Instructions to use derrickdso/samplegen-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use derrickdso/samplegen-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="derrickdso/samplegen-small")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("derrickdso/samplegen-small") model = AutoModelForTextToWaveform.from_pretrained("derrickdso/samplegen-small") - Notebooks
- Google Colab
- Kaggle
File size: 1,320 Bytes
c2f9b04 1f7a2e3 c2f9b04 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | 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, 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)
parameters = data.pop("parameters", None)
# 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:
with torch.autocast("cuda"):
outputs = self.model.generate(**inputs, **parameters)
else:
with torch.autocast("cuda"):
outputs = self.model.generate(**inputs,)
# postprocess the prediction
prediction = outputs[0].cpu().numpy().tolist()
return [{"generated_audio": prediction}] |