138 lines
4.0 KiB
Python
138 lines
4.0 KiB
Python
import pytest
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import torch
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from sam_audio_comfy.audio import (
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AudioChunk,
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crossfade_audio_chunks,
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plan_audio_chunks,
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prepare_audio,
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prepare_visual_prompt,
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result_to_audio,
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slice_visual_frames,
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synchronize_video_audio,
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)
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def test_chunk_plan_covers_audio_with_requested_overlap():
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assert plan_audio_chunks(21, 1, 10.0, 2.0) == [
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AudioChunk(0, 10),
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AudioChunk(8, 18),
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AudioChunk(16, 21),
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]
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assert plan_audio_chunks(21, 1, 0.0, 2.0) == [AudioChunk(0, 21)]
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assert plan_audio_chunks(5, 1, 10.0, 2.0) == [AudioChunk(0, 5)]
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with pytest.raises(ValueError, match="shorter than chunk_duration"):
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plan_audio_chunks(21, 1, 10.0, 10.0)
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def test_crossfade_audio_chunks_normalizes_overlap():
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first = torch.ones(1, 1, 6)
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second = torch.full((1, 1, 6), 3.0)
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output = crossfade_audio_chunks(
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[(AudioChunk(0, 6), first), (AudioChunk(4, 10), second)], 10
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)
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assert torch.equal(output[..., :4], torch.ones(1, 1, 4))
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assert torch.allclose(output[0, 0, 4:6], torch.tensor([5 / 3, 7 / 3]))
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assert torch.equal(output[..., 6:], torch.full((1, 1, 4), 3.0))
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def test_visual_frames_are_sliced_to_chunk_time_range():
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frames = torch.arange(10).view(10, 1)
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assert torch.equal(
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slice_visual_frames(frames, AudioChunk(2, 6), 10), frames[2:6]
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)
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assert torch.equal(
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slice_visual_frames(frames, AudioChunk(0, 10), 10), frames
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)
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def test_visual_frames_use_native_frame_timestamps_when_available():
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frames = torch.arange(30).view(30, 1)
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selected = slice_visual_frames(
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frames,
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AudioChunk(10, 20),
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total_samples=40,
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sample_rate=10,
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frame_rate=5,
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)
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assert torch.equal(selected, frames[5:10])
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def test_native_video_components_are_trimmed_to_common_duration():
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waveforms = [torch.ones(1, 20)]
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frames = torch.ones(8, 3, 2, 2)
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audio_trimmed, length, frames_kept = synchronize_video_audio(
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waveforms, 20, frames, sample_rate=10, frame_rate=8
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)
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assert length == 10
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assert audio_trimmed[0].shape[-1] == 10
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assert frames_kept.shape[0] == 8
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audio_kept, length, frames_trimmed = synchronize_video_audio(
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waveforms, 20, torch.ones(30, 3, 2, 2), sample_rate=10, frame_rate=10
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)
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assert length == 20
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assert audio_kept[0].shape[-1] == 20
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assert frames_trimmed.shape[0] == 20
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def test_prepare_audio_preserves_batch_and_resamples():
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waveform = torch.stack(
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[torch.ones(2, 8), torch.full((2, 8), 0.5)], dim=0
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)
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rows, length = prepare_audio(
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{"waveform": waveform, "sample_rate": 8}, target_sample_rate=4
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)
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assert len(rows) == 2
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assert rows[0].shape == (2, 4)
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assert rows[1].shape == (2, 4)
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assert length == 4
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def test_result_to_audio_crops_and_pads_mono_batch():
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audio = result_to_audio(
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[torch.arange(8), torch.arange(3)], sample_rate=48_000, target_length=5
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)
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assert audio["sample_rate"] == 48_000
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assert audio["waveform"].shape == (2, 1, 5)
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assert torch.equal(audio["waveform"][0, 0], torch.arange(5).float())
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assert torch.equal(
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audio["waveform"][1, 0], torch.tensor([0.0, 1.0, 2.0, 0.0, 0.0])
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)
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def test_prepare_visual_prompt_converts_layout_scale_and_repeats_mask():
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images = torch.zeros(3, 4, 6, 3)
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images[..., 0] = 1.0
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mask = torch.tensor([[0.0, 1.0], [0.0, 0.0]])
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frames, masks = prepare_visual_prompt(images, mask)
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assert frames.shape == (3, 3, 4, 6)
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assert frames.dtype == torch.uint8
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assert frames[:, 0].min().item() == 255
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assert frames[:, 1:].max().item() == 0
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assert masks.shape == (3, 1, 4, 6)
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assert masks.dtype == torch.bool
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assert torch.equal(masks[0], masks[1])
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def test_prepare_visual_prompt_rejects_mismatched_frame_counts():
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images = torch.zeros(3, 4, 6, 3)
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mask = torch.zeros(2, 4, 6)
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try:
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prepare_visual_prompt(images, mask)
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except ValueError as error:
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assert "2 frames" in str(error)
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else:
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raise AssertionError("expected a frame-count validation error")
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