307 lines
11 KiB
Python
307 lines
11 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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class SelvaDatasetResampler:
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"""Resample all clips in a dataset to a target sample rate using soxr VHQ."""
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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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"dataset": (AUDIO_DATASET,),
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"target_sr": ("INT", {
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"default": 44100, "min": 8000, "max": 192000,
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"tooltip": "Target sample rate. 44100 for large SelVA model, 16000 for small.",
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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 = "resample"
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CATEGORY = SELVA_CATEGORY
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DESCRIPTION = "Resample all clips to target_sr using soxr VHQ. Skips clips already at target rate."
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def resample(self, dataset, target_sr: int):
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import soxr
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out = []
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changed = 0
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for item in dataset:
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sr = item["sample_rate"]
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if sr == target_sr:
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out.append(item)
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continue
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wav = item["waveform"][0] # [C, L]
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# soxr expects [L, C] (time-first), float64
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wav_np = wav.permute(1, 0).double().numpy() # [L, C]
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wav_rs = soxr.resample(wav_np, sr, target_sr, quality="VHQ")
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wav_t = torch.from_numpy(wav_rs).float().permute(1, 0).unsqueeze(0) # [1, C, L]
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out.append({"waveform": wav_t, "sample_rate": target_sr, "name": item["name"]})
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changed += 1
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print(f"[DatasetResampler] {changed}/{len(dataset)} clips resampled → {target_sr} Hz", flush=True)
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return (out,)
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class SelvaDatasetLUFSNormalizer:
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"""Normalize each clip to a target integrated LUFS level + true peak limit."""
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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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"dataset": (AUDIO_DATASET,),
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"target_lufs": ("FLOAT", {
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"default": -23.0, "min": -40.0, "max": -6.0, "step": 0.5,
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"tooltip": "Target integrated loudness in LUFS. -23 is EBU R128 standard.",
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}),
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"true_peak_dbtp": ("FLOAT", {
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"default": -1.0, "min": -6.0, "max": 0.0, "step": 0.5,
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"tooltip": "True peak ceiling in dBTP. Applied after LUFS gain.",
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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 = "normalize"
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CATEGORY = SELVA_CATEGORY
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DESCRIPTION = (
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"Normalize each clip to target_lufs (BS.1770-4) then apply a true peak ceiling. "
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"Skips clips that are too short for LUFS measurement (< 0.4 s)."
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)
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def normalize(self, dataset, target_lufs: float, true_peak_dbtp: float):
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import pyloudnorm as pyln
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tp_linear = 10.0 ** (true_peak_dbtp / 20.0)
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out = []
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skipped = 0
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for item in dataset:
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wav = item["waveform"][0] # [C, L]
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sr = item["sample_rate"]
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# pyloudnorm wants [L] mono or [L, C] multichannel, float64
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wav_np = wav.permute(1, 0).double().numpy() # [L, C]
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if wav_np.shape[1] == 1:
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wav_np = wav_np[:, 0] # [L] mono
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meter = pyln.Meter(sr)
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try:
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loudness = meter.integrated_loudness(wav_np)
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except Exception:
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skipped += 1
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out.append(item)
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continue
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if not np.isfinite(loudness):
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skipped += 1
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out.append(item)
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continue
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gain_db = target_lufs - loudness
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gain_linear = 10.0 ** (gain_db / 20.0)
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wav_norm = wav * gain_linear
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# True peak limit
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peak = wav_norm.abs().max().item()
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if peak > tp_linear:
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wav_norm = wav_norm * (tp_linear / peak)
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out.append({"waveform": wav_norm.unsqueeze(0), "sample_rate": sr, "name": item["name"]})
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print(
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f"[LUFSNormalizer] {len(dataset) - skipped}/{len(dataset)} clips normalized "
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f"target={target_lufs} LUFS TP={true_peak_dbtp} dBTP skipped={skipped}",
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flush=True,
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)
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return (out,)
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def _check_hf_shelf(wav: torch.Tensor, sr: int) -> bool:
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"""Return True if clip looks codec-compressed (hard HF shelf above 15 kHz).
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Method: compare mean energy in 1–5 kHz band vs 15–20 kHz band via STFT.
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A ratio > 40 dB (i.e. near-silence above 15 kHz) flags codec artifacts.
