame: np.ndarray,
+        face_coords: tuple,
+        mel: np.ndarray,
+    ) -> np.ndarray:
+        """Sync a single frame for real-time streaming."""
+        return self._process_batch([frame], [face_coords], [mel[:16]])[0]
+
+    def _save_video(
+        self,
+        frames: list[np.ndarray],
+        audio: np.ndarray,
+        audio_sr: int,
+        fps: float,
+        output_path: Path,
+    ) -> None:
+        """Save lip-synced video with audio."""
+        import subprocess
+        import tempfile
+
+        # Save frames to temp video
+        temp_video = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+        temp_audio = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+
+        height, width = frames[0].shape[:2]
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        writer = cv2.VideoWriter(temp_video.name, fourcc, fps, (width, height))
+
+        for frame in frames:
+            writer.write(frame)
+        writer.release()
+
+        # Save audio
+        import soundfile as sf
+
+        sf.write(temp_audio.name, audio, audio_sr)
+
+        # Combine video and audio with ffmpeg
+        subprocess.run(
+            [
+                "ffmpeg",
+                "-y",
+                "-i",
+                temp_video.name,
+                "-i",
+                temp_audio.name,
+                "-c:v",
+                "libx264",
+                "-c:a",
+                "aac",
+                "-strict",
+                "experimental",
+                str(output_path),
+            ],
+            check=True,
+            capture_output=True,
+        )
+
+        # Cleanup
+        Path(temp_video.name).unlink()
+        Path(temp_audio.name).unlink()

zen_translator/lip_sync/wav2lip_model.py

@@ -0,0 +1,345 @@
+"""
+Wav2Lip neural network architecture.
+
+Based on the original Wav2Lip paper:
+"A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild"
+https://arxiv.org/abs/2008.10010
+"""
+
+import torch
+import torch.nn as nn
+
+
+class Conv2d(nn.Module):
+    """2D convolution with weight standardization option."""
+
+    def __init__(
+        self,
+        cin: int,
+        cout: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: int = 0,
+        residual: bool = False,
+    ):
+        super().__init__()
+        self.conv_block = nn.Sequential(
+            nn.Conv2d(cin, cout, kernel_size, stride, padding),
+            nn.BatchNorm2d(cout),
+        )
+        self.act = nn.ReLU()
+        self.residual = residual
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = self.conv_block(x)
+        if self.residual:
+            out += x
+        return self.act(out)
+
+
+class ConvTranspose2d(nn.Module):
+    """Transposed 2D convolution for upsampling."""
+
+    def __init__(
+        self,
+        cin: int,
+        cout: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: int = 0,
+        output_padding: int = 0,
+    ):
+        super().__init__()
+        self.conv_block = nn.Sequential(
+            nn.ConvTranspose2d(cin, cout, kernel_size, stride, padding, output_padding),
+            nn.BatchNorm2d(cout),
+        )
+        self.act = nn.ReLU()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = self.conv_block(x)
+        return self.act(out)
+
+
+class ResBlock(nn.Module):
+    """Residual block with two convolutions."""
+
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        self.block = nn.Sequential(
+            Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
+            Conv2d(out_channels, out_channels, kernel_size=3, padding=1, residual=True),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.block(x)
+
+
+class AudioEncoder(nn.Module):
+    """Encoder for mel spectrogram audio features."""
+
+    def __init__(self):
+        super().__init__()
+
+        self.audio_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
+        )
+
+    def forward(self, audio_sequences: torch.Tensor) -> torch.Tensor:
+        # audio_sequences: (batch_size, T, 1, 80, 16)
+        batch_size = audio_sequences.size(0)
+        audio_sequences = audio_sequences.view(
+            -1, 1, audio_sequences.size(3), audio_sequences.size(4)
+        )
+        audio_embedding = self.audio_encoder(audio_sequences)
+        audio_embedding = audio_embedding.view(batch_size, -1, 512, 1, 1)
+        return audio_embedding
+
+
+class FaceEncoder(nn.Module):
+    """Encoder for face image features."""
+
+    def __init__(self):
+        super().__init__()
+
+        self.face_encoder_blocks = nn.ModuleList(
+            [
+                nn.Sequential(
+                    Conv2d(6, 16, kernel_size=7, stride=1, padding=3),
+                ),  # 96, 96
+                nn.Sequential(
+                    Conv2d(16, 32, kernel_size=3, stride=2, padding=1),
+                    Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+                    Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 48, 48
+                nn.Sequential(
+                    Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
+                    Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                    Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                    Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 24, 24
+                nn.Sequential(
+                    Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                    Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+                    Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 12, 12
+                nn.Sequential(
+                    Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                    Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+                    Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 6, 6
+                nn.Sequential(
+                    Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                    Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 3, 3
+                nn.Sequential(
+                    Conv2d(512, 512, kernel_size=3, stride=1, padding=0),
+                    Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
+                ),  # 1, 1
+            ]
+        )
+
+    def forward(self, face_sequences: torch.Tensor) -> list[torch.Tensor]:
+        feats = []
+        x = face_sequences
+        for block in self.face_encoder_blocks:
+            x = block(x)
+            feats.append(x)
+        return feats
+
+
+class FaceDecoder(nn.Module):
+    """Decoder to generate lip-synced face."""
+
+    def __init__(self):
+        super().__init__()
+
+        self.face_decoder_blocks = nn.ModuleList(
+            [
+                nn.Sequential(
+                    Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
+                ),
+                nn.Sequential(
+                    ConvTranspose2d(1024, 512, kernel_size=3, stride=1, padding=0),
+                    Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 3, 3
+                nn.Sequential(
+                    ConvTranspose2d(
+                        1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1
+                    ),
+                    Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+                    Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 6, 6
+                nn.Sequential(
+                    ConvTranspose2d(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1),
+                    Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),
+                    Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 12, 12
+                nn.Sequential(
+                    ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1),
+                    Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+                    Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 24, 24
+                nn.Sequential(
+                    ConvTranspose2d(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1),
+                    Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+                    Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 48, 48
+                nn.Sequential(
+                    ConvTranspose2d(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
+                    Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                    Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                ),  # 96, 96
+            ]
+        )
+
+        self.output_block = nn.Sequential(
+            Conv2d(80, 32, kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0),
+            nn.Sigmoid(),
+        )
+
+    def forward(
+        self, audio_embedding: torch.Tensor, face_features: list[torch.Tensor]
+    ) -> torch.Tensor:
+        x = audio_embedding
+        for i, block in enumerate(self.face_decoder_blocks):
+            x = block(x)
+            if i < len(face_features):
+                # Skip connection from encoder
+                skip = face_features[-(i + 1)]
+                x = torch.cat([x, skip], dim=1)
+
+        x = self.output_block(x)
+        return x
+
+
+class Wav2Lip(nn.Module):
+    """
+    Wav2Lip model for lip synchronization.
+
+    Takes mel spectrogram audio features and face images,
+    generates lip-synced face images.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+        self.audio_encoder = AudioEncoder()
+        self.face_encoder = FaceEncoder()
+        self.face_decoder = FaceDecoder()
+
+    def forward(
+        self,
+        audio_sequences: torch.Tensor,
+        face_sequences: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Generate lip-synced faces.
+
+        Args:
+            audio_sequences: Mel spectrogram features (B, T, 1, 80, 16)
+            face_sequences: Face images (B, 6, 96, 96) - 6 channels for half face + reference
+
+        Returns:
+            Generated face images (B, 3, 96, 96)
+        """
+        # Encode audio
+        audio_embedding = self.audio_encoder(audio_sequences)
+        audio_embedding = audio_embedding.squeeze(1)  # (B, 512, 1, 1)
+
+        # Encode face
+        face_features = self.face_encoder(face_sequences)
+
+        # Decode to generate lip-synced face
+        output = self.face_decoder(audio_embedding, face_features)
+
+        return output
+
+
+class Wav2LipGAN(Wav2Lip):
+    """Wav2Lip with GAN discriminator for higher quality."""
+
+    def __init__(self):
+        super().__init__()
+
+        # Discriminator for sync detection
+        self.sync_discriminator = SyncDiscriminator()
+
+    def sync_loss(
+        self,
+        mel: torch.Tensor,
+        generated_face: torch.Tensor,
+        real_face: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Compute sync discriminator loss."""
+        # Real sync
+        real_sync = self.sync_discriminator(mel, real_face)
+        # Fake sync
+        fake_sync = self.sync_discriminator(mel, generated_face)
+
+        return real_sync, fake_sync
+
+
+class SyncDiscriminator(nn.Module):
+    """Discriminator for audio-visual sync detection."""
+
+    def __init__(self):
+        super().__init__()
+
+        self.face_encoder = nn.Sequential(
+            Conv2d(3, 32, kernel_size=7, stride=1, padding=3),
+            Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=1),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
+            nn.AdaptiveAvgPool2d((1, 1)),
+        )
+
+        self.audio_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
+            nn.AdaptiveAvgPool2d((1, 1)),
+        )
+
+        self.fc = nn.Sequential(
+            nn.Linear(1024, 512),
+            nn.ReLU(),
+            nn.Linear(512, 1),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, mel: torch.Tensor, face: torch.Tensor) -> torch.Tensor:
+        face_embedding = self.face_encoder(face)
+        face_embedding = face_embedding.view(face.size(0), -1)
+
+        audio_embedding = self.audio_encoder(mel.unsqueeze(1))
+        audio_embedding = audio_embedding.view(mel.size(0), -1)
+
+        combined = torch.cat([face_embedding, audio_embedding], dim=1)
+        return self.fc(combined)

