up

handler.py

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+from typing import Dict, List, Any
+from transformers import AutoProcessor, MusicgenForConditionalGeneration
+import torch
+
+processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
+model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
+
+inputs = processor(
+    text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
+    padding=True,
+    return_tensors="pt",
+)
+
+audio_values = model.generate(**inputs, max_new_tokens=256)
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and processor from path
+        self.processor = AutoProcessor.from_pretrained(path)
+        self.model = MusicgenForConditionalGeneration.from_pretrained(path)
+        # self.model =  AutoModelForSeq2SeqLM.from_pretrained(path, device_map="auto", load_in_8bit=True)
+        # self.tokenizer = AutoTokenizer.from_pretrained(path)
+
+    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
+        # input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids
+        inputs = processor(
+            text=inputs,
+            padding=True,
+            return_tensors="pt",)
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            outputs = self.model.generate(inputs, max_new_tokens=256, **parameters)
+        else:
+            outputs = self.model.generate(inputs, max_new_tokens=256)
+
+        # postprocess the prediction
+        prediction = outputs[0].numpy()
+
+        return [{"generated_audio": prediction}]