fix: guard _estimate_snr against short clips, fix freqs device in _check_hf_shelf

Bug 1: mono.unfold(0, 2048, 512) returns an empty tensor for clips shorter
than 2048 samples (~46ms). torch.quantile on an empty tensor crashes with
"quantile() input tensor must be non-empty". Guard: return 60.0 (assume
clean) for clips too short to frame — the pipeline has no minimum-length
filter so any short file in the dataset folder would crash the Inspector.

Bug 2: torch.linspace(...) in _check_hf_shelf created a CPU tensor, making
band_lo/band_hi CPU boolean masks. Indexing a GPU mag_sq tensor with CPU
masks crashes. Pass device=mono.device so freqs lands on the same device
as the audio.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nodes/selva_dataset_pipeline.py

@@ -205,7 +205,7 @@ def _check_hf_shelf(wav: torch.Tensor, sr: int) -> bool:
     stft   = torch.stft(mono, n_fft, hop, n_fft, window, return_complex=True)
     mag_sq = stft.abs().pow(2).mean(-1)  # [n_freqs]
 
-    freqs     = torch.linspace(0, sr / 2, n_fft // 2 + 1)
+    freqs     = torch.linspace(0, sr / 2, n_fft // 2 + 1, device=mono.device)
     band_lo   = (freqs >= 1000) & (freqs < 5000)
     band_hi   = (freqs >= 15000) & (freqs < 20000)
 
@@ -221,6 +221,8 @@ def _check_hf_shelf(wav: torch.Tensor, sr: int) -> bool:
 def _estimate_snr(wav: torch.Tensor) -> float:
     """Rough SNR estimate: ratio of 95th-percentile frame RMS to 5th-percentile frame RMS."""
     mono   = wav[0].mean(0)  # [L]
+    if mono.shape[0] < 2048:
+        return 60.0  # clip too short to frame — assume clean
     frames = mono.unfold(0, 2048, 512)          # [N, 2048]
     rms    = frames.pow(2).mean(-1).sqrt()      # [N]
     p95    = torch.quantile(rms, 0.95).item()