feat: add Chatterbox Turbo engine and per-engine language lists

- New ChatterboxTurboTTSBackend wrapping ChatterboxTurboTTS (ResembleAI/chatterbox-turbo)
- English-only 350M model with paralinguistic tag support ([laugh], [cough], [chuckle])
- Bypasses upstream token=True bug by calling snapshot_download(token=None) + from_local()
- Same CPU-on-macOS forcing and torch.load monkey-patching as multilingual backend
- Full engine integration: generate, stream, model status/download/delete endpoints
- Language dropdown now shows only languages supported by the selected engine
- Per-engine language maps: Qwen (10), LuxTTS (en), Chatterbox (23), Turbo (en)
- Auto-switches to English when selecting English-only engines
- Backend language regex expanded to accept all 23 Chatterbox languages

backend/backends/__init__.py

@@ -122,6 +122,7 @@ TTS_ENGINES = {
     "qwen": "Qwen TTS",
     "luxtts": "LuxTTS",
     "chatterbox": "Chatterbox TTS",
+    "chatterbox_turbo": "Chatterbox Turbo",
 }
 
 
@@ -171,6 +172,9 @@ def get_tts_backend_for_engine(engine: str) -> TTSBackend:
         elif engine == "chatterbox":
             from .chatterbox_backend import ChatterboxTTSBackend
             backend = ChatterboxTTSBackend()
+        elif engine == "chatterbox_turbo":
+            from .chatterbox_turbo_backend import ChatterboxTurboTTSBackend
+            backend = ChatterboxTurboTTSBackend()
         else:
             raise ValueError(f"Unknown TTS engine: {engine}. Supported: {list(TTS_ENGINES.keys())}")
         

