od2025/void_signal

1from typing import Dict, List, Any2from parler_tts import ParlerTTSForConditionalGeneration3from transformers import AutoTokenizer4import torch5 6class EndpointHandler:7    def __init__(self, path=""):8        # load model and processor from path9        self.tokenizer = AutoTokenizer.from_pretrained(path)10        self.model = ParlerTTSForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float16).to("cuda")11 12    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:13        """14        Args:15            data (:dict:):16                The payload with the text prompt and generation parameters.17        """18        # process input19        inputs = data.pop("inputs", data)20        voice_description = data.pop("voice_description", "data")21        parameters = data.pop("parameters", None)22 23        gen_kwargs = {"min_new_tokens": 10}24        if parameters is not None:25            gen_kwargs.update(parameters)26 27        # preprocess28        inputs = self.tokenizer(29            text=[inputs],30            padding=True,31            return_tensors="pt",).to("cuda")32        voice_description = self.tokenizer(33            text=[voice_description],34            padding=True,35            return_tensors="pt",).to("cuda")36 37        # pass inputs with all kwargs in data38        with torch.autocast("cuda"):39            outputs = self.model.generate(**voice_description, prompt_input_ids=inputs.input_ids, **gen_kwargs)40 41        # postprocess the prediction42        prediction = outputs[0].cpu().numpy().tolist()43 44        return [{"generated_audio": prediction}]