zen_translator/pipeline.py

@@ -0,0 +1,365 @@
+"""
+Main translation pipeline orchestrating all components.
+
+Combines Qwen3-Omni, CosyVoice, and Wav2Lip for end-to-end
+real-time translation with voice cloning and lip sync.
+"""
+
+import asyncio
+import logging
+from collections.abc import AsyncIterator
+from pathlib import Path
+
+import numpy as np
+
+from .config import TranslatorConfig
+from .lip_sync import Wav2LipSync
+from .translation import Qwen3OmniTranslator
+from .voice_clone import CosyVoiceCloner, NewsAnchorVoiceBank
+
+logger = logging.getLogger(__name__)
+
+
+class TranslationPipeline:
+    """
+    End-to-end translation pipeline with voice cloning and lip sync.
+
+    Pipeline stages:
+    1. Audio/Video input → Qwen3-Omni (translation + understanding)
+    2. Translated text → CosyVoice (voice synthesis in cloned voice)
+    3. Cloned audio + Video → Wav2Lip (lip synchronization)
+
+    Total latency target: <1 second end-to-end
+    """
+
+    def __init__(self, config: TranslatorConfig | None = None):
+        self.config = config or TranslatorConfig()
+
+        # Initialize components
+        self.translator = Qwen3OmniTranslator(self.config)
+        self.voice_cloner = CosyVoiceCloner(self.config)
+        self.lip_sync = Wav2LipSync(self.config)
+
+        # News anchor voice bank
+        self.anchor_voices = NewsAnchorVoiceBank(
+            self.voice_cloner,
+            self.config.model_cache_dir / "voices" / "anchors",
+        )
+
+        self._loaded = False
+
+    async def load(self) -> None:
+        """Load all models."""
+        if self._loaded:
+            return
+
+        logger.info("Loading translation pipeline components...")
+
+        # Load models in parallel where possible
+        await asyncio.gather(
+            asyncio.to_thread(self.translator.load),
+            asyncio.to_thread(self.voice_cloner.load),
+            asyncio.to_thread(self.lip_sync.load)
+            if self.config.enable_lip_sync
+            else asyncio.sleep(0),
+        )
+
+        self._loaded = True
+        logger.info("Translation pipeline loaded successfully")
+
+    async def unload(self) -> None:
+        """Unload all models to free memory."""
+        self.translator.unload()
+        self.voice_cloner.unload()
+        self.lip_sync.unload()
+        self._loaded = False
+
+    async def translate_audio(
+        self,
+        audio: np.ndarray | Path | str,
+        source_lang: str | None = None,
+        target_lang: str | None = None,
+        speaker_id: str | None = None,
+    ) -> dict:
+        """
+        Translate audio and optionally clone voice.
+
+        Args:
+            audio: Input audio
+            source_lang: Source language (auto-detect if None)
+            target_lang: Target language
+            speaker_id: Registered speaker for voice cloning
+
+        Returns:
+            dict with text, audio, and metadata
+        """
+        if not self._loaded:
+            await self.load()
+
+        # Step 1: Translate with Qwen3-Omni
+        translation = await self.translator.translate_audio(
+            audio,
+            source_lang=source_lang,
+            target_lang=target_lang,
+            return_audio=speaker_id is None,  # Use Qwen3-Omni TTS if no cloning
+        )
+
+        result = {
+            "text": translation["text"],
+            "source_lang": translation["source_lang"],
+            "target_lang": translation["target_lang"],
+        }
+
+        # Step 2: Voice cloning (if speaker registered)
+        if speaker_id and speaker_id in self.voice_cloner.speaker_embeddings:
+            cloned = await self.voice_cloner.clone_voice(
+                text=translation["text"],
+                speaker_id=speaker_id,
+                language=target_lang or self.config.target_language,
+            )
+            result["audio"] = cloned["audio"]
+            result["sample_rate"] = cloned["sample_rate"]
+            result["speaker_id"] = speaker_id
+        elif "audio" in translation:
+            result["audio"] = translation["audio"]
+            result["sample_rate"] = translation.get("sample_rate", 24000)
+
+        return result
+
+    async def translate_video(
+        self,
+        video: Path | str,
+        source_lang: str | None = None,
+        target_lang: str | None = None,
+        speaker_id: str | None = None,
+        output_path: Path | None = None,
+    ) -> dict:
+        """
+        Translate video with lip sync.
+
+        Full pipeline:
+        1. Extract audio/video analysis with Qwen3-Omni
+        2. Translate speech to target language
+        3. Clone voice with CosyVoice
+        4. Synchronize lips with Wav2Lip
+
+        Args:
+            video: Input video path
+            source_lang: Source language
+            target_lang: Target language
+            speaker_id: Speaker for voice cloning (uses original voice profile if None)
+            output_path: Output video path
+
+        Returns:
+            dict with output path and translation details
+        """
+        if not self._loaded:
+            await self.load()
+
+        video_path = Path(video)
+
+        # Step 1: Extract and analyze video with Qwen3-Omni
+        logger.info("Analyzing video with Qwen3-Omni...")
+        translation = await self.translator.translate_video(
+            video_path,
+            source_lang=source_lang,
+            target_lang=target_lang,
+        )
+
+        result = {
+            "text": translation["text"],
+            "source_lang": translation["source_lang"],
+            "target_lang": translation["target_lang"],
+        }
+
+        # Step 2: Register speaker from original video if needed
+        if speaker_id is None:
+            # Extract voice from original video for cloning
+            speaker_id = f"video_{video_path.stem}"
+            await self._register_speaker_from_video(video_path, speaker_id)
+
+        # Step 3: Clone voice with translated text
+        logger.info(f"Cloning voice with speaker: {speaker_id}")
+        cloned = await self.voice_cloner.clone_voice(
+            text=translation["text"],
+            speaker_id=speaker_id,
+            language=target_lang or self.config.target_language,
+        )
+
+        result["audio"] = cloned["audio"]
+        result["sample_rate"] = cloned["sample_rate"]
+        result["speaker_id"] = speaker_id
+
+        # Step 4: Lip synchronization
+        if self.config.enable_lip_sync:
+            logger.info("Synchronizing lips with Wav2Lip...")
+
+            if output_path is None:
+                output_path = video_path.parent / f"{video_path.stem}_translated.mp4"
+
+            lip_result = await self.lip_sync.sync_video(
+                video=video_path,
+                audio=cloned["audio"],
+                output_path=output_path,
+                audio_sample_rate=cloned["sample_rate"],
+            )
+
+            result["output_path"] = lip_result["output_path"]
+            result["frame_count"] = lip_result.get("frame_count")
+
+        return result
+
+    async def stream_translate(
+        self,
+        audio_stream: AsyncIterator[np.ndarray],
+        source_lang: str | None = None,
+        target_lang: str | None = None,
+        speaker_id: str | None = None,
+    ) -> AsyncIterator[dict]:
+        """
+        Stream translation for real-time applications.
+
+        Yields translation chunks as they become available.
+        Target first-packet latency: <500ms
+        """
+        if not self._loaded:
+            await self.load()
+
+        # Create streaming translation
+        async for translation_chunk in self.translator.stream_translate(
+            audio_stream,
+            source_lang=source_lang,
+            target_lang=target_lang,
+        ):
+            # Clone voice for this chunk
+            if speaker_id and speaker_id in self.voice_cloner.speaker_embeddings:
+                async for voice_chunk in self.voice_cloner.stream_clone(
+                    self._text_chunks(translation_chunk["text"]),
+                    speaker_id=speaker_id,
+                    language=target_lang or self.config.target_language,
+                ):
+                    yield {
+                        "text": voice_chunk["text"],
+                        "audio": voice_chunk["audio"],
+                        "sample_rate": voice_chunk["sample_rate"],
+                        "source_lang": translation_chunk.get("source_lang"),
+                        "target_lang": translation_chunk.get("target_lang"),
+                    }
+            else:
+                yield translation_chunk
+
+    async def _text_chunks(self, text: str) -> AsyncIterator[str]:
+        """Convert text to async iterator of chunks."""
+        yield text
+
+    async def _register_speaker_from_video(
+        self,
+        video_path: Path,
+        speaker_id: str,
+    ) -> None:
+        """Extract and register speaker voice from video."""
+        import subprocess
+        import tempfile
+
+        # Extract audio from video
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+            temp_audio = f.name
+
+        subprocess.run(
+            [
+                "ffmpeg",
+                "-y",
+                "-i",
+                str(video_path),
+                "-vn",  # No video
+                "-acodec",
+                "pcm_s16le",
+                "-ar",
+                "16000",
+                "-ac",
+                "1",
+                temp_audio,
+            ],
+            check=True,
+            capture_output=True,
+        )
+
+        # Register speaker
+        await self.voice_cloner.register_speaker(
+            speaker_id=speaker_id,
+            reference_audio=temp_audio,
+            sample_rate=16000,
+        )
+
+        # Cleanup
+        Path(temp_audio).unlink()
+
+    async def register_speaker(
+        self,
+        speaker_id: str,
+        reference_audio: np.ndarray | Path | str,
+        sample_rate: int = 16000,
+    ) -> dict:
+        """Register a speaker for voice cloning."""
+        return await self.voice_cloner.register_speaker(
+            speaker_id=speaker_id,
+            reference_audio=reference_audio,
+            sample_rate=sample_rate,
+        )
+
+    async def load_news_anchors(self) -> dict[str, bool]:
+        """Load all pre-registered news anchor voices."""
+        return await self.anchor_voices.load_all_voices()
+
+    def get_supported_languages(self) -> dict:
+        """Get supported input and output languages."""
+        return {
+            "input": self.config.supported_input_languages,
+            "output": self.config.supported_output_languages,
+        }
+
+
+class BatchTranslationPipeline(TranslationPipeline):
+    """Pipeline optimized for batch processing."""
+
+    async def translate_batch(
+        self,
+        items: list[dict],
+        parallel_workers: int = 4,
+    ) -> list[dict]:
+        """
+        Translate multiple items in parallel.
+
+        Args:
+            items: List of dicts with 'audio' or 'video' keys
+            parallel_workers: Number of parallel workers
+
+        Returns:
+            List of translation results
+        """
+        semaphore = asyncio.Semaphore(parallel_workers)
+
+        async def process_item(item: dict) -> dict:
+            async with semaphore:
+                if "video" in item:
+                    return await self.translate_video(
+                        video=item["video"],
+                        source_lang=item.get("source_lang"),
+                        target_lang=item.get("target_lang"),
+                        speaker_id=item.get("speaker_id"),
+                        output_path=item.get("output_path"),
+                    )
+                else:
+                    return await self.translate_audio(
+                        audio=item["audio"],
+                        source_lang=item.get("source_lang"),
+                        target_lang=item.get("target_lang"),
+                        speaker_id=item.get("speaker_id"),
+                    )
+
+        results = await asyncio.gather(
+            *[process_item(item) for item in items],
+            return_exceptions=True,
+        )
+
+        return [r if not isinstance(r, Exception) else {"error": str(r)} for r in results]

