feat: Intel Arc (XPU) GPU support

backend/app.py

@@ -155,6 +155,20 @@ def _get_gpu_status() -> str:
         return "MPS (Apple Silicon)"
     elif backend_type == "mlx":
         return "Metal (Apple Silicon via MLX)"
+
+    # Intel XPU (Arc / Data Center) via IPEX
+    try:
+        import intel_extension_for_pytorch  # noqa: F401
+
+        if hasattr(torch, "xpu") and torch.xpu.is_available():
+            try:
+                xpu_name = torch.xpu.get_device_name(0)
+            except Exception:
+                xpu_name = "Intel GPU"
+            return f"XPU ({xpu_name})"
+    except ImportError:
+        pass
+
     return "None (CPU only)"
 
 

backend/backends/base.py

@@ -126,6 +126,37 @@ def get_torch_device(
     return "cpu"
 
 
+def empty_device_cache(device: str) -> None:
+    """
+    Free cached memory on the given device (CUDA or XPU).
+
+    Backends should call this after unloading models so VRAM is returned
+    to the OS.
+    """
+    import torch
+
+    if device == "cuda" and torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    elif device == "xpu" and hasattr(torch, "xpu"):
+        torch.xpu.empty_cache()
+
+
+def manual_seed(seed: int, device: str) -> None:
+    """
+    Set the random seed on both CPU and the active accelerator.
+
+    Covers CUDA and Intel XPU so that generation is reproducible
+    regardless of which GPU backend is in use.
+    """
+    import torch
+
+    torch.manual_seed(seed)
+    if device == "cuda" and torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+    elif device == "xpu" and hasattr(torch, "xpu"):
+        torch.xpu.manual_seed(seed)
+
+
 async def combine_voice_prompts(
     audio_paths: List[str],
     reference_texts: List[str],

backend/backends/chatterbox_backend.py

@@ -18,6 +18,8 @@
 from .base import (
     is_model_cached,
     get_torch_device,
+    empty_device_cache,
+    manual_seed,
     combine_voice_prompts as _combine_voice_prompts,
     model_load_progress,
     patch_chatterbox_f32,
@@ -48,7 +50,7 @@ def __init__(self):
         self._model_load_lock = asyncio.Lock()
 
     def _get_device(self) -> str:
-        return get_torch_device(force_cpu_on_mac=True)
+        return get_torch_device(force_cpu_on_mac=True, allow_xpu=True)
 
     def is_loaded(self) -> bool:
         return self.model is not None
@@ -117,10 +119,7 @@ def unload_model(self) -> None:
             del self.model
             self.model = None
             self._device = None
-            if device == "cuda":
-                import torch
-
-                torch.cuda.empty_cache()
+            empty_device_cache(device)
             logger.info("Chatterbox unloaded")
 
     async def create_voice_prompt(
@@ -200,7 +199,7 @@ def _generate_sync():
             import torch
 
             if seed is not None:
-                torch.manual_seed(seed)
+                manual_seed(seed, self._device)
 
             logger.info(f"[Chatterbox] Generating: lang={language}")
 
@@ -220,10 +219,7 @@ def _generate_sync():
             else:
                 audio = np.asarray(wav, dtype=np.float32)
 
-            sample_rate = (
-                getattr(self.model, "sr", None)
-                or getattr(self.model, "sample_rate", 24000)
-            )
+            sample_rate = getattr(self.model, "sr", None) or getattr(self.model, "sample_rate", 24000)
 
             return audio, sample_rate
 

backend/backends/chatterbox_turbo_backend.py

@@ -18,6 +18,8 @@
 from .base import (
     is_model_cached,
     get_torch_device,
+    empty_device_cache,
+    manual_seed,
     combine_voice_prompts as _combine_voice_prompts,
     model_load_progress,
     patch_chatterbox_f32,
@@ -48,7 +50,7 @@ def __init__(self):
         self._model_load_lock = asyncio.Lock()
 
     def _get_device(self) -> str:
-        return get_torch_device(force_cpu_on_mac=True)
+        return get_torch_device(force_cpu_on_mac=True, allow_xpu=True)
 
     def is_loaded(self) -> bool:
         return self.model is not None
@@ -116,10 +118,7 @@ def unload_model(self) -> None:
             del self.model
             self.model = None
             self._device = None
-            if device == "cuda":
-                import torch
-
-                torch.cuda.empty_cache()
+            empty_device_cache(device)
             logger.info("Chatterbox Turbo unloaded")
 
     async def create_voice_prompt(
@@ -181,7 +180,7 @@ def _generate_sync():
             import torch
 
             if seed is not None:
-                torch.manual_seed(seed)
+                manual_seed(seed, self._device)
 
             logger.info("[Chatterbox Turbo] Generating (English)")
 