backend/backends/chatterbox_turbo_backend.py

@@ -0,0 +1,307 @@
+"""
+Chatterbox Turbo TTS backend implementation.
+
+Wraps ChatterboxTurboTTS from chatterbox-tts for fast, English-only
+voice cloning with paralinguistic tag support ([laugh], [cough], etc.).
+Forces CPU on macOS due to known MPS tensor issues.
+"""
+
+import asyncio
+import logging
+import platform
+import threading
+from pathlib import Path
+from typing import ClassVar, List, Optional, Tuple
+
+import numpy as np
+
+from . import TTSBackend
+from ..utils.audio import normalize_audio, load_audio
+from ..utils.progress import get_progress_manager
+from ..utils.tasks import get_task_manager
+
+logger = logging.getLogger(__name__)
+
+CHATTERBOX_TURBO_HF_REPO = "ResembleAI/chatterbox-turbo"
+
+# Files that must be present for the turbo model
+_TURBO_WEIGHT_FILES = [
+    "t3_turbo_v1.safetensors",
+    "s3gen_meanflow.safetensors",
+    "ve.safetensors",
+]
+
+
+class ChatterboxTurboTTSBackend:
+    """Chatterbox Turbo TTS backend — fast, English-only, with paralinguistic tags."""
+
+    # Class-level lock for torch.load monkey-patching
+    _load_lock: ClassVar[threading.Lock] = threading.Lock()
+
+    def __init__(self):
+        self.model = None
+        self.model_size = "default"
+        self._device = None
+        self._model_load_lock = asyncio.Lock()
+
+    def _get_device(self) -> str:
+        """Get the best available device. Forces CPU on macOS (MPS issue)."""
+        if platform.system() == "Darwin":
+            return "cpu"
+        try:
+            import torch
+
+            if torch.cuda.is_available():
+                return "cuda"
+        except ImportError:
+            pass
+        return "cpu"
+
+    def is_loaded(self) -> bool:
+        return self.model is not None
+
+    def _get_model_path(self, model_size: str = "default") -> str:
+        return CHATTERBOX_TURBO_HF_REPO
+
+    def _is_model_cached(self, model_size: str = "default") -> bool:
+        """Check if the Chatterbox Turbo model is cached locally."""
+        try:
+            from huggingface_hub import constants as hf_constants
+
+            repo_cache = Path(hf_constants.HF_HUB_CACHE) / (
+                "models--" + CHATTERBOX_TURBO_HF_REPO.replace("/", "--")
+            )
+
+            if not repo_cache.exists():
+                return False
+
+            blobs_dir = repo_cache / "blobs"
+            if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
+                return False
+
+            # Check for turbo weight files
+            snapshots_dir = repo_cache / "snapshots"
+            if snapshots_dir.exists():
+                for fname in _TURBO_WEIGHT_FILES:
+                    if not any(snapshots_dir.rglob(fname)):
+                        return False
+                return True
+
+            return False
+        except Exception as e:
+            logger.warning(f"Error checking Chatterbox Turbo cache: {e}")
+            return False
+
+    async def load_model(self, model_size: str = "default") -> None:
+        """Load the Chatterbox Turbo model."""
+        if self.model is not None:
+            return
+        async with self._model_load_lock:
+            if self.model is not None:
+                return
+            await asyncio.to_thread(self._load_model_sync)
+
+    def _load_model_sync(self):
+        """Synchronous model loading."""
+        from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
+
+        progress_manager = get_progress_manager()
+        task_manager = get_task_manager()
+        model_name = "chatterbox-turbo"
+
+        is_cached = self._is_model_cached()
+
+        # Set up HF progress tracking (intercepts tqdm for file-level progress)
+        progress_callback = create_hf_progress_callback(model_name, progress_manager)
+        tracker = HFProgressTracker(progress_callback, filter_non_downloads=is_cached)
+        tracker_context = tracker.patch_download()
+        tracker_context.__enter__()
+
+        if not is_cached:
+            task_manager.start_download(model_name)
+            progress_manager.update_progress(
+                model_name=model_name,
+                current=0,
+                total=0,
+                filename="Connecting to HuggingFace...",
+                status="downloading",
+            )
+
+        try:
+            device = self._get_device()
+            self._device = device
+
+            logger.info(f"Loading Chatterbox Turbo TTS on {device}...")
+
+            import torch
+            from huggingface_hub import snapshot_download
+            from chatterbox.tts_turbo import ChatterboxTurboTTS
+
+            # Download model files ourselves so we can pass token=None
+            # (upstream from_pretrained passes token=True which requires
+            # a stored HF token even though the repo is public).
+            try:
+                local_path = snapshot_download(
+                    repo_id=CHATTERBOX_TURBO_HF_REPO,
+                    token=None,
+                    allow_patterns=[
+                        "*.safetensors", "*.json", "*.txt", "*.pt", "*.model",
+                    ],
+                )
+            finally:
+                tracker_context.__exit__(None, None, None)
+
+            # Monkey-patch torch.load for CPU loading. The model's .pt files
+            # were saved on CUDA; from_local() doesn't pass map_location
+            # so loading on CPU fails without this.
+            if device == "cpu":
+                _orig_torch_load = torch.load
+
+                def _patched_load(*args, **kwargs):
+                    kwargs.setdefault("map_location", "cpu")
+                    return _orig_torch_load(*args, **kwargs)
+
+                with ChatterboxTurboTTSBackend._load_lock:
+                    torch.load = _patched_load
+                    try:
+                        self.model = ChatterboxTurboTTS.from_local(
+                            local_path, device,
+                        )
+                    finally:
+                        torch.load = _orig_torch_load
+            else:
+                self.model = ChatterboxTurboTTS.from_local(
+                    local_path, device,
+                )
+
+            if not is_cached:
+                progress_manager.mark_complete(model_name)
+                task_manager.complete_download(model_name)
+
+            logger.info("Chatterbox Turbo TTS loaded successfully")
+
+        except ImportError as e:
+            logger.error(
+                "chatterbox-tts package not found. "
+                "Install with: pip install chatterbox-tts"
+            )
+            if not is_cached:
+                progress_manager.mark_error(model_name, str(e))
+                task_manager.error_download(model_name, str(e))
+            raise
+        except Exception as e:
+            logger.error(f"Failed to load Chatterbox Turbo: {e}")
+            if not is_cached:
+                progress_manager.mark_error(model_name, str(e))
+                task_manager.error_download(model_name, str(e))
+            raise
+
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        if self.model is not None:
+            device = self._device
+            del self.model
+            self.model = None
+            self._device = None
+            if device == "cuda":
+                import torch
+
+                torch.cuda.empty_cache()
+            logger.info("Chatterbox Turbo unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio.
+
+        Chatterbox Turbo processes reference audio at generation time, so the
+        prompt just stores the file path.
+        """
+        voice_prompt = {
+            "ref_audio": str(audio_path),
+            "ref_text": reference_text,
+        }
+        return voice_prompt, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        """Combine multiple reference samples."""
+        combined_audio = []
+        for path in audio_paths:
+            audio, _sr = load_audio(path)
+            audio = normalize_audio(audio)
+            combined_audio.append(audio)
+
+        mixed = np.concatenate(combined_audio)
+        mixed = normalize_audio(mixed)
+        combined_text = " ".join(reference_texts)
+        return mixed, combined_text
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio using Chatterbox Turbo TTS.
+
+        Supports paralinguistic tags in text: [laugh], [cough], [chuckle], etc.
+
+        Args:
+            text: Text to synthesize (may include paralinguistic tags)
+            voice_prompt: Dict with ref_audio path
+            language: Ignored (Turbo is English-only)
+            seed: Random seed for reproducibility
+            instruct: Unused (protocol compatibility)
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model()
+
+        ref_audio = voice_prompt.get("ref_audio")
+        if ref_audio and not Path(ref_audio).exists():
+            logger.warning(f"Reference audio not found: {ref_audio}")
+            ref_audio = None
+
+        def _generate_sync():
+            import torch
+
+            if seed is not None:
+                torch.manual_seed(seed)
+
+            logger.info("[Chatterbox Turbo] Generating (English)")
+
+            wav = self.model.generate(
+                text,
+                audio_prompt_path=ref_audio,
+                temperature=0.8,
+                top_k=1000,
+                top_p=0.95,
+                repetition_penalty=1.2,
+            )
+
+            # Convert tensor -> numpy
+            if isinstance(wav, torch.Tensor):
+                audio = wav.squeeze().cpu().numpy().astype(np.float32)
+            else:
+                audio = np.asarray(wav, dtype=np.float32)
+
+            sample_rate = (
+                getattr(self.model, "sr", None)
+                or getattr(self.model, "sample_rate", 24000)
+            )
+
+            return audio, sample_rate
+
+        return await asyncio.to_thread(_generate_sync)