zen_translator/streaming/__init__.py

@@ -0,0 +1,5 @@
+"""Real-time streaming server for Zen Translator."""
+
+from .server import TranslationServer, create_app
+
+__all__ = ["TranslationServer", "create_app"]

zen_translator/streaming/server.py

@@ -0,0 +1,290 @@
+"""
+Real-time streaming translation server.
+
+Provides WebSocket and REST APIs for:
+- Real-time audio translation
+- Video translation with lip sync
+- Voice cloning management
+- WebRTC integration
+"""
+
+import logging
+from contextlib import asynccontextmanager
+from pathlib import Path
+
+import numpy as np
+from fastapi import FastAPI, File, Form, UploadFile, WebSocket, WebSocketDisconnect
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse
+from pydantic import BaseModel
+
+from ..config import TranslatorConfig
+from ..pipeline import TranslationPipeline
+
+logger = logging.getLogger(__name__)
+
+
+class TranslationRequest(BaseModel):
+    """Request for text-based translation."""
+
+    text: str
+    source_lang: str | None = None
+    target_lang: str = "en"
+    speaker_id: str | None = None
+
+
+class SpeakerRegistration(BaseModel):
+    """Request to register a speaker for voice cloning."""
+
+    speaker_id: str
+
+
+class TranslationResponse(BaseModel):
+    """Response from translation."""
+
+    text: str
+    source_lang: str
+    target_lang: str
+    speaker_id: str | None = None
+    audio_url: str | None = None
+
+
+class TranslationServer:
+    """Main translation server."""
+
+    def __init__(self, config: TranslatorConfig | None = None):
+        self.config = config or TranslatorConfig()
+        self.pipeline = TranslationPipeline(self.config)
+        self.active_connections: list[WebSocket] = []
+
+    async def startup(self) -> None:
+        """Initialize server and load models."""
+        logger.info("Starting translation server...")
+        await self.pipeline.load()
+        logger.info("Server ready")
+
+    async def shutdown(self) -> None:
+        """Cleanup on shutdown."""
+        logger.info("Shutting down server...")
+        await self.pipeline.unload()
+
+
+# Global server instance
+_server: TranslationServer | None = None
+
+
+def get_server() -> TranslationServer:
+    """Get the global server instance."""
+    global _server
+    if _server is None:
+        _server = TranslationServer()
+    return _server
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Application lifespan manager."""
+    server = get_server()
+    await server.startup()
+    yield
+    await server.shutdown()
+
+
+def create_app() -> FastAPI:
+    """Create and configure the FastAPI application."""
+
+    app = FastAPI(
+        title="Zen Translator API",
+        description="Real-time multimodal translation with voice cloning and lip sync",
+        version="0.1.0",
+        lifespan=lifespan,
+    )
+
+    # CORS middleware
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    # Health check
+    @app.get("/health")
+    async def health_check():
+        return {"status": "healthy", "version": "0.1.0"}
+
+    # Translation endpoints
+    @app.post("/translate/audio", response_model=TranslationResponse)
+    async def translate_audio(
+        audio: UploadFile = File(...),
+        source_lang: str | None = Form(None),
+        target_lang: str = Form("en"),
+        speaker_id: str | None = Form(None),
+    ):
+        """Translate audio file."""
+        server = get_server()
+
+        # Read audio file
+        audio_bytes = await audio.read()
+        audio_array = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32768.0
+
+        result = await server.pipeline.translate_audio(
+            audio=audio_array,
+            source_lang=source_lang,
+            target_lang=target_lang,
+            speaker_id=speaker_id,
+        )
+
+        # Save audio to temp file if present
+        audio_url = None
+        if "audio" in result:
+            import tempfile
+
+            import soundfile as sf
+
+            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+                sf.write(f.name, result["audio"], result["sample_rate"])
+                audio_url = f"/audio/{Path(f.name).name}"
+
+        return TranslationResponse(
+            text=result["text"],
+            source_lang=result["source_lang"],
+            target_lang=result["target_lang"],
+            speaker_id=result.get("speaker_id"),
+            audio_url=audio_url,
+        )
+
+    @app.post("/translate/video")
+    async def translate_video(
+        video: UploadFile = File(...),
+        source_lang: str | None = Form(None),
+        target_lang: str = Form("en"),
+        speaker_id: str | None = Form(None),
+    ):
+        """Translate video with lip sync."""
+        server = get_server()
+
+        # Save uploaded video
+        import tempfile
+
+        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as f:
+            video_path = Path(f.name)
+            f.write(await video.read())
+
+        output_path = video_path.parent / f"{video_path.stem}_translated.mp4"
+
+        result = await server.pipeline.translate_video(
+            video=video_path,
+            source_lang=source_lang,
+            target_lang=target_lang,
+            speaker_id=speaker_id,
+            output_path=output_path,
+        )
+
+        # Cleanup input
+        video_path.unlink()
+
+        return FileResponse(
+            result["output_path"],
+            media_type="video/mp4",
+            filename="translated_video.mp4",
+        )
+
+    @app.post("/speakers/register")
+    async def register_speaker(
+        speaker_id: str = Form(...),
+        audio: UploadFile = File(...),
+    ):
+        """Register a speaker for voice cloning."""
+        server = get_server()
+
+        # Read audio
+        audio_bytes = await audio.read()
+        audio_array = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32768.0
+
+        result = await server.pipeline.register_speaker(
+            speaker_id=speaker_id,
+            reference_audio=audio_array,
+        )
+
+        return result
+
+    @app.get("/speakers")
+    async def list_speakers():
+        """List registered speakers."""
+        server = get_server()
+        return {"speakers": server.pipeline.voice_cloner.list_speakers()}
+
+    @app.get("/languages")
+    async def get_languages():
+        """Get supported languages."""
+        server = get_server()
+        return server.pipeline.get_supported_languages()
+
+    # WebSocket for real-time streaming
+    @app.websocket("/ws/translate")
+    async def websocket_translate(websocket: WebSocket):
+        """WebSocket endpoint for real-time translation."""
+        server = get_server()
+        await websocket.accept()
+        server.active_connections.append(websocket)
+
+        try:
+            # Receive configuration
+            config_data = await websocket.receive_json()
+            source_lang = config_data.get("source_lang")
+            target_lang = config_data.get("target_lang", "en")
+            speaker_id = config_data.get("speaker_id")
+
+            await websocket.send_json({"status": "ready", "message": "Send audio chunks"})
+
+            # Create audio stream
+            async def audio_generator():
+                while True:
+                    try:
+                        data = await websocket.receive_bytes()
+                        audio = np.frombuffer(data, dtype=np.int16).astype(np.float32) / 32768.0
+                        yield audio
+                    except WebSocketDisconnect:
+                        break
+
+            # Stream translation
+            async for result in server.pipeline.stream_translate(
+                audio_stream=audio_generator(),
+                source_lang=source_lang,
+                target_lang=target_lang,
+                speaker_id=speaker_id,
+            ):
+                # Send text
+                await websocket.send_json(
+                    {
+                        "type": "text",
+                        "text": result["text"],
+                    }
+                )
+
+                # Send audio
+                if "audio" in result:
+                    audio_bytes = (result["audio"] * 32768).astype(np.int16).tobytes()
+                    await websocket.send_bytes(audio_bytes)
+
+        except WebSocketDisconnect:
+            logger.info("WebSocket disconnected")
+        finally:
+            server.active_connections.remove(websocket)
+
+    return app
+
+
+# CLI entry point
+def main():
+    """Run the translation server."""
+    import uvicorn
+
+    app = create_app()
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+
+
+if __name__ == "__main__":
+    main()

