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import json |
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import torch |
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from tqdm import tqdm |
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import torchaudio |
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import librosa |
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import os |
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import math |
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import numpy as np |
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from tools.get_bsrnnvae import get_bsrnnvae |
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import tools.torch_tools as torch_tools |
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class Tango: |
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def __init__(self, \ |
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device="cuda:0"): |
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self.sample_rate = 44100 |
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self.device = device |
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self.vae = get_bsrnnvae() |
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self.vae = self.vae.eval().to(device) |
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def sound2sound_generate_longterm(self, fname, batch_size=1, duration=20.48, steps=200, disable_progress=False): |
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""" Genrate audio without condition. """ |
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num_frames = math.ceil(duration * 100. / 8) |
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with torch.no_grad(): |
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orig_samples, fs = torchaudio.load(fname) |
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if(fs!=44100): |
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orig_samples = torchaudio.functional.resample(orig_samples, fs, 44100) |
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fs = 44100 |
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if(orig_samples.shape[-1]<int(duration*44100*2)): |
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orig_samples = torch.cat([orig_samples, torch.zeros(orig_samples.shape[0], int(duration*44100*2+480)-orig_samples.shape[-1], \ |
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dtype=orig_samples.dtype, device=orig_samples.device)], -1) |
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orig_samples = torch.cat([orig_samples, torch.zeros(orig_samples.shape[0], int(duration * fs)//2, dtype=orig_samples.dtype, device=orig_samples.device)], -1).to(self.device) |
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if(fs!=44100):orig_samples = torchaudio.functional.resample(orig_samples, fs, 44100) |
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resampled_audios = orig_samples[[0],0:int(duration*2*44100)+480].clamp(-1,1) |
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orig_samples = orig_samples[[0],0:int(duration*2*44100)] |
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audio = self.vae(orig_samples[:,None,:])[:,0,:] |
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if(orig_samples.shape[-1]<audio.shape[-1]): |
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orig_samples = torch.cat([orig_samples, torch.zeros(orig_samples.shape[0], audio.shape[-1]-orig_samples.shape[-1], dtype=orig_samples.dtype, device=orig_samples.device)],-1) |
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else: |
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orig_samples = orig_samples[:,0:audio.shape[-1]] |
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output = torch.cat([orig_samples.detach().cpu(),audio.detach().cpu()],0) |
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return output |
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