backend/main.py

@@ -699,6 +699,29 @@ async def generate_speech(
                 )
 
             await tts_model.load_model()
+        elif engine == "chatterbox_turbo":
+            if not tts_model._is_model_cached():
+                model_name = "chatterbox-turbo"
+
+                async def download_chatterbox_turbo_background():
+                    try:
+                        await tts_model.load_model()
+                    except Exception as e:
+                        task_manager.error_download(model_name, str(e))
+
+                task_manager.start_download(model_name)
+                asyncio.create_task(download_chatterbox_turbo_background())
+
+                raise HTTPException(
+                    status_code=202,
+                    detail={
+                        "message": "Chatterbox Turbo model is being downloaded. Please wait and try again.",
+                        "model_name": model_name,
+                        "downloading": True,
+                    },
+                )
+
+            await tts_model.load_model()
 
         # Create voice prompt from profile
         voice_prompt = await profiles.create_voice_prompt_for_profile(
@@ -717,7 +740,7 @@ async def generate_speech(
         )
 
         # Trim trailing silence/hallucination for Chatterbox output
-        if engine == "chatterbox":
+        if engine in ("chatterbox", "chatterbox_turbo"):
             from .utils.audio import trim_tts_output
             audio = trim_tts_output(audio, sample_rate)
 
@@ -798,6 +821,13 @@ async def stream_speech(
                 detail="Chatterbox model is not downloaded yet. Use /generate to trigger a download.",
             )
         await tts_model.load_model()
+    elif engine == "chatterbox_turbo":
+        if not tts_model._is_model_cached():
+            raise HTTPException(
+                status_code=400,
+                detail="Chatterbox Turbo model is not downloaded yet. Use /generate to trigger a download.",
+            )
+        await tts_model.load_model()
 
     voice_prompt = await profiles.create_voice_prompt_for_profile(
         data.profile_id, db, engine=engine,
@@ -812,7 +842,7 @@ async def stream_speech(
     )
 