zen_translator/training/__init__.py

@@ -0,0 +1,27 @@
+"""Training infrastructure for Zen Translator."""
+
+from .news_anchor_dataset import (
+    NEWS_CHANNELS,
+    NewsAnchorDatasetBuilder,
+    NewsAnchorSample,
+    build_news_anchor_dataset,
+)
+from .swift_config import (
+    NewsAnchorConfig,
+    SwiftTrainingConfig,
+    ZenIdentityConfig,
+    create_training_dataset,
+    generate_identity_dataset,
+)
+
+__all__ = [
+    "SwiftTrainingConfig",
+    "ZenIdentityConfig",
+    "NewsAnchorConfig",
+    "create_training_dataset",
+    "generate_identity_dataset",
+    "NewsAnchorDatasetBuilder",
+    "NewsAnchorSample",
+    "NEWS_CHANNELS",
+    "build_news_anchor_dataset",
+]

zen_translator/training/news_anchor_dataset.py

@@ -0,0 +1,418 @@
+"""
+News anchor dataset collection and processing pipeline.
+
+Collects, processes, and prepares news anchor audio/video data
+for finetuning Zen Translator for accurate broadcast translation.
+"""
+
+import json
+import logging
+import re
+from collections.abc import AsyncIterator
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+
+from ..config import NewsAnchorConfig
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class NewsAnchorSample:
+    """A single news anchor audio/video sample."""
+
+    anchor_id: str
+    audio_path: Path
+    video_path: Path | None
+    transcript: str
+    language: str
+    duration_seconds: float
+    news_domain: str
+    timestamp: datetime
+    source_url: str | None = None
+
+    def to_dict(self) -> dict:
+        return {
+            "anchor_id": self.anchor_id,
+            "audio_path": str(self.audio_path),
+            "video_path": str(self.video_path) if self.video_path else None,
+            "transcript": self.transcript,
+            "language": self.language,
+            "duration_seconds": self.duration_seconds,
+            "news_domain": self.news_domain,
+            "timestamp": self.timestamp.isoformat(),
+            "source_url": self.source_url,
+        }
+
+
+class NewsAnchorDatasetBuilder:
+    """
+    Builds training datasets from news anchor recordings.
+
+    Pipeline:
+    1. Collect audio/video from news sources
+    2. Extract and transcribe speech
+    3. Segment into training samples
+    4. Create translation pairs
+    5. Export in ms-swift format
+    """
+
+    def __init__(self, config: NewsAnchorConfig):
+        self.config = config
+        self.samples: list[NewsAnchorSample] = []
+
+    async def collect_from_youtube(
+        self,
+        channel_urls: list[str],
+        max_videos_per_channel: int = 10,
+    ) -> AsyncIterator[NewsAnchorSample]:
+        """
+        Collect news anchor data from YouTube channels.
+
+        Supports channels like:
+        - CNN, BBC News, NHK World, DW News, etc.
+        """
+        try:
+            import yt_dlp
+        except ImportError:
+            logger.error("yt-dlp not installed. Run: pip install yt-dlp")
+            return
+
+        output_dir = self.config.dataset_dir / "raw" / "youtube"
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        ydl_opts = {
+            "format": "bestvideo[height<=720]+bestaudio/best[height<=720]",
+            "outtmpl": str(output_dir / "%(channel)s/%(id)s.%(ext)s"),
+            "writesubtitles": True,
+            "writeautomaticsub": True,
+            "subtitleslangs": ["en", "zh", "ja", "ko", "es", "fr", "de"],
+            "postprocessors": [
+                {
+                    "key": "FFmpegExtractAudio",
+                    "preferredcodec": "wav",
+                    "preferredquality": "192",
+                },
+            ],
+            "max_downloads": max_videos_per_channel,
+        }
+
+        for channel_url in channel_urls:
+            try:
+                with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+                    info = ydl.extract_info(channel_url, download=True)
+
+                    for entry in info.get("entries", []):
+                        if entry is None:
+                            continue
+
+                        video_id = entry["id"]
+                        channel_name = entry.get("channel", "unknown")
+
+                        # Find downloaded files
+                        audio_path = output_dir / channel_name / f"{video_id}.wav"
+                        video_path = output_dir / channel_name / f"{video_id}.mp4"
+
+                        if not audio_path.exists():
+                            continue
+
+                        # Get transcript from subtitles
+                        transcript = await self._extract_transcript(
+                            entry, output_dir / channel_name
+                        )
+
+                        sample = NewsAnchorSample(
+                            anchor_id=channel_name.lower().replace(" ", "_"),
+                            audio_path=audio_path,
+                            video_path=video_path if video_path.exists() else None,
+                            transcript=transcript,
+                            language=entry.get("language", "en"),
+                            duration_seconds=entry.get("duration", 0),
+                            news_domain=self._detect_news_domain(entry.get("title", "")),
+                            timestamp=datetime.now(),
+                            source_url=entry.get("webpage_url"),
+                        )
+
+                        self.samples.append(sample)
+                        yield sample
+
+            except Exception as e:
+                logger.error(f"Error collecting from {channel_url}: {e}")
+
+    async def _extract_transcript(self, entry: dict, output_dir: Path) -> str:
+        """Extract transcript from video subtitles."""
+        video_id = entry["id"]
+
+        # Try different subtitle formats
+        for ext in [".en.vtt", ".en.srt", ".vtt", ".srt"]:
+            sub_path = output_dir / f"{video_id}{ext}"
+            if sub_path.exists():
+                return self._parse_subtitle_file(sub_path)
+
+        # Fallback to auto-generated transcript
+        return entry.get("description", "")[:500]
+
+    def _parse_subtitle_file(self, path: Path) -> str:
+        """Parse VTT or SRT subtitle file."""
+        content = path.read_text()
+
+        # Remove timing information and formatting
+        lines = []
+        for line in content.split("\n"):
+            # Skip timing lines
+            if re.match(r"^\d+:\d+", line) or re.match(r"^\d+$", line):
+                continue
+            # Skip WebVTT header
+            if line.startswith("WEBVTT") or line.startswith("Kind:"):
+                continue
+            # Clean HTML tags
+            line = re.sub(r"<[^>]+>", "", line)
+            if line.strip():
+                lines.append(line.strip())
+
+        return " ".join(lines)
+
+    def _detect_news_domain(self, title: str) -> str:
+        """Detect news domain from video title."""
+        title_lower = title.lower()
+
+        domain_keywords = {
+            "politics": ["election", "vote", "congress", "parliament", "president", "minister"],
+            "economics": ["economy", "market", "stock", "trade", "inflation", "gdp"],
+            "technology": ["tech", "ai", "software", "startup", "digital", "cyber"],
+            "sports": ["game", "match", "championship", "olympics", "team", "player"],
+            "weather": ["weather", "storm", "hurricane", "temperature", "forecast"],
+            "breaking_news": ["breaking", "urgent", "just in", "developing"],
+            "international": ["world", "global", "international", "foreign"],
+        }
+
+        for domain, keywords in domain_keywords.items():
+            if any(kw in title_lower for kw in keywords):
+                return domain
+
+        return "general"
+
+    async def segment_samples(
+        self,
+        min_duration: float = 5.0,
+        max_duration: float = 30.0,
+    ) -> list[NewsAnchorSample]:
+        """Segment long recordings into training-sized chunks."""
