root
b02138669a
Delay-and-Sum still uses the tutorial STFT/cov/GCC-PHAT/DelaySum/ISTFT path. TDOA is refreshed every BEAMFORM_TDOA_EVERY hops (default 5).
107 lines
2.9 KiB
Python
107 lines
2.9 KiB
Python
import inspect
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import numpy as np
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import pytest
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from audio_cleanup import AudioCleaner
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def test_enhance_batch_without_lengths_typeerror():
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c = AudioCleaner(mode="force")
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assert "model" in c.backend
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import torch
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w = torch.zeros(1, 8000)
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with pytest.raises(TypeError):
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c._enhancer.enhance_batch(w)
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def test_process_uses_relative_lengths_one():
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src = inspect.getsource(AudioCleaner._process_inner)
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assert "tensor([1.0])" in src
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def test_process_preserves_length_and_int16():
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c = AudioCleaner(mode="force")
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n = 16000
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pcm = (np.sin(2 * np.pi * 440 * np.arange(n) / 16000) * 8000).astype(np.int16)
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out = c.process(pcm)
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assert out.dtype == np.int16
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assert out.shape == pcm.shape
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def test_overlap_process_emits_hop_length():
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c = AudioCleaner(mode="force", chunk_s=1.0, hop_s=0.2)
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hop = 3200
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pcm = (np.random.randn(hop) * 500).astype(np.int16)
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out = c.process(pcm)
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assert out.shape == (hop,)
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def test_overlap_runs_model_after_enough_context():
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c = AudioCleaner(mode="force", chunk_s=0.5, hop_s=0.25)
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hop = 4000
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for _ in range(4):
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pcm = (np.sin(2 * np.pi * 440 * np.arange(hop) / 16000) * 8000).astype(
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np.int16
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)
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out = c.process(pcm)
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assert out.shape == (hop,)
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assert out.dtype == np.int16
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def test_beamform_loads_delaysum_module():
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c = AudioCleaner(mode="off", beamform="force")
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assert c._beamformer is not None
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assert type(c._beamformer).__name__ == "DelaySum_Beamformer"
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assert "beamform" in c.backend
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def test_beamform_skips_normalizer_at_native_rate():
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c = AudioCleaner(mode="off", beamform="force", sample_rate=16000)
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assert c._normalizer is None
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def test_tdoa_reused_across_hops():
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c = AudioCleaner(mode="off", beamform="force", tdoa_every=5)
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assert c._gccphat is not None
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calls = {"n": 0}
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orig = c._gccphat
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def wrapped(*a, **k):
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calls["n"] += 1
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return orig(*a, **k)
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c._gccphat = wrapped
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hop = 3200
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t = np.arange(hop)
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ch0 = (np.sin(2 * np.pi * 440 * t / 16000) * 8000).astype(np.int16)
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stereo = np.stack([ch0, np.roll(ch0, 2)], axis=1)
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for _ in range(3):
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out = c.process(stereo)
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assert out.shape == (hop,)
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assert calls["n"] == 1
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def test_process_records_last_dt_ms():
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c = AudioCleaner(mode="off", beamform="off")
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hop = 3200
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pcm = (np.random.randn(hop) * 500).astype(np.int16)
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c.process(pcm)
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assert c.last_dt_ms >= 0.0
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def test_stereo_beamform_then_model_preserves_hop():
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c = AudioCleaner(
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mode="force", beamform="force", chunk_s=0.5, hop_s=0.25)
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assert c.backend == "model+beamform"
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hop = 4000
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t = np.arange(hop)
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ch0 = (np.sin(2 * np.pi * 440 * t / 16000) * 8000).astype(np.int16)
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ch1 = np.roll(ch0, 3)
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stereo = np.stack([ch0, ch1], axis=1)
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for _ in range(4):
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out = c.process(stereo)
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assert out.dtype == np.int16
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assert out.shape == (hop,)
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