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"""
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if sr < 32000:
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return False # can't assess HF at low sample rates
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n_fft = 2048
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hop = 512
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mono = wav[0].mean(0) # [L]
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window = torch.hann_window(n_fft, device=mono.device)
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stft = torch.stft(mono, n_fft, hop, n_fft, window, return_complex=True)
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mag_sq = stft.abs().pow(2).mean(-1) # [n_freqs]
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freqs = torch.linspace(0, sr / 2, n_fft // 2 + 1)
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band_lo = (freqs >= 1000) & (freqs < 5000)
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band_hi = (freqs >= 15000) & (freqs < 20000)
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if band_hi.sum() == 0:
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return False
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energy_lo = mag_sq[band_lo].mean().clamp(min=1e-12)
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energy_hi = mag_sq[band_hi].mean().clamp(min=1e-12)
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ratio_db = 10.0 * torch.log10(energy_lo / energy_hi).item()
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return ratio_db > 40.0
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def _estimate_snr(wav: torch.Tensor) -> float:
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"""Rough SNR estimate: ratio of 95th-percentile frame RMS to 5th-percentile frame RMS."""
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mono = wav[0].mean(0) # [L]
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frames = mono.unfold(0, 2048, 512) # [N, 2048]
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rms = frames.pow(2).mean(-1).sqrt() # [N]
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p95 = torch.quantile(rms, 0.95).item()
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p05 = torch.quantile(rms, 0.05).clamp(min=1e-8).item()
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return 20.0 * np.log10(p95 / p05 + 1e-8)
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class SelvaDatasetInspector:
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"""Analyze each clip for quality issues and optionally filter out flagged clips."""
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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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"dataset": (AUDIO_DATASET,),
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"skip_rejected": ("BOOLEAN", {
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"default": True,
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"tooltip": "If True, flagged clips are removed from the output dataset. "
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"If False, all clips pass through but the report still lists issues.",
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}),
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"min_snr_db": ("FLOAT", {
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"default": 15.0, "min": 0.0, "max": 60.0, "step": 1.0,
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"tooltip": "Clips with estimated SNR below this value are flagged.",
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}),
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"check_codec_artifacts": ("BOOLEAN", {
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"default": True,
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"tooltip": "Flag clips with a hard HF shelf above 15 kHz (MP3/codec artifact signature).",
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}),
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}
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}
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RETURN_TYPES = (AUDIO_DATASET, "STRING")
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RETURN_NAMES = ("dataset", "report")
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FUNCTION = "inspect"
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CATEGORY = SELVA_CATEGORY
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DESCRIPTION = (
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"Analyze each clip for clipping, low SNR, and codec artifacts. "
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"Outputs a filtered AUDIO_DATASET and a text report. "
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"Connect report to a ShowText node to preview in the UI."
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)
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def inspect(self, dataset, skip_rejected: bool, min_snr_db: float, check_codec_artifacts: bool):
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clean = []
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flagged = []
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lines = ["SelVA Dataset Inspector Report", "=" * 40]
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for item in dataset:
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wav = item["waveform"]
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sr = item["sample_rate"]
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name = item["name"]
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issues = []
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# Clipping
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peak = wav.abs().max().item()
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if peak > 0.99:
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issues.append(f"clipping (peak={peak:.3f})")
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# Low SNR
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snr = _estimate_snr(wav)
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if snr < min_snr_db:
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issues.append(f"low SNR ({snr:.1f} dB < {min_snr_db} dB)")
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# Codec artifacts
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if check_codec_artifacts and _check_hf_shelf(wav, sr):
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issues.append("codec artifact (HF shelf > 15 kHz)")
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if issues:
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flagged.append(name)
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lines.append(f" FLAGGED {name}: {', '.join(issues)}")
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if not skip_rejected:
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clean.append(item)
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else:
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clean.append(item)
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lines.append(f" OK {name}")
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lines.append("=" * 40)
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lines.append(
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f"Total: {len(dataset)} Clean: {len(clean)} Flagged: {len(flagged)}"
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+ (" (removed)" if skip_rejected else " (kept)")
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)
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report = "\n".join(lines)
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print(f"[DatasetInspector]\n{report}", flush=True)
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return (clean, report)
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