69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
"""SelVA Audio Dataset Pipeline — chainable in-memory preprocessing nodes.
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Typical chain:
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SelvaDatasetLoader
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↓ AUDIO_DATASET
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SelvaDatasetResampler (optional)
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↓ AUDIO_DATASET
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SelvaDatasetLUFSNormalizer (optional)
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↓ AUDIO_DATASET
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SelvaDatasetInspector (optional)
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↓ AUDIO_DATASET + STRING report
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SelvaDatasetItemExtractor → AUDIO (bridges to save/preview nodes)
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"""
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from pathlib import Path
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import numpy as np
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import torch
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import torchaudio
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from .utils import SELVA_CATEGORY
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# ComfyUI custom type name — passed between all dataset pipeline nodes
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AUDIO_DATASET = "AUDIO_DATASET"
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_AUDIO_EXTS = {".wav", ".flac", ".mp3", ".ogg", ".aac", ".m4a"}
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class SelvaDatasetLoader:
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"""Load all audio files in a folder into an in-memory AUDIO_DATASET."""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"folder": ("STRING", {
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"default": "",
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"tooltip": "Absolute path to folder containing audio files. Searched recursively.",
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}),
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}
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}
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RETURN_TYPES = (AUDIO_DATASET,)
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RETURN_NAMES = ("dataset",)
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FUNCTION = "load"
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CATEGORY = SELVA_CATEGORY
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DESCRIPTION = "Load all audio files from a folder into memory as an AUDIO_DATASET."
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def load(self, folder: str):
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folder = Path(folder.strip())
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if not folder.exists():
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raise FileNotFoundError(f"[DatasetLoader] Folder not found: {folder}")
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files = [f for f in folder.rglob("*") if f.suffix.lower() in _AUDIO_EXTS]
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if not files:
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raise RuntimeError(f"[DatasetLoader] No audio files found in {folder}")
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dataset = []
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for f in sorted(files):
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try:
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wav, sr = torchaudio.load(str(f)) # [C, L]
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wav = wav.unsqueeze(0).float() # [1, C, L]
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dataset.append({"waveform": wav, "sample_rate": sr, "name": f.stem})
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except Exception as e:
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print(f"[DatasetLoader] Skipping {f.name}: {e}", flush=True)
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print(f"[DatasetLoader] Loaded {len(dataset)} clips from {folder}", flush=True)
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return (dataset,)
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