@@ -200,10 +199,7 @@ def _generate_sync():
             else:
                 audio = np.asarray(wav, dtype=np.float32)
 
-            sample_rate = (
-                getattr(self.model, "sr", None)
-                or getattr(self.model, "sample_rate", 24000)
-            )
+            sample_rate = getattr(self.model, "sr", None) or getattr(self.model, "sample_rate", 24000)
 
             return audio, sample_rate
 

backend/backends/hume_backend.py

@@ -24,6 +24,8 @@
 from .base import (
     is_model_cached,
     get_torch_device,
+    empty_device_cache,
+    manual_seed,
     combine_voice_prompts as _combine_voice_prompts,
     model_load_progress,
 )
@@ -66,7 +68,7 @@ def __init__(self):
     def _get_device(self) -> str:
         # Force CPU on macOS — MPS has issues with flow matching
         # and large vocab lm_head (>65536 output channels)
-        return get_torch_device(force_cpu_on_mac=True)
+        return get_torch_device(force_cpu_on_mac=True, allow_xpu=True)
 
     def is_loaded(self) -> bool:
         return self.model is not None
@@ -105,6 +107,7 @@ def _load_model_sync(self, model_size: str = "1B"):
             # package.  The real package pulls in onnx/tensorboard/matplotlib via
             # descript-audiotools, so we use a lightweight shim instead.
             from ..utils.dac_shim import install_dac_shim
+
             install_dac_shim()
 
             import torch
@@ -142,9 +145,12 @@ def _load_model_sync(self, model_size: str = "1B"):
                 allow_patterns=["tokenizer*", "special_tokens*"],
             )
 
-            # Determine dtype — use bf16 on CUDA for ~50% memory savings
+            # Determine dtype — use bf16 on CUDA/XPU for ~50% memory savings
             if device == "cuda" and torch.cuda.is_bf16_supported():
                 model_dtype = torch.bfloat16
+            elif device == "xpu":
+                # Intel Arc (Alchemist+) supports bf16 natively
+                model_dtype = torch.bfloat16
             else:
                 model_dtype = torch.float32
 
@@ -153,14 +159,14 @@ def _load_model_sync(self, model_size: str = "1B"):
             # This avoids monkey-patching AutoTokenizer.from_pretrained
             # which corrupts the classmethod descriptor for other engines.
             from tada.modules.aligner import AlignerConfig
+
             AlignerConfig.tokenizer_name = tokenizer_path
 
             # Load encoder (only needed for voice prompt encoding)
             from tada.modules.encoder import Encoder
+
             logger.info("Loading TADA encoder...")
-            self.encoder = Encoder.from_pretrained(
-                TADA_CODEC_REPO, subfolder="encoder"
-            ).to(device)
+            self.encoder = Encoder.from_pretrained(TADA_CODEC_REPO, subfolder="encoder").to(device)
             self.encoder.eval()
 
             # Load the causal LM (includes decoder for wav generation).
@@ -169,12 +175,11 @@ def _load_model_sync(self, model_size: str = "1B"):
             # which hits the gated repo. Pre-load the config from HF,
             # inject the local tokenizer path, then pass it in.
             from tada.modules.tada import TadaForCausalLM, TadaConfig
+
             logger.info(f"Loading TADA {model_size} model...")
             config = TadaConfig.from_pretrained(repo)
             config.tokenizer_name = tokenizer_path
-            self.model = TadaForCausalLM.from_pretrained(
-                repo, config=config, torch_dtype=model_dtype
-            ).to(device)
+            self.model = TadaForCausalLM.from_pretrained(repo, config=config, torch_dtype=model_dtype).to(device)
             self.model.eval()
 
         logger.info(f"HumeAI TADA {model_size} loaded successfully on {device}")
@@ -188,11 +193,11 @@ def unload_model(self) -> None:
             del self.encoder
             self.encoder = None
 
