86 lines
3.5 KiB
Python
86 lines
3.5 KiB
Python
import requests
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import io
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import librosa # Changed from librosa.core
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import torch
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import numpy # For type hinting, though librosa.load returns numpy array
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import warnings
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class AudioLoadPath:
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@classmethod
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def INPUT_TYPES(cls): # Changed s to cls for convention
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return {
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"required": {
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"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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}
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}
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RETURN_TYPES = ("AUDIO",)
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CATEGORY = "EasyAI" # Or your preferred category
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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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audio_data_source = None
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try:
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if path.startswith(('http://', 'https://')):
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# For network paths, download and load from memory
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response = requests.get(path, timeout=10) # Added timeout
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response.raise_for_status() # Raises an exception for bad status codes
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audio_data_source = io.BytesIO(response.content)
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else:
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# For local paths (absolute or relative)
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audio_data_source = path
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# Use librosa to load audio.
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# mono=False ensures that the output numpy array is always 2D (channels, samples).
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# For mono audio, this will be (1, samples).
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# librosa.load will resample to the target 'sample_rate' if it's provided.
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with warnings.catch_warnings():
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warnings.simplefilter("ignore") # Suppress librosa warnings if any
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audio_np, loaded_sr = librosa.load(
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audio_data_source,
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sr=sample_rate,
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offset=offset,
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duration=duration,
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mono=False # Ensures audio_np is 2D: (channels, samples)
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)
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# Convert numpy array to PyTorch tensor
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audio_tensor = torch.from_numpy(audio_np) # Shape: (channels, samples)
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# Add a batch dimension to conform to (batch_size, channels, samples)
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# Here, batch_size is 1 as we are loading a single audio file.
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audio_tensor = audio_tensor.unsqueeze(0) # Shape: (1, channels, samples)
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# Prepare the output dictionary for the "AUDIO" type
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output_audio_dict = {
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"waveform": audio_tensor,
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"sample_rate": loaded_sr # Use the actual loaded sample rate (should match input 'sample_rate')
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}
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# Return as a tuple, as ComfyUI expects
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return (output_audio_dict,)
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except requests.exceptions.RequestException as e:
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# Handle network-specific errors
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raise Exception(f"Failed to load audio from URL: {str(e)}")
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except FileNotFoundError as e:
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# Handle local file not found errors
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raise Exception(f"Audio file not found: {path} - {str(e)}")
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except Exception as e:
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# Handle other potential errors (e.g., librosa failing to decode, invalid path)
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raise Exception(f"Failed to load audio: {str(e)}")
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# Node mappings for ComfyUI
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NODE_CLASS_MAPPINGS = {
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"AudioLoadPath": AudioLoadPath
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"AudioLoadPath": "Load Audio (Path/URL)"
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} |