51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
import soundfile as sf
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import requests
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import io
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import numpy as np
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import torch
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class AudioLoadPath:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "path": ("STRING", {"default": "X://insert/path/here.mp4"}),
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"sample_rate": ("INT", {"default": 22050, "min": 6000, "max": 192000, "step": 1}),
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"offset": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1e6, "step": 0.001}),
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"duration": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1e6, "step": 0.001})}}
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RETURN_TYPES = ("AUDIO", )
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CATEGORY = "Audio Reactor"
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FUNCTION = "load"
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def load(self, path: str, sample_rate: int, offset: float, duration: float|None):
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if duration == 0.0:
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duration = None
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if path.startswith(('http://', 'https://')):
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# 对于网络路径，直接从内存加载
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try:
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response = requests.get(path)
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response.raise_for_status()
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audio_data = io.BytesIO(response.content)
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# 使用 soundfile 从内存中读取音频数据
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audio, file_sr = sf.read(audio_data)
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# 如果需要重采样
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if file_sr != sample_rate:
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# 这里需要添加重采样逻辑
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# 可以使用 librosa.resample 或其他方法
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pass
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except Exception as e:
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raise Exception(f"加载网络音频失败: {str(e)}")
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else:
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# 本地文件使用原有的 librosa 方式加载
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audio, _ = librosa.load(path, sr=sample_rate, offset=offset, duration=duration)
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# 转换为 torch tensor 并调整维度
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audio = torch.from_numpy(audio)[None,:,None]
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return (audio,)
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NODE_CLASS_MAPPINGS = {
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"AudioLoadPath": AudioLoadPath,
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} |