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import torch |
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import numpy as np |
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from scipy.io.wavfile import write |
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import torchaudio |
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from audiosr.utilities.audio.audio_processing import griffin_lim |
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def pad_wav(waveform, segment_length): |
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waveform_length = waveform.shape[-1] |
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assert waveform_length > 100, "Waveform is too short, %s" % waveform_length |
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if segment_length is None or waveform_length == segment_length: |
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return waveform |
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elif waveform_length > segment_length: |
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return waveform[:segment_length] |
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elif waveform_length < segment_length: |
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temp_wav = np.zeros((1, segment_length)) |
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temp_wav[:, :waveform_length] = waveform |
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return temp_wav |
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def normalize_wav(waveform): |
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waveform = waveform - np.mean(waveform) |
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waveform = waveform / (np.max(np.abs(waveform)) + 1e-8) |
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return waveform * 0.5 |
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def read_wav_file(filename, segment_length): |
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waveform, sr = torchaudio.load(filename) |
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waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=16000) |
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waveform = waveform.numpy()[0, ...] |
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waveform = normalize_wav(waveform) |
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waveform = waveform[None, ...] |
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waveform = pad_wav(waveform, segment_length) |
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waveform = waveform / np.max(np.abs(waveform)) |
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waveform = 0.5 * waveform |
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return waveform |
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def get_mel_from_wav(audio, _stft): |
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audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1) |
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audio = torch.autograd.Variable(audio, requires_grad=False) |
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melspec, magnitudes, phases, energy = _stft.mel_spectrogram(audio) |
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melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32) |
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magnitudes = torch.squeeze(magnitudes, 0).numpy().astype(np.float32) |
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energy = torch.squeeze(energy, 0).numpy().astype(np.float32) |
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return melspec, magnitudes, energy |
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def inv_mel_spec(mel, out_filename, _stft, griffin_iters=60): |
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mel = torch.stack([mel]) |
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mel_decompress = _stft.spectral_de_normalize(mel) |
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mel_decompress = mel_decompress.transpose(1, 2).data.cpu() |
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spec_from_mel_scaling = 1000 |
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spec_from_mel = torch.mm(mel_decompress[0], _stft.mel_basis) |
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spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0) |
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spec_from_mel = spec_from_mel * spec_from_mel_scaling |
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audio = griffin_lim( |
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torch.autograd.Variable(spec_from_mel[:, :, :-1]), _stft._stft_fn, griffin_iters |
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) |
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audio = audio.squeeze() |
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audio = audio.cpu().numpy() |
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audio_path = out_filename |
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write(audio_path, _stft.sampling_rate, audio) |
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