Create README.md

README.md

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+---
+language:
+- ja
+base_model:
+- google/gemma-2-9b-it
+pipeline_tag: any-to-any
+license: gemma
+datasets:
+- fixie-ai/common_voice_17_0
+---
+```py
+import transformers
+import librosa
+import torch
+import numpy as np
+from typing import Dict, Any
+
+model = transformers.AutoModel.from_pretrained(
+    "neody/ultravox-gemma-2-9b-it", trust_remote_code=True
+)
+model.to("cuda", dtype=torch.bfloat16)
+processor = transformers.AutoProcessor.from_pretrained(
+    "neody/ultravox-gemma-2-9b-it", trust_remote_code=True
+)
+path = "record.wav"
+audio, sr = librosa.load(path, sr=16000)
+
+
+def preprocess(inputs: Dict[str, Any], device, dtype):
+    turns: list = inputs.get("turns", [])
+
+    audio = inputs.get("audio", None)
+    # Convert to float32 if needed.
+    if isinstance(audio, np.ndarray):
+        if audio.dtype == np.float64:
+            audio = audio.astype(np.float32)
+        elif audio.dtype == np.int16:
+            audio = audio.astype(np.float32) / np.float32(32768.0)
+        elif audio.dtype == np.int32:
+            audio = audio.astype(np.float32) / np.float32(2147483648.0)
+
+    if audio is not None and (len(turns) == 0 or turns[-1]["role"] != "user"):
+        prompt = inputs.get("prompt", "<|audio|>")
+        if "<|audio|>" not in prompt:
+            print(
+                "Prompt does not contain '<|audio|>', appending '<|audio|>' to the end of the prompt."
+            )
+
+            prompt += " <|audio|>"
+        turns.append({"role": "user", "content": prompt})
+
+    text = processor.tokenizer.apply_chat_template(
+        turns, add_generation_prompt=True, tokenize=False
+    )
+
+    if "sampling_rate" not in inputs and audio is not None:
+        print(
+            "No sampling rate provided, using default of 16kHz. We highly recommend providing the correct sampling rate."
+        )
+
+    output = processor(
+        text=text,
+        audio=audio,
+        sampling_rate=inputs.get("sampling_rate", 16000),
+    )
+    if "audio_values" in output:
+        output["audio_values"] = output["audio_values"].to(device, dtype)
+    return output.to(device, dtype)
+
+
+turns = []
+print(
+    processor.tokenizer.decode(
+        model.generate(
+            **preprocess(
+                {"audio": audio, "turns": turns, "sampling_rate": sr},
+                "cuda",
+                torch.bfloat16,
+            ),
+            max_new_tokens=300,
+        ).squeeze(),
+        skip_special_tokens=True,
+    )
+)
+```