Update custom model files, README, and requirements

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"""Custom inference handler for HuggingFace Inference Endpoints."""

from typing import Any

import torch

try:
    # For remote execution, imports are relative
    from .asr_modeling import ASRModel
    from .asr_pipeline import ASRPipeline
except ImportError:
    # For local execution, imports are not relative
    from asr_modeling import ASRModel  # type: ignore[no-redef]
    from asr_pipeline import ASRPipeline  # type: ignore[no-redef]


def _best_device() -> torch.device:
    """Best available inference device (cuda > mps > cpu)."""
    if torch.cuda.is_available():
        return torch.device("cuda")
    if torch.backends.mps.is_available():
        return torch.device("mps")
    return torch.device("cpu")


class EndpointHandler:
    """HuggingFace Inference Endpoints handler for ASR model.
    Handles model loading, warmup, and inference requests for deployment
    on HuggingFace Inference Endpoints or similar services.
    """

    def __init__(self, path: str = ""):
        """Initialize the endpoint handler.
        Args:
            path: Path to model directory or HuggingFace model ID
        """
        import os

        import nltk

        nltk.download("punkt_tab", quiet=True)

        os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")

        # `ASRModel.from_pretrained` constructs its own submodules and forwards
        # **kwargs to `ASRModel.__init__`, which discards them -- no loader
        # reads `device_map`, `torch_dtype` or `low_cpu_mem_usage`, and it sets
        # `_is_loading_from_pretrained` precisely to keep `device_map="auto"`
        # out of the sub-model loaders. Passing them here did nothing, so the
        # model stayed on CPU and a GPU Inference Endpoint silently decoded on
        # CPU. Place it explicitly instead. dtype comes from
        # `config.model_dtype` and the attention backend from
        # `config.attn_implementation`, which already downgrades FA2 when
        # flash_attn is missing.
        self.model = ASRModel.from_pretrained(path)
        self.device = _best_device()
        self.model.to(self.device)
        self.model.eval()

        self.pipe = ASRPipeline(
            model=self.model,
            feature_extractor=self.model.feature_extractor,
            tokenizer=self.model.tokenizer,
            device=self.device,
        )

    def __call__(self, data: dict[str, Any]) -> dict[str, Any] | list[dict[str, Any]]:
        """Process an inference request.
        Args:
            data: Request data containing 'inputs' (audio path/bytes) and optional 'parameters'
        Returns:
            Transcription result with 'text' key
        """
        inputs = data.get("inputs")
        if inputs is None:
            raise ValueError("Missing 'inputs' in request data")

        # Pass through any parameters from request, let model config provide defaults
        params = data.get("parameters", {})

        return self.pipe(inputs, **params)

	"""Custom inference handler for HuggingFace Inference Endpoints."""

	from typing import Any

	import torch

	try:
	# For remote execution, imports are relative
	from .asr_modeling import ASRModel
	from .asr_pipeline import ASRPipeline
	except ImportError:
	# For local execution, imports are not relative
	from asr_modeling import ASRModel # type: ignore[no-redef]
	from asr_pipeline import ASRPipeline # type: ignore[no-redef]


	def _best_device() -> torch.device:
	"""Best available inference device (cuda > mps > cpu)."""
	if torch.cuda.is_available():
	return torch.device("cuda")
	if torch.backends.mps.is_available():
	return torch.device("mps")
	return torch.device("cpu")


	class EndpointHandler:
	"""HuggingFace Inference Endpoints handler for ASR model.

	Handles model loading, warmup, and inference requests for deployment
	on HuggingFace Inference Endpoints or similar services.
	"""

	def __init__(self, path: str = ""):
	"""Initialize the endpoint handler.

	Args:
	path: Path to model directory or HuggingFace model ID
	"""
	import os

	import nltk

	nltk.download("punkt_tab", quiet=True)

	os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")

	# `ASRModel.from_pretrained` constructs its own submodules and forwards
	# **kwargs to `ASRModel.__init__`, which discards them -- no loader
	# reads `device_map`, `torch_dtype` or `low_cpu_mem_usage`, and it sets
	# `_is_loading_from_pretrained` precisely to keep `device_map="auto"`
	# out of the sub-model loaders. Passing them here did nothing, so the
	# model stayed on CPU and a GPU Inference Endpoint silently decoded on
	# CPU. Place it explicitly instead. dtype comes from
	# `config.model_dtype` and the attention backend from
	# `config.attn_implementation`, which already downgrades FA2 when
	# flash_attn is missing.
	self.model = ASRModel.from_pretrained(path)
	self.device = _best_device()
	self.model.to(self.device)
	self.model.eval()

	self.pipe = ASRPipeline(
	model=self.model,
	feature_extractor=self.model.feature_extractor,
	tokenizer=self.model.tokenizer,
	device=self.device,
	)

	def __call__(self, data: dict[str, Any]) -> dict[str, Any] \| list[dict[str, Any]]:
	"""Process an inference request.

	Args:
	data: Request data containing 'inputs' (audio path/bytes) and optional 'parameters'

	Returns:
	Transcription result with 'text' key
	"""
	inputs = data.get("inputs")
	if inputs is None:
	raise ValueError("Missing 'inputs' in request data")

	# Pass through any parameters from request, let model config provide defaults
	params = data.get("parameters", {})

	return self.pipe(inputs, **params)