Ethanfel
e5110b88e1
New "auto" option (now the default) on the Sampler's input_sr. detect_input_sr finds the spectral cutoff cliff (steepest drop) and its dB confidence: effective cutoff = that cliff if confident, else sr/2 — one rule that covers band-limited (→ matched input_sr), full-band (→ 24000), and genuine low-rate files (→ their rate). Rounds DOWN to the nearest supported Nyquist to avoid feeding the model an empty band. Logs its decision. Falls back to 24000 when unsure. Tests cover sharp 4/6/8/12 kHz cutoffs, full-band, genuine-8kHz, silence, stereo. Verified end-to-end on the real model (8 kHz clip -> auto picks 16000). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
"""Tests for auto input_sr bandwidth detection (detect_input_sr).
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Runnable with pytest, or standalone: python tests/test_auto_input_sr.py
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Only needs torch (no ComfyUI). Loads universr_wrapper by path.
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"""
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import importlib.util
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import os
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import torch
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_ND = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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_spec = importlib.util.spec_from_file_location("_usr", os.path.join(_ND, "universr_wrapper.py"))
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usr = importlib.util.module_from_spec(_spec)
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_spec.loader.exec_module(usr)
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SR = 48000
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def _broadband(seconds=3.0, sr=SR):
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torch.manual_seed(0)
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return (torch.randn(int(sr * seconds)) * 0.3)
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def _brickwall(x, sr, cut):
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X = torch.fft.rfft(x)
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f = torch.fft.rfftfreq(x.shape[-1], 1 / sr)
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X[f > cut] = 0
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return torch.fft.irfft(X, n=x.shape[-1])
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def test_sharp_cutoffs_map_to_expected_input_sr():
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base = _broadband()
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for cut, expected in [(4000, 8000), (6000, 12000), (8000, 16000), (12000, 24000)]:
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x = _brickwall(base, SR, cut).reshape(1, 1, -1)
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isr, info = usr.detect_input_sr(x, SR)
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assert info["confident"], f"cut={cut}: expected a confident cliff, got {info}"
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assert isr == expected, f"cut={cut}: got input_sr={isr} ({info['cutoff_hz']:.0f} Hz)"
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def test_full_band_falls_back_to_24000():
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x = _broadband().reshape(1, 1, -1)
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isr, info = usr.detect_input_sr(x, SR)
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assert not info["confident"]
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assert isr == 24000, info
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def test_genuine_low_rate_file_maps_to_its_rate():
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# A real 8 kHz file: sr=8000, content fills its 4 kHz band -> input_sr 8000.
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torch.manual_seed(1)
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x = (torch.randn(8000 * 3) * 0.3).reshape(1, 1, -1)
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isr, info = usr.detect_input_sr(x, 8000)
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assert isr == 8000, info
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def test_silent_or_tiny_falls_back():
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isr, info = usr.detect_input_sr(torch.zeros(1, 1, 1000), SR)
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assert isr == 24000 and not info["confident"]
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def test_stereo_is_mono_mixed():
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base = _broadband()
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x = _brickwall(base, SR, 8000)
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stereo = torch.stack([x, x], 0).reshape(1, 2, -1)
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isr, _ = usr.detect_input_sr(stereo, SR)
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assert isr == 16000
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if __name__ == "__main__":
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fns = [v for k, v in sorted(globals().items()) if k.startswith("test_")]
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for fn in fns:
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fn()
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print(f"PASS {fn.__name__}")
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print(f"\nAll {len(fns)} tests passed.")
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