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from typing import Dict, List, Any
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import torch
class EndpointHandler:
def __init__(self, path=""):
# load model and processor from path
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.model = ParlerTTSForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float16).to("cuda")
def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
"""
Args:
data (:dict:):
The payload with the text prompt and generation parameters.
"""
# process input
inputs = data.pop("inputs", data)
voice_description = data.pop("voice_description", "data")
parameters = data.pop("parameters", None)
gen_kwargs = {"min_new_tokens": 10}
if parameters is not None:
gen_kwargs.update(parameters)
# preprocess
inputs = self.tokenizer(
text=[inputs],
padding=True,
return_tensors="pt",).to("cuda")
voice_description = self.tokenizer(
text=[voice_description],
padding=True,
return_tensors="pt",).to("cuda")
# pass inputs with all kwargs in data
with torch.autocast("cuda"):
outputs = self.model.generate(**voice_description, prompt_input_ids=inputs.input_ids, **gen_kwargs)
# postprocess the prediction
prediction = outputs[0].cpu().numpy().tolist()
return [{"generated_audio": prediction}] |