+        device = self._device
         self._device = None
 
-        import torch
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
+        if device:
+            empty_device_cache(device)
 
         logger.info("HumeAI TADA unloaded")
 
@@ -213,9 +218,7 @@ async def create_voice_prompt(
         """
         await self.load_model(self.model_size)
 
-        cache_key = (
-            "tada_" + get_cache_key(audio_path, reference_text)
-        ) if use_cache else None
+        cache_key = ("tada_" + get_cache_key(audio_path, reference_text)) if use_cache else None
 
         if cache_key:
             cached = get_cached_voice_prompt(cache_key)
@@ -239,9 +242,7 @@ def _encode_sync():
 
             # Encode with forced alignment
             text_arg = [reference_text] if reference_text else None
-            prompt = self.encoder(
-                audio, text=text_arg, sample_rate=sr
-            )
+            prompt = self.encoder(audio, text=text_arg, sample_rate=sr)
 
             # Serialize EncoderOutput to a dict of CPU tensors for caching
             prompt_dict = {}
@@ -299,9 +300,7 @@ def _generate_sync():
             from tada.modules.encoder import EncoderOutput
 
             if seed is not None:
-                torch.manual_seed(seed)
-                if torch.cuda.is_available():
-                    torch.cuda.manual_seed(seed)
+                manual_seed(seed, self._device)
 
             device = self._device
 

backend/backends/luxtts_backend.py

@@ -12,7 +12,14 @@
 import numpy as np
 
 from . import TTSBackend
-from .base import is_model_cached, get_torch_device, combine_voice_prompts as _combine_voice_prompts, model_load_progress
+from .base import (
+    is_model_cached,
+    get_torch_device,
+    empty_device_cache,
+    manual_seed,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+)
 from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
 
 logger = logging.getLogger(__name__)
@@ -30,7 +37,7 @@ def __init__(self):
         self._device = None
 
     def _get_device(self) -> str:
-        return get_torch_device(allow_mps=True)
+        return get_torch_device(allow_mps=True, allow_xpu=True)
 
     def is_loaded(self) -> bool:
         return self.model is not None
@@ -69,9 +76,12 @@ def _load_model_sync(self):
 
             if device == "cpu":
                 import os
+
                 threads = os.cpu_count() or 4
                 self.model = LuxTTS(
-                    model_path=LUXTTS_HF_REPO, device="cpu", threads=min(threads, 8),
+                    model_path=LUXTTS_HF_REPO,
+                    device="cpu",
+                    threads=min(threads, 8),
                 )
             else:
                 self.model = LuxTTS(model_path=LUXTTS_HF_REPO, device=device)
@@ -81,12 +91,12 @@ def _load_model_sync(self):
     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
 
-            import torch
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+            empty_device_cache(device)
 
             logger.info("LuxTTS unloaded")
 
@@ -154,12 +164,8 @@ async def generate(
         await self.load_model()
 
         def _generate_sync():
-            import torch
-
             if seed is not None:
-                torch.manual_seed(seed)
-                if torch.cuda.is_available():
-                    torch.cuda.manual_seed(seed)
+                manual_seed(seed, self.device)
 
             wav = self.model.generate_speech(
                 text=text,

backend/backends/pytorch_backend.py

@@ -14,6 +14,8 @@
 from .base import (
     is_model_cached,
     get_torch_device,
+    empty_device_cache,
+    manual_seed,
     combine_voice_prompts as _combine_voice_prompts,
     model_load_progress,
 )
@@ -120,8 +122,7 @@ def unload_model(self):
             self.model = None
             self._current_model_size = None
 
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+            empty_device_cache(self.device)
 
             logger.info("TTS model unloaded")
 
@@ -213,9 +214,7 @@ def _generate_sync():
             """Run synchronous generation in thread pool."""
             # Set seed if provided
             if seed is not None:
-                torch.manual_seed(seed)
-                if torch.cuda.is_available():
-                    torch.cuda.manual_seed(seed)
+                manual_seed(seed, self.device)
 
             # Generate audio - this is the blocking operation
             wavs, sample_rate = self.model.generate_voice_clone(
@@ -297,8 +296,7 @@ def unload_model(self):
             self.model = None
             self.processor = None
 
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+            empty_device_cache(self.device)
 
             logger.info("Whisper model unloaded")