| """Regenerate spectrogram PNGs for inference_demo/ using librosa.display.specshow |
| (matches AudioLDM's _plot_combined style). |
| |
| Each pair produces a 3-row figure (BG / FG Pred / FG GT) with: |
| - time (s) on x-axis |
| - mel bin on y-axis |
| - dB-scale colorbar |
| - model + sample_id title |
| """ |
| from pathlib import Path |
| import sys |
| import numpy as np |
| import torchaudio |
| import librosa |
| import librosa.display |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
|
|
|
|
| def load_mono(path, target_sr): |
| wav, sr = torchaudio.load(str(path)) |
| if wav.shape[0] > 1: |
| wav = wav.mean(0, keepdim=True) |
| if sr != target_sr: |
| wav = torchaudio.functional.resample(wav, sr, target_sr) |
| return wav.squeeze(0).numpy() |
|
|
|
|
| def mel_db(wav, sr, n_fft, hop, n_mels, fmax): |
| return librosa.power_to_db( |
| librosa.feature.melspectrogram( |
| y=wav, sr=sr, n_fft=n_fft, hop_length=hop, |
| n_mels=n_mels, fmin=0, fmax=fmax, |
| ), |
| ref=np.max, |
| ) |
|
|
|
|
| def plot_triple(bg_wav, fg_pred_wav, fg_gt_wav, sr, title, out_png, |
| n_fft=1024, hop=256, n_mels=80): |
| fmax = sr // 2 |
| fig, axes = plt.subplots(3, 1, figsize=(10, 7.2), dpi=120, sharex=True) |
| fig.suptitle(title, fontsize=12, fontweight="bold") |
| for ax, wav, label in zip( |
| axes, |
| [bg_wav, fg_pred_wav, fg_gt_wav], |
| ["BG (input)", "FG Pred (model output)", "FG GT (target)"], |
| ): |
| mel = mel_db(np.asarray(wav, dtype=np.float32), sr, |
| n_fft=n_fft, hop=hop, n_mels=n_mels, fmax=fmax) |
| img = librosa.display.specshow( |
| mel, sr=sr, hop_length=hop, fmin=0, fmax=fmax, |
| x_axis="time", y_axis="mel", ax=ax, |
| ) |
| ax.set_title(label, fontsize=10, loc="left") |
| ax.set_ylabel("Hz (mel)", fontsize=9) |
| plt.colorbar(img, ax=ax, format="%+2.0f dB", pad=0.01) |
| axes[-1].set_xlabel("Time (s)", fontsize=10) |
| fig.tight_layout(rect=(0, 0, 1, 0.96)) |
| out_png.parent.mkdir(parents=True, exist_ok=True) |
| fig.savefig(out_png, bbox_inches="tight") |
| plt.close(fig) |
|
|
|
|
| def main(): |
| root = Path("/home/dingqy/inference_demo") |
| configs = [ |
| ("sa", 44100, "Stable Audio Open 1.0"), |
| ("frieren", 16000, "Frieren-V2A"), |
| ] |
| for subdir, sr, model_label in configs: |
| d = root / subdir |
| if not d.exists(): |
| print(f"skip {d} (missing)") |
| continue |
| |
| bg_files = sorted(d.glob("*_bg.wav")) |
| print(f"[{subdir}] {len(bg_files)} pairs (sr={sr})") |
| for bg_p in bg_files: |
| stem = bg_p.name[:-len("_bg.wav")] |
| fg_gt_p = d / f"{stem}_fg_gt.wav" |
| fg_pred_p = d / f"{stem}_fg_pred.wav" |
| if not (fg_gt_p.exists() and fg_pred_p.exists()): |
| print(f" skip {stem} (missing gt/pred)") |
| continue |
| bg = load_mono(bg_p, sr) |
| fg_pred = load_mono(fg_pred_p, sr) |
| fg_gt = load_mono(fg_gt_p, sr) |
| out = d / f"{stem}_spec.png" |
| sid = stem.replace("val_", "", 1) |
| plot_triple( |
| bg, fg_pred, fg_gt, sr, |
| title=f"{model_label} — {sid}", |
| out_png=out, |
| ) |
| print(f" wrote {out.name}") |
| print("done") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
