Method LoadModel

backend/python/liquid-audio/backend.py:146–219

(self, request, context)

    def LoadModel(self, request, context):
        try:
            import torch

            self.options = parse_options(request.Options)
            if self.options.get("voice") and self.options["voice"] not in VOICE_PROMPTS:
                print(f"Warning: unknown voice '{self.options['voice']}'; defaulting to '{DEFAULT_VOICE}'",
                      file=sys.stderr)

            requested_device = self.options.get("device")
            self.device = requested_device or _select_device()
            if self.device == "cuda" and not torch.cuda.is_available():
                return backend_pb2.Result(success=False, message="CUDA requested but not available")
            if self.device == "mps" and not (hasattr(torch.backends, "mps") and
                                             torch.backends.mps.is_available()):
                print("MPS not available; falling back to CPU", file=sys.stderr)
                self.device = "cpu"

            dtype_name = str(self.options.get("dtype", "bfloat16")).lower()
            self.dtype = {
                "bfloat16": torch.bfloat16,
                "bf16":     torch.bfloat16,
                "float16":  torch.float16,
                "fp16":     torch.float16,
                "half":     torch.float16,
                "float32":  torch.float32,
                "fp32":     torch.float32,
            }.get(dtype_name, torch.bfloat16)

            # request.Model holds the raw `parameters.model` value (an HF
            # repo id like "LiquidAI/LFM2.5-Audio-1.5B"); request.ModelFile
            # is LocalAI's ModelPath-prefixed local copy that exists only
            # when the gallery supplied a `files:` list. Mirror the
            # transformers/vibevoice convention: prefer the repo id and
            # only switch to the local path if it's been staged on disk.
            model_id = request.Model
            if not model_id:
                model_id = request.ModelFile
            if not model_id:
                return backend_pb2.Result(success=False, message="No model identifier provided")
            if request.ModelFile and os.path.isdir(request.ModelFile):
                model_id = request.ModelFile
            self.model_id = model_id

            # Pure fine-tune jobs don't need an in-memory inference model — the
            # Trainer instantiates its own copy at StartFineTune time.
            if self.mode == "finetune":
                print(f"Loaded liquid-audio backend in fine-tune mode (model id: {model_id})",
                      file=sys.stderr)
                return backend_pb2.Result(success=True, message="OK")

            from liquid_audio import LFM2AudioModel, LFM2AudioProcessor

            # liquid_audio's from_pretrained unconditionally routes through
            # huggingface_hub.snapshot_download, which rejects local paths
            # (HFValidationError on `/models/LiquidAI/LFM2.5-Audio-1.5B`).
            # When LocalAI's gallery has already staged the weights on disk,
            # short-circuit the download to return the local directory.