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from pathlib import Path |
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import torch |
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from audiocraft.data.audio import audio_write |
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def get_white_noise(chs: int = 1, num_frames: int = 1): |
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wav = torch.randn(chs, num_frames) |
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return wav |
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def get_batch_white_noise(bs: int = 1, chs: int = 1, num_frames: int = 1): |
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wav = torch.randn(bs, chs, num_frames) |
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return wav |
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def save_wav(path: str, wav: torch.Tensor, sample_rate: int): |
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assert wav.dim() == 2, wav.shape |
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fp = Path(path) |
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assert fp.suffix in ['.mp3', '.ogg', '.wav', '.flac'], fp |
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audio_write(fp.parent / fp.stem, wav, sample_rate, fp.suffix[1:], |
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normalize=False, strategy='clip', peak_clip_headroom_db=0) |
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