     # Trim trailing silence/hallucination for Chatterbox output
-    if engine == "chatterbox":
+    if engine in ("chatterbox", "chatterbox_turbo"):
         from .utils.audio import trim_tts_output
         audio = trim_tts_output(audio, sample_rate)
 
@@ -1433,6 +1463,15 @@ async def get_model_status():
         except Exception:
             return False
 
+    # Check if Chatterbox Turbo backend is loaded
+    def check_chatterbox_turbo_loaded():
+        try:
+            from .backends import get_tts_backend_for_engine
+            backend = get_tts_backend_for_engine("chatterbox_turbo")
+            return backend.is_loaded()
+        except Exception:
+            return False
+
     model_configs = [
         {
             "model_name": "qwen-tts-1.7B",
@@ -1462,6 +1501,13 @@ async def get_model_status():
             "model_size": "default",
             "check_loaded": check_chatterbox_loaded,
         },
+        {
+            "model_name": "chatterbox-turbo",
+            "display_name": "Chatterbox Turbo (English, Tags)",
+            "hf_repo_id": "ResembleAI/chatterbox-turbo",
+            "model_size": "default",
+            "check_loaded": check_chatterbox_turbo_loaded,
+        },
         {
             "model_name": "whisper-base",
             "display_name": "Whisper Base",
@@ -1668,6 +1714,10 @@ async def trigger_model_download(request: models.ModelDownloadRequest):
             "model_size": "default",
             "load_func": lambda: get_tts_backend_for_engine("chatterbox").load_model(),
         },
+        "chatterbox-turbo": {
+            "model_size": "default",
+            "load_func": lambda: get_tts_backend_for_engine("chatterbox_turbo").load_model(),
+        },
         "whisper-base": {
             "model_size": "base",
             "load_func": lambda: transcribe.get_whisper_model().load_model("base"),
@@ -1790,6 +1840,11 @@ async def delete_model(model_name: str):
             "model_size": "default",
             "model_type": "chatterbox",
         },
+        "chatterbox-turbo": {
+            "hf_repo_id": "ResembleAI/chatterbox-turbo",
+            "model_size": "default",
+            "model_type": "chatterbox_turbo",
+        },
         "whisper-base": {
             "hf_repo_id": "openai/whisper-base",
             "model_size": "base",
@@ -1834,6 +1889,11 @@ async def delete_model(model_name: str):
             chatterbox = get_tts_backend_for_engine("chatterbox")
             if chatterbox.is_loaded():
                 chatterbox.unload_model()
+        elif config["model_type"] == "chatterbox_turbo":
+            from .backends import get_tts_backend_for_engine
+            turbo = get_tts_backend_for_engine("chatterbox_turbo")
+            if turbo.is_loaded():
+                turbo.unload_model()
         elif config["model_type"] == "whisper":
             whisper_model = transcribe.get_whisper_model()
             if whisper_model.is_loaded() and whisper_model.model_size == config["model_size"]:

backend/models.py

@@ -11,7 +11,7 @@ class VoiceProfileCreate(BaseModel):
     """Request model for creating a voice profile."""
     name: str = Field(..., min_length=1, max_length=100)
     description: Optional[str] = Field(None, max_length=500)
-    language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he)$")
+    language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he|ar|da|el|fi|hi|ms|nl|no|pl|sv|sw|tr)$")
 
 
 class VoiceProfileResponse(BaseModel):
@@ -57,7 +57,7 @@ class GenerationRequest(BaseModel):
     seed: Optional[int] = Field(None, ge=0)
     model_size: Optional[str] = Field(default="1.7B", pattern="^(1\\.7B|0\\.6B)$")
     instruct: Optional[str] = Field(None, max_length=500)
-    engine: Optional[str] = Field(default="qwen", pattern="^(qwen|luxtts|chatterbox)$")
+    engine: Optional[str] = Field(default="qwen", pattern="^(qwen|luxtts|chatterbox|chatterbox_turbo)$")
 
 
 class GenerationResponse(BaseModel):