+        import librosa
+
+        segmented = []
+
+        for sample in self.samples:
+            if sample.duration_seconds <= max_duration:
+                if sample.duration_seconds >= min_duration:
+                    segmented.append(sample)
+                continue
+
+            # Load audio
+            audio, sr = librosa.load(str(sample.audio_path), sr=16000)
+
+            # Split into chunks
+            chunk_samples = int(max_duration * sr)
+            hop_samples = int(chunk_samples * 0.8)  # 20% overlap
+
+            for i, start in enumerate(range(0, len(audio) - chunk_samples, hop_samples)):
+                chunk = audio[start : start + chunk_samples]
+
+                # Save chunk
+                chunk_path = sample.audio_path.parent / f"{sample.audio_path.stem}_chunk{i}.wav"
+                import soundfile as sf
+
+                sf.write(str(chunk_path), chunk, sr)
+
+                # Create new sample
+                chunk_sample = NewsAnchorSample(
+                    anchor_id=sample.anchor_id,
+                    audio_path=chunk_path,
+                    video_path=None,  # Video segmentation is more complex
+                    transcript=f"[Chunk {i}] {sample.transcript}",  # Would need alignment
+                    language=sample.language,
+                    duration_seconds=max_duration,
+                    news_domain=sample.news_domain,
+                    timestamp=sample.timestamp,
+                    source_url=sample.source_url,
+                )
+                segmented.append(chunk_sample)
+
+        self.samples = segmented
+        return segmented
+
+    async def create_translation_pairs(
+        self,
+        target_languages: list[str] = ["en", "zh", "ja", "es"],
+    ) -> list[dict]:
+        """Create translation pairs for training."""
+        from ..config import TranslatorConfig
+        from ..translation import Qwen3OmniTranslator
+
+        config = TranslatorConfig()
+        translator = Qwen3OmniTranslator(config)
+        translator.load()
+
+        pairs = []
+
+        for sample in self.samples:
+            for target_lang in target_languages:
+                if target_lang == sample.language:
+                    continue
+
+                # Translate transcript
+                try:
+                    # For actual training, we'd use actual audio translation
+                    # Here we show the data format
+                    pairs.append(
+                        {
+                            "conversations": [
+                                {
+                                    "role": "system",
+                                    "content": f"You are Zen Translator. Translate the speech to {target_lang}.",
+                                },
+                                {
+                                    "role": "user",
+                                    "content": [
+                                        {"type": "audio", "audio": str(sample.audio_path)},
+                                        {"type": "text", "text": f"Translate to {target_lang}."},
+                                    ],
+                                },
+                                {
+                                    "role": "assistant",
+                                    "content": f"[{target_lang}] {sample.transcript}",  # Placeholder
+                                },
+                            ],
+                            "metadata": {
+                                "anchor_id": sample.anchor_id,
+                                "source_lang": sample.language,
+                                "target_lang": target_lang,
+                                "domain": sample.news_domain,
+                            },
+                        }
+                    )
+                except Exception as e:
+                    logger.error(f"Error creating pair: {e}")
+
+        return pairs
+
+    async def export_dataset(
+        self,
+        output_path: Path,
+        format: str = "jsonl",
+        split_ratio: tuple[float, float, float] = (0.8, 0.1, 0.1),
+    ) -> dict[str, Path]:
+        """
+        Export dataset for ms-swift training.
+
+        Returns paths to train/val/test splits.
+        """
+        import random
+
+        pairs = await self.create_translation_pairs()
+        random.shuffle(pairs)
+
+        n = len(pairs)
+        train_end = int(n * split_ratio[0])
+        val_end = train_end + int(n * split_ratio[1])
+
+        splits = {
+            "train": pairs[:train_end],
+            "val": pairs[train_end:val_end],
+            "test": pairs[val_end:],
+        }
+
+        output_path.mkdir(parents=True, exist_ok=True)
+        paths = {}
+
+        for split_name, split_data in splits.items():
+            split_path = output_path / f"{split_name}.jsonl"
+
+            with open(split_path, "w") as f:
+                for item in split_data:
+                    f.write(json.dumps(item, ensure_ascii=False) + "\n")
+
+            paths[split_name] = split_path
+            logger.info(f"Exported {len(split_data)} samples to {split_path}")
+
+        # Save metadata
+        metadata = {
+            "total_samples": len(pairs),
+            "splits": {k: len(v) for k, v in splits.items()},
+            "anchors": list(set(s.anchor_id for s in self.samples)),
+            "languages": list(set(s.language for s in self.samples)),
+            "domains": list(set(s.news_domain for s in self.samples)),
+            "created": datetime.now().isoformat(),
+        }
+
+        with open(output_path / "metadata.json", "w") as f:
+            json.dump(metadata, f, indent=2)
+
+        return paths
+
+
+# Predefined news channel URLs for data collection
+NEWS_CHANNELS = {
+    "cnn": "https://www.youtube.com/@CNN",
+    "bbc": "https://www.youtube.com/@BBCNews",
+    "nhk": "https://www.youtube.com/@NHKWORLDJAPAN",
+    "dw": "https://www.youtube.com/@DWNews",
+    "france24_en": "https://www.youtube.com/@FRANCE24English",
+    "aljazeera": "https://www.youtube.com/@AlJazeeraEnglish",
+    "sky": "https://www.youtube.com/@SkyNews",
+    "reuters": "https://www.youtube.com/@Reuters",
+    "ap": "https://www.youtube.com/@AssociatedPress",
+    "bloomberg": "https://www.youtube.com/@BloombergTelevision",
+    # Non-English channels
+    "cctv": "https://www.youtube.com/@CCTVVideoNewsAgency",
+    "nhk_ja": "https://www.youtube.com/@NHK",
+    "tbs_ja": "https://www.youtube.com/@tbsnewsdig",
+    "kbs_ko": "https://www.youtube.com/@KBSNews",
+    "tvn_ko": "https://www.youtube.com/@tvaborigen",
+}
+
+
+async def build_news_anchor_dataset(
+    output_dir: Path,
+    channels: list[str] | None = None,
+    max_videos_per_channel: int = 10,
+) -> Path:
+    """
+    Convenience function to build a news anchor dataset.
+
+    Args:
+        output_dir: Output directory for dataset
+        channels: List of channel keys from NEWS_CHANNELS
+        max_videos_per_channel: Max videos to download per channel
+
+    Returns:
+        Path to the created dataset
+    """
+    from ..config import NewsAnchorConfig
+
+    config = NewsAnchorConfig()
+    config.dataset_dir = output_dir
+
+    builder = NewsAnchorDatasetBuilder(config)
+
+    # Select channels
+    if channels is None:
+        channels = ["cnn", "bbc", "nhk", "dw"]
+
+    channel_urls = [NEWS_CHANNELS[c] for c in channels if c in NEWS_CHANNELS]
+
+    # Collect data
+    logger.info(f"Collecting from {len(channel_urls)} channels...")
+    async for sample in builder.collect_from_youtube(channel_urls, max_videos_per_channel):
+        logger.info(f"Collected: {sample.anchor_id} - {sample.duration_seconds:.1f}s")
+
+    # Segment
+    logger.info("Segmenting samples...")
+    await builder.segment_samples()
+
+    # Export
+    logger.info("Exporting dataset...")
+    await builder.export_dataset(output_dir / "processed")
+
+    return output_dir / "processed"

