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Running
on
Zero
Running
on
Zero
| import torch | |
| import numpy as np | |
| from scipy.io.wavfile import write | |
| import pickle | |
| import json | |
| from audioldm_eval.audio.audio_processing import griffin_lim | |
| def save_pickle(obj, fname): | |
| # print("Save pickle at " + fname) | |
| with open(fname, "wb") as f: | |
| pickle.dump(obj, f) | |
| def load_pickle(fname): | |
| # print("Load pickle at " + fname) | |
| with open(fname, "rb") as f: | |
| res = pickle.load(f) | |
| return res | |
| def write_json(my_dict, fname): | |
| # print("Save json file at " + fname) | |
| json_str = json.dumps(my_dict) | |
| with open(fname, "w") as json_file: | |
| json_file.write(json_str) | |
| def load_json(fname): | |
| with open(fname, "r") as f: | |
| data = json.load(f) | |
| return data | |
| def get_mel_from_wav(audio, _stft): | |
| audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1) | |
| audio = torch.autograd.Variable(audio, requires_grad=False) | |
| melspec, energy = _stft.mel_spectrogram(audio) | |
| melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32) | |
| energy = torch.squeeze(energy, 0).numpy().astype(np.float32) | |
| return melspec, energy | |
| def inv_mel_spec(mel, out_filename, _stft, griffin_iters=60): | |
| mel = torch.stack([mel]) | |
| mel_decompress = _stft.spectral_de_normalize(mel) | |
| mel_decompress = mel_decompress.transpose(1, 2).data.cpu() | |
| spec_from_mel_scaling = 1000 | |
| spec_from_mel = torch.mm(mel_decompress[0], _stft.mel_basis) | |
| spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0) | |
| spec_from_mel = spec_from_mel * spec_from_mel_scaling | |
| audio = griffin_lim( | |
| torch.autograd.Variable(spec_from_mel[:, :, :-1]), _stft._stft_fn, griffin_iters | |
| ) | |
| audio = audio.squeeze() | |
| audio = audio.cpu().numpy() | |
| audio_path = out_filename | |
| write(audio_path, _stft.sampling_rate, audio) | |