| import json
|
| import torch
|
| from tqdm import tqdm
|
| import torchaudio
|
| import librosa
|
| import os
|
| import math
|
| import numpy as np
|
| from tools.get_bsrnnvae import get_bsrnnvae
|
| import tools.torch_tools as torch_tools
|
|
|
| class Tango:
|
| def __init__(self, \
|
| device="cuda:0"):
|
|
|
| self.sample_rate = 44100
|
| self.device = device
|
|
|
| self.vae = get_bsrnnvae()
|
| self.vae = self.vae.eval().to(device)
|
|
|
| def sound2sound_generate_longterm(self, fname, batch_size=1, duration=15.36, steps=200, disable_progress=False):
|
| """ Genrate audio without condition. """
|
| num_frames = math.ceil(duration * 100. / 8)
|
| with torch.no_grad():
|
| orig_samples, fs = torchaudio.load(fname)
|
| if(fs!=44100):
|
| orig_samples = torchaudio.functional.resample(orig_samples, fs, 44100)
|
| fs = 44100
|
| if(orig_samples.shape[-1]<int(duration*44100*2)):
|
| orig_samples = torch.cat([orig_samples, torch.zeros(orig_samples.shape[0], int(duration*44100*2+480)-orig_samples.shape[-1], \
|
| dtype=orig_samples.dtype, device=orig_samples.device)], -1)
|
|
|
| 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)
|
| if(fs!=44100):orig_samples = torchaudio.functional.resample(orig_samples, fs, 44100)
|
|
|
| resampled_audios = orig_samples[[0],0:int(duration*2*44100)+480].clamp(-1,1)
|
| orig_samples = orig_samples[[0],0:int(duration*2*44100)]
|
|
|
| audio = self.vae(orig_samples[:,None,:])[:,0,:]
|
|
|
| if(