zen_translator/training/swift_config.py

@@ -0,0 +1,289 @@
+"""
+ms-swift finetuning configuration for Zen Translator.
+
+Supports:
+- Qwen3-Omni identity finetuning
+- News anchor voice adaptation
+- Translation quality improvement
+"""
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Literal
+
+import yaml
+
+
+@dataclass
+class SwiftTrainingConfig:
+    """Configuration for ms-swift training."""
+
+    # Model configuration
+    model_type: str = "qwen3-omni"
+    model_id_or_path: str = "Qwen/Qwen3-Omni-30B-A3B-Instruct"
+
+    # Training method
+    train_type: Literal["lora", "full", "longlora", "adalora"] = "lora"
+
+    # LoRA configuration
+    lora_rank: int = 64
+    lora_alpha: int = 128
+    lora_dropout: float = 0.05
+    lora_target_modules: list[str] = field(
+        default_factory=lambda: [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+            "o_proj",
+            "gate_proj",
+            "up_proj",
+            "down_proj",
+        ]
+    )
+
+    # Training hyperparameters
+    num_train_epochs: int = 3
+    per_device_train_batch_size: int = 1
+    gradient_accumulation_steps: int = 16
+    learning_rate: float = 2e-5
+    lr_scheduler_type: str = "cosine"
+    warmup_ratio: float = 0.1
+    weight_decay: float = 0.01
+
+    # Optimization
+    optim: str = "adamw_torch"
+    bf16: bool = True
+    fp16: bool = False
+    gradient_checkpointing: bool = True
+    flash_attn: bool = True
+
+    # Data configuration
+    dataset_path: str = "./data/training"
+    max_length: int = 8192
+    truncation_strategy: str = "delete"
+
+    # Output
+    output_dir: str = "./outputs/zen-translator"
+    logging_steps: int = 10
+    save_strategy: str = "steps"
+    save_steps: int = 100
+    save_total_limit: int = 3
+
+    # Evaluation
+    eval_strategy: str = "steps"
+    eval_steps: int = 100
+
+    # DeepSpeed (for multi-GPU)
+    deepspeed: str | None = None
+
+    def to_swift_args(self) -> list[str]:
+        """Convert to ms-swift command line arguments."""
+        args = [
+            f"--model_type={self.model_type}",
+            f"--model_id_or_path={self.model_id_or_path}",
+            f"--train_type={self.train_type}",
+            f"--lora_rank={self.lora_rank}",
+            f"--lora_alpha={self.lora_alpha}",
+            f"--lora_dropout={self.lora_dropout}",
+            f"--lora_target_modules={','.join(self.lora_target_modules)}",
+            f"--num_train_epochs={self.num_train_epochs}",
+            f"--per_device_train_batch_size={self.per_device_train_batch_size}",
+            f"--gradient_accumulation_steps={self.gradient_accumulation_steps}",
+            f"--learning_rate={self.learning_rate}",
+            f"--lr_scheduler_type={self.lr_scheduler_type}",
+            f"--warmup_ratio={self.warmup_ratio}",
+            f"--weight_decay={self.weight_decay}",
+            f"--optim={self.optim}",
+            f"--gradient_checkpointing={str(self.gradient_checkpointing).lower()}",
+            f"--flash_attn={str(self.flash_attn).lower()}",
+            f"--dataset={self.dataset_path}",
+            f"--max_length={self.max_length}",
+            f"--truncation_strategy={self.truncation_strategy}",
+            f"--output_dir={self.output_dir}",
+            f"--logging_steps={self.logging_steps}",
+            f"--save_strategy={self.save_strategy}",
+            f"--save_steps={self.save_steps}",
+            f"--save_total_limit={self.save_total_limit}",
+            f"--eval_strategy={self.eval_strategy}",
+            f"--eval_steps={self.eval_steps}",
+        ]
+
+        if self.bf16:
+            args.append("--bf16=true")
+        if self.deepspeed:
+            args.append(f"--deepspeed={self.deepspeed}")
+
+        return args
+
+    def to_yaml(self, path: Path) -> None:
+        """Save configuration to YAML file."""
+        config_dict = {
+            "model": {
+                "type": self.model_type,
+                "id_or_path": self.model_id_or_path,
+            },
+            "training": {
+                "type": self.train_type,
+                "epochs": self.num_train_epochs,
+                "batch_size": self.per_device_train_batch_size,
+                "gradient_accumulation": self.gradient_accumulation_steps,
+                "learning_rate": self.learning_rate,
+                "scheduler": self.lr_scheduler_type,
+                "warmup_ratio": self.warmup_ratio,
+            },
+            "lora": {
+                "rank": self.lora_rank,
+                "alpha": self.lora_alpha,
+                "dropout": self.lora_dropout,
+                "target_modules": self.lora_target_modules,
+            },
+            "data": {
+                "path": self.dataset_path,
+                "max_length": self.max_length,
+            },
+            "output": {
+                "dir": self.output_dir,
+                "save_steps": self.save_steps,
+            },
+        }
+
+        with open(path, "w") as f:
+            yaml.dump(config_dict, f, default_flow_style=False)
+
+
+@dataclass
+class ZenIdentityConfig(SwiftTrainingConfig):
+    """Configuration specifically for Zen identity finetuning."""
+
+    # Identity-specific settings
+    system_prompt: str = """You are Zen Translator, a real-time multilingual translation system created by Hanzo AI.
+
+Your core capabilities:
+- Real-time speech translation across 18 input languages and 10 output languages
+- Voice cloning to preserve speaker characteristics
+- Visual context understanding for improved accuracy
+- News anchor voice adaptation for broadcast-quality translation
+
+Personality traits:
+- Professional and precise
+- Culturally aware in translations
+- Natural and fluent in all supported languages
+- Maintains speaker intent and emotion"""
+
+    def __post_init__(self):
+        self.output_dir = "./outputs/zen-translator-identity"
+
+
+@dataclass
+class NewsAnchorConfig(SwiftTrainingConfig):
+    """Configuration for news anchor voice finetuning."""
+
+    # News anchor specific settings
+    anchor_names: list[str] = field(
+        default_factory=lambda: [
+            "cnn",
+            "bbc",
+            "nhk",
+            "dw",
+            "france24",
+            "aljazeera",
+            "sky",
+            "reuters",
+            "ap",
+            "bloomberg",
+        ]
+    )
+
+    # Focus on translation accuracy for news content
+    news_domains: list[str] = field(
+        default_factory=lambda: [
+            "politics",
+            "economics",
+            "technology",
+            "sports",
+            "weather",
+            "breaking_news",
+            "international",
+        ]
+    )
+
+    def __post_init__(self):
+        self.output_dir = "./outputs/zen-translator-anchor"
+        # Increase training for domain adaptation
+        self.num_train_epochs = 5
+
+
+def create_training_dataset(
+    conversations: list[dict],
+    output_path: Path,
+    format: Literal["jsonl", "json"] = "jsonl",
+) -> None:
+    """
+    Create training dataset in ms-swift format.
+
+    Expected conversation format:
+    {
+        "conversations": [
+            {"role": "system", "content": "..."},
+            {"role": "user", "content": [
+                {"type": "audio", "audio": "path/to/audio.wav"},
+                {"type": "text", "text": "Translate this to Spanish"}
+            ]},
+            {"role": "assistant", "content": "Translation..."}
+        ]
+    }
+    """
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    if format == "jsonl":
+        with open(output_path, "w") as f:
+            for conv in conversations:
+                f.write(json.dumps(conv, ensure_ascii=False) + "\n")
+    else:
+        with open(output_path, "w") as f:
+            json.dump(conversations, f, indent=2, ensure_ascii=False)
+
+
+def generate_identity_dataset(
+    output_path: Path,
+    num_samples: int = 1000,
+) -> None:
+    """Generate identity training dataset for Zen Translator."""
+
+    system_prompt = ZenIdentityConfig().system_prompt
+
+    # Sample identity conversations
+    identity_templates = [
+        {
+            "user": "Who are you?",
+            "assistant": "I'm Zen Translator, a real-time multilingual translation system created by Hanzo AI. I specialize in translating speech across 18 languages with voice cloning and lip synchronization capabilities.",
+        },
+        {
+            "user": "What languages do you support?",
+            "assistant": "I support 18 input languages including English, Chinese, Japanese, Korean, Spanish, French, German, and various Chinese dialects. For output, I can translate to 10 languages with natural voice synthesis.",
+        },
+        {
+            "user": "Can you clone voices?",
+            "assistant": "Yes, with just 3 seconds of reference audio, I can clone a speaker's voice and preserve their unique characteristics, emotion, and inflection patterns in the translated output.",
+        },
+        {
+            "user": "How do you handle noisy audio?",
+            "assistant": "I use visual context from video streams - lip movements, gestures, and on-screen text - to enhance translation accuracy even in noisy environments. This multimodal approach helps me disambiguate unclear audio.",
+        },
+    ]
+
+    conversations = []
+    for i in range(num_samples):
+        template = identity_templates[i % len(identity_templates)]
+        conversations.append(
+            {
+                "conversations": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": template["user"]},
+                    {"role": "assistant", "content": template["assistant"]},
+                ]
+            }
+        )
+
+    create_training_dataset(conversations, output_path)

zen_translator/translation/__init__.py

@@ -0,0 +1,5 @@
+"""Translation module using Qwen3-Omni."""
+
+from .qwen3_omni import Qwen3OmniTranslator
+
+__all__ = ["Qwen3OmniTranslator"]

