forked from mrq/tortoise-tts
44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
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import torch
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import torchaudio
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def load_wav_to_torch(full_path):
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sampling_rate, data = read(full_path)
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if data.dtype == np.int32:
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norm_fix = 2 ** 31
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elif data.dtype == np.int16:
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norm_fix = 2 ** 15
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elif data.dtype == np.float16 or data.dtype == np.float32:
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norm_fix = 1.
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else:
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raise NotImplemented(f"Provided data dtype not supported: {data.dtype}")
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return (torch.FloatTensor(data.astype(np.float32)) / norm_fix, sampling_rate)
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def load_audio(audiopath, sampling_rate):
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if audiopath[-4:] == '.wav':
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audio, lsr = load_wav_to_torch(audiopath)
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elif audiopath[-4:] == '.mp3':
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# https://github.com/neonbjb/pyfastmp3decoder - Definitely worth it.
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from pyfastmp3decoder.mp3decoder import load_mp3
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audio, lsr = load_mp3(audiopath, sampling_rate)
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audio = torch.FloatTensor(audio)
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# Remove any channel data.
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if len(audio.shape) > 1:
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if audio.shape[0] < 5:
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audio = audio[0]
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else:
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assert audio.shape[1] < 5
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audio = audio[:, 0]
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if lsr != sampling_rate:
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audio = torchaudio.functional.resample(audio, lsr, sampling_rate)
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# Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
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# '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.
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if torch.any(audio > 2) or not torch.any(audio < 0):
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print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
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audio.clip_(-1, 1)
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return audio.unsqueeze(0)
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