upload custom handler and requirements.txt for direct compatibility with HF inference endpoints

handler.py

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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}]

requirements.txt

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+git+https://github.com/huggingface/parler-tts.git