zen_translator/translation/qwen3_omni.py

@@ -0,0 +1,351 @@
+"""
+Qwen3-Omni translation module.
+
+Real-time multimodal translation using Qwen3-Omni-30B-A3B.
+Supports audio, video, and text input with real-time speech output.
+"""
+
+import logging
+from collections.abc import AsyncIterator
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import numpy as np
+import torch
+from transformers import AutoProcessor
+
+from ..config import TranslatorConfig
+
+if TYPE_CHECKING:
+    pass
+
+logger = logging.getLogger(__name__)
+
+# Lazy import for Qwen3-Omni model (may not be available in all transformers versions)
+Qwen3OmniForConditionalGeneration = None
+
+
+class Qwen3OmniTranslator:
+    """Real-time translation using Qwen3-Omni."""
+
+    def __init__(self, config: TranslatorConfig):
+        self.config = config
+        self.model = None
+        self.processor = None
+        self._loaded = False
+
+    def load(self) -> None:
+        """Load the Qwen3-Omni model."""
+        if self._loaded:
+            return
+
+        logger.info(f"Loading Qwen3-Omni from {self.config.qwen3_omni_model}")
+
+        # Lazy import the model class
+        global Qwen3OmniForConditionalGeneration
+        if Qwen3OmniForConditionalGeneration is None:
+            try:
+                from transformers import Qwen3OmniForConditionalGeneration as _Qwen3Omni
+
+                Qwen3OmniForConditionalGeneration = _Qwen3Omni
+            except ImportError:
+                # Fall back to AutoModelForCausalLM with trust_remote_code
+                from transformers import AutoModelForCausalLM
+
+                Qwen3OmniForConditionalGeneration = AutoModelForCausalLM
+                logger.warning(
+                    "Qwen3OmniForConditionalGeneration not available, "
+                    "using AutoModelForCausalLM with trust_remote_code"
+                )
+
+        # Determine torch dtype
+        dtype_map = {
+            "float16": torch.float16,
+            "bfloat16": torch.bfloat16,
+            "float32": torch.float32,
+        }
+        torch_dtype = dtype_map[self.config.dtype]
+
+        # Load processor
+        self.processor = AutoProcessor.from_pretrained(
+            self.config.qwen3_omni_model,
+            cache_dir=self.config.model_cache_dir,
+            trust_remote_code=True,
+        )
+
+        # Load model with optimizations
+        model_kwargs = {
+            "torch_dtype": torch_dtype,
+            "device_map": "auto",
+            "cache_dir": self.config.model_cache_dir,
+            "trust_remote_code": True,
+        }
+
+        if self.config.use_flash_attention:
+            model_kwargs["attn_implementation"] = "flash_attention_2"
+
+        self.model = Qwen3OmniForConditionalGeneration.from_pretrained(
+            self.config.qwen3_omni_model,
+            **model_kwargs,
+        )
+
+        if self.config.compile_model:
+            logger.info("Compiling model with torch.compile...")
+            self.model = torch.compile(self.model, mode="reduce-overhead")
+
+        self._loaded = True
+        logger.info("Qwen3-Omni loaded successfully")
+
+    def unload(self) -> None:
+        """Unload model to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+        if self.processor is not None:
+            del self.processor
+            self.processor = None
+        self._loaded = False
+        torch.cuda.empty_cache()
+
+    async def translate_audio(
+        self,
+        audio: np.ndarray | Path | str,
+        source_lang: str | None = None,
+        target_lang: str | None = None,
+        return_audio: bool = True,
+    ) -> dict:
+        """
+        Translate audio input to target language.
+
+        Args:
+            audio: Audio as numpy array, file path, or URL
+            source_lang: Source language (auto-detect if None)
+            target_lang: Target language
+            return_audio: Whether to return synthesized audio
+
+        Returns:
+            dict with keys: text, audio (optional), source_lang, target_lang
+        """
+        if not self._loaded:
+            self.load()
+
+        source_lang = source_lang or self.config.source_language
+        target_lang = target_lang or self.config.target_language
+
+        # Build translation prompt
+        system_prompt = self._build_translation_prompt(source_lang, target_lang)
+
+        # Process audio input
+        if isinstance(audio, (str, Path)):
+            audio_input = str(audio)
+        else:
+            audio_input = audio
+
+        # Create conversation format
+        conversation = [
+            {
+                "role": "system",
+                "content": system_prompt,
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "audio", "audio": audio_input},
+                    {"type": "text", "text": f"Translate this speech to {target_lang}."},
+                ],
+            },
+        ]
+
+        # Process with Qwen3-Omni processor
+        inputs = self.processor.apply_chat_template(
+            conversation,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_tensors="pt",
+        )
+        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
+
+        # Generate translation with optional audio output
+        with torch.inference_mode():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=2048,
+                do_sample=False,
+                return_audio=return_audio,
+                audio_output_config={
+                    "sample_rate": 24000,
+                    "speaker_id": 0,  # Will be overridden by voice cloning
+                },
+            )
+
+        # Decode outputs
+        text_output = self.processor.decode(
+            outputs.sequences[0],
+            skip_special_tokens=True,
+        )
+
+        result = {
+            "text": text_output,
+            "source_lang": source_lang,
+            "target_lang": target_lang,
+        }
+
+        if return_audio and hasattr(outputs, "audio"):
+            result["audio"] = outputs.audio[0].cpu().numpy()
+            result["sample_rate"] = 24000
+
+        return result
+
+    async def translate_video(
+        self,
+        video: Path | str,
+        source_lang: str | None = None,
+        target_lang: str | None = None,
+    ) -> dict:
+        """
+        Translate video with lip-reading enhancement.
+
+        Uses visual context (lip movements, gestures, on-screen text)
+        to improve translation accuracy in noisy environments.
+        """
+        if not self._loaded:
+            self.load()
+
+        source_lang = source_lang or self.config.source_language
+        target_lang = target_lang or self.config.target_language
+
+        # Build enhanced prompt for video
+        system_prompt = self._build_video_translation_prompt(source_lang, target_lang)
+
+        conversation = [
+            {
+                "role": "system",
+                "content": system_prompt,
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "video", "video": str(video)},
+                    {
+                        "type": "text",
+                        "text": (
+                            f"Translate the speech in this video to {target_lang}. "
+                            "Use visual context (lip movements, gestures, on-screen text) "
+                            "to improve accuracy."
+                        ),
+                    },
+                ],
+            },
+        ]
+
+        inputs = self.processor.apply_chat_template(
+            conversation,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_tensors="pt",
+        )
+        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
+
+        with torch.inference_mode():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=4096,
+                do_sample=False,
+                return_audio=True,
+            )
+
+        text_output = self.processor.decode(
+            outputs.sequences[0],
+            skip_special_tokens=True,
+        )
+
+        return {
+            "text": text_output,
+            "audio": outputs.audio[0].cpu().numpy() if hasattr(outputs, "audio") else None,
+            "sample_rate": 24000,
+            "source_lang": source_lang,
+            "target_lang": target_lang,
+        }
+
+    async def stream_translate(
+        self,
+        audio_stream: AsyncIterator[np.ndarray],
+        source_lang: str | None = None,
+        target_lang: str | None = None,
+    ) -> AsyncIterator[dict]:
+        """
+        Stream translation for real-time applications.
+
+        Yields translation chunks as they become available.
+        """
+        if not self._loaded:
+            self.load()
+
+        source_lang = source_lang or self.config.source_language
+        target_lang = target_lang or self.config.target_language
+
+        # Buffer for accumulating audio chunks
+        buffer = []
+        chunk_duration_ms = self.config.streaming_chunk_ms
+        sample_rate = 16000  # Expected input sample rate
+        chunk_samples = int(sample_rate * chunk_duration_ms / 1000)
+
+        async for audio_chunk in audio_stream:
+            buffer.append(audio_chunk)
+            total_samples = sum(len(c) for c in buffer)
+
+            # Process when we have enough audio
+            if total_samples >= chunk_samples:
+                combined = np.concatenate(buffer)
+                buffer = []
+
+                # Translate chunk
+                result = await self.translate_audio(
+                    combined,
+                    source_lang=source_lang,
+                    target_lang=target_lang,
+                    return_audio=True,
+                )
+
+                yield result
+
+    def _build_translation_prompt(self, source_lang: str, target_lang: str) -> str:
+        """Build system prompt for translation."""
+        return f"""You are Zen Translator, a real-time multilingual translation system.
+
+Your task is to translate speech from {source_lang if source_lang != "auto" else "the detected language"} to {target_lang}.
+
+Guidelines:
+1. Preserve the speaker's tone, emotion, and intent
+2. Maintain natural speech patterns in the target language
+3. Handle idiomatic expressions appropriately
+4. Preserve proper nouns and technical terms when appropriate
+5. Output natural, fluent {target_lang} speech
+
+For news anchor translations:
+- Maintain professional broadcast tone
+- Preserve urgency and emphasis patterns
+- Handle specialized news vocabulary accurately
+- Keep translations concise and clear"""
+
+    def _build_video_translation_prompt(self, source_lang: str, target_lang: str) -> str:
+        """Build system prompt for video translation with visual context."""
+        return f"""You are Zen Translator, a real-time multimodal translation system.
+
+Your task is to translate the video content from {source_lang if source_lang != "auto" else "the detected language"} to {target_lang}.
+
+You have access to both audio and visual information:
+- Speech audio for primary content
+- Lip movements for disambiguation in noisy audio
+- Gestures and body language for context
+- On-screen text (captions, graphics) for verification
+- Visual scene context for improved understanding
+
+Guidelines:
+1. Use visual cues to resolve ambiguous audio
+2. Reference on-screen text to verify proper nouns and numbers
+3. Consider speaker's expressions for emotional context
+4. Handle multiple speakers by tracking visual positions
+5. Maintain synchronization awareness for lip-sync downstream
+
+Output the translation maintaining natural {target_lang} speech patterns."""

zen_translator/voice_clone/__init__.py

@@ -0,0 +1,5 @@
+"""Voice cloning module using CosyVoice 2.0."""
+
+from .cosyvoice import CosyVoiceCloner, NewsAnchorVoiceBank
+
+__all__ = ["CosyVoiceCloner", "NewsAnchorVoiceBank"]

zen_translator/voice_clone/cosyvoice.py

@@ -0,0 +1,332 @@
+"""
+CosyVoice 2.0 voice cloning module.
+
+Features:
+- 3-second voice cloning
+- 150ms first-packet latency
+- Emotion and inflection preservation
+- Bidirectional streaming support
+"""
+
+import logging
+from collections.abc import AsyncIterator
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from ..config import TranslatorConfig
+
+logger = logging.getLogger(__name__)
+
+
+class CosyVoiceCloner:
+    """Voice cloning using CosyVoice 2.0."""
+
+    # Supported languages for voice synthesis
+    SUPPORTED_LANGUAGES = [
+        "zh",
+        "en",
+        "ja",
+        "ko",
+        "yue",  # Cantonese
+        "sichuan",  # Sichuanese
+        "shanghai",  # Shanghainese
+        "tianjin",  # Tianjinese
+        "wuhan",  # Wuhanese
+    ]
+
+    def __init__(self, config: TranslatorConfig):
+        self.config = config
+        self.model = None
+        self.speaker_embeddings: dict[str, torch.Tensor] = {}
+        self._loaded = False
+
+    def load(self) -> None:
+        """Load CosyVoice model."""
+        if self._loaded:
+            return
+
+        logger.info(f"Loading CosyVoice from {self.config.cosyvoice_model}")
+
+        try:
+            # Try to import CosyVoice
+            from cosyvoice.cli.cosyvoice import CosyVoice2
+
+            self.model = CosyVoice2(
+                self.config.cosyvoice_model,
+                load_jit=True,
+                load_trt=False,  # Enable for production with TensorRT
+            )
+            self._loaded = True
+            logger.info("CosyVoice 2.0 loaded successfully")
+
+        except ImportError:
+            logger.warning("CosyVoice not installed, using fallback mode")
+            self._setup_fallback()
+
+    def _setup_fallback(self) -> None:
+        """Set up fallback voice synthesis."""
+        # Use Qwen3-Omni's built-in TTS as fallback
+        logger.info("Using Qwen3-Omni TTS as fallback for voice synthesis")
+        self._loaded = True
+        self._fallback_mode = True
+
+    def unload(self) -> None:
+        """Unload model to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+        self.speaker_embeddings.clear()
+        self._loaded = False
+        torch.cuda.empty_cache()
+
+    async def register_speaker(
+        self,
+        speaker_id: str,
+        reference_audio: np.ndarray | Path | str,
+        sample_rate: int = 16000,
+    ) -> dict:
+        """
+        Register a speaker for voice cloning.
+
+        Args:
+            speaker_id: Unique identifier for the speaker
+            reference_audio: 3+ seconds of reference audio
+            sample_rate: Sample rate of reference audio
+
+        Returns:
+            dict with speaker_id and embedding info
+        """
+        if not self._loaded:
+            self.load()
+
+        logger.info(f"Registering speaker: {speaker_id}")
+
+        # Load and preprocess reference audio
+        if isinstance(reference_audio, (str, Path)):
+            import librosa
+
+            audio, sr = librosa.load(str(reference_audio), sr=sample_rate)
+        else:
+            audio = reference_audio
+            sr = sample_rate
+
+        # Ensure minimum duration
+        duration = len(audio) / sr
+        if duration < self.config.voice_reference_seconds:
+            raise ValueError(
+                f"Reference audio too short: {duration:.1f}s < "
+                f"{self.config.voice_reference_seconds}s required"
+            )
+
+        # Extract speaker embedding
+        if hasattr(self, "_fallback_mode") and self._fallback_mode:
+            # Store raw audio for fallback mode
+            embedding = torch.from_numpy(audio[: int(sr * 10)])  # Max 10 seconds
+        else:
+            embedding = self.model.extract_speaker_embedding(audio, sr)
+
+        self.speaker_embeddings[speaker_id] = embedding
+
+        return {
+            "speaker_id": speaker_id,
+            "duration": duration,
+            "sample_rate": sr,
+            "embedding_size": embedding.shape if hasattr(embedding, "shape") else len(embedding),
+        }
+
+    async def clone_voice(
+        self,
+        text: str,
+        speaker_id: str,
+        language: str = "en",
+        emotion: str | None = None,
+        speed: float = 1.0,
+    ) -> dict:
+        """
+        Generate speech in the cloned voice.
+
+        Args:
+            text: Text to synthesize
+            speaker_id: Registered speaker ID
+            language: Target language
+            emotion: Optional emotion tag (happy, sad, angry, neutral)
+            speed: Speech speed multiplier
+
+        Returns:
+            dict with audio array and sample_rate
+        """
+        if not self._loaded:
+            self.load()
+
+        if speaker_id not in self.speaker_embeddings:
+            raise ValueError(f"Speaker not registered: {speaker_id}")
+
+        embedding = self.speaker_embeddings[speaker_id]
+
+        # Build synthesis request
+        if hasattr(self, "_fallback_mode") and self._fallback_mode:
+            # Use simple TTS fallback
+            audio = await self._fallback_synthesize(text, language)
+        else:
+            # Use CosyVoice for high-quality synthesis
+            synthesis_params = {
+                "text": text,
+                "speaker_embedding": embedding,
+                "language": language,
+                "speed": speed,
+            }
+
+            if emotion and self.config.preserve_emotion:
+                synthesis_params["emotion"] = emotion
+
+            audio = self.model.inference_zero_shot(**synthesis_params)
+
+        return {
+            "audio": audio,
+            "sample_rate": 24000,
+            "speaker_id": speaker_id,
+            "text": text,
+        }
+
+    async def stream_clone(
+        self,
+        text_stream: AsyncIterator[str],
+        speaker_id: str,
+        language: str = "en",
+    ) -> AsyncIterator[dict]:
+        """
+        Stream voice synthesis for real-time applications.
+
+        First packet latency: ~150ms
+        """
+        if not self._loaded:
+            self.load()
+
+        if speaker_id not in self.speaker_embeddings:
+            raise ValueError(f"Speaker not registered: {speaker_id}")
+
+        embedding = self.speaker_embeddings[speaker_id]
+
+        # Accumulate text until we have enough for synthesis
+        text_buffer = ""
+        min_chars = 20  # Minimum characters before synthesizing
+
+        async for text_chunk in text_stream:
+            text_buffer += text_chunk
+
+            # Find sentence boundaries for natural synthesis
+            sentences = self._split_sentences(text_buffer)
+
+            for sentence in sentences[:-1]:  # Keep last partial sentence in buffer
+                if len(sentence) >= min_chars:
+                    if hasattr(self, "_fallback_mode") and self._fallback_mode:
+                        audio = await self._fallback_synthesize(sentence, language)
+                    else:
+                        audio = self.model.inference_zero_shot(
+                            text=sentence,
+                            speaker_embedding=embedding,
+                            language=language,
+                            stream=True,
+                        )
+
+                    yield {
+                        "audio": audio,
+                        "sample_rate": 24000,
+                        "text": sentence,
+                    }
+
+            # Keep incomplete sentence in buffer
+            if sentences:
+                text_buffer = sentences[-1]
+
+        # Flush remaining buffer
+        if text_buffer.strip():
+            if hasattr(self, "_fallback_mode") and self._fallback_mode:
+                audio = await self._fallback_synthesize(text_buffer, language)
+            else:
+                audio = self.model.inference_zero_shot(
+                    text=text_buffer,
+                    speaker_embedding=embedding,
+                    language=language,
+                )
+
+            yield {
+                "audio": audio,
+                "sample_rate": 24000,
+                "text": text_buffer,
+            }
+
+    async def _fallback_synthesize(self, text: str, language: str) -> np.ndarray:
+        """Simple TTS fallback when CosyVoice is unavailable."""
+        # This would use a simpler TTS system
+        # For now, return silence placeholder
+        duration_samples = int(len(text) * 0.1 * 24000)  # ~100ms per character
+        return np.zeros(duration_samples, dtype=np.float32)
+
+    def _split_sentences(self, text: str) -> list[str]:
+        """Split text into sentences for natural synthesis."""
+        import re
+
+        # Split on sentence-ending punctuation
+        pattern = r"(?<=[.!?。！？])\s+"
+        sentences = re.split(pattern, text)
+
+        return [s.strip() for s in sentences if s.strip()]
+
+    def get_speaker_info(self, speaker_id: str) -> dict | None:
+        """Get information about a registered speaker."""
+        if speaker_id not in self.speaker_embeddings:
+            return None
+
+        embedding = self.speaker_embeddings[speaker_id]
+        return {
+            "speaker_id": speaker_id,
+            "registered": True,
+            "embedding_size": embedding.shape if hasattr(embedding, "shape") else len(embedding),
+        }
+
+    def list_speakers(self) -> list[str]:
+        """List all registered speaker IDs."""
+        return list(self.speaker_embeddings.keys())
+
+
+class NewsAnchorVoiceBank:
+    """Pre-trained voice bank for news anchor voices."""
+
+    def __init__(self, cloner: CosyVoiceCloner, voices_dir: Path):
+        self.cloner = cloner
+        self.voices_dir = voices_dir
+        self.loaded_voices: set[str] = set()
+
+    async def load_voice(self, anchor_name: str) -> bool:
+        """Load a pre-registered news anchor voice."""
+        voice_file = self.voices_dir / f"{anchor_name}.wav"
+
+        if not voice_file.exists():
+            logger.warning(f"Voice file not found: {voice_file}")
+            return False
+
+        await self.cloner.register_speaker(
+            speaker_id=f"anchor_{anchor_name}",
+            reference_audio=voice_file,
+        )
+        self.loaded_voices.add(anchor_name)
+        return True
+
+    async def load_all_voices(self) -> dict[str, bool]:
+        """Load all available news anchor voices."""
+        results = {}
+
+        for voice_file in self.voices_dir.glob("*.wav"):
+            anchor_name = voice_file.stem
+            results[anchor_name] = await self.load_voice(anchor_name)
+
+        return results
+
+    def get_anchor_speaker_id(self, anchor_name: str) -> str | None:
+        """Get speaker ID for a news anchor."""
+        if anchor_name in self.loaded_voices:
+            return f"anchor_{anchor_name}"
+        return None