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from typing import Dict, List, Any
from transformers import AutoProcessor, MusicgenForConditionalGeneration
import torch
class EndpointHandler:
def __init__(self, path=""):
# load model and processor from path
self.processor = AutoProcessor.from_pretrained(path)
self.model = MusicgenForConditionalGeneration.from_pretrained(path).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)
parameters = data.pop("parameters", None)
duration = data.pop("duration", 5.0)
duration = max(min(duration, 30.0), 0.0)
max_new_tokens = int(duration * self.model.config.audio_encoder.frame_rate)
# preprocess
inputs = self.processor(
text=[inputs],
padding=True,
return_tensors="pt",).to("cuda")
# pass inputs with all kwargs in data
if parameters is not None:
outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens, **parameters)
else:
outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens)
# postprocess the prediction
prediction = outputs[0].cpu().numpy()
return [{"generated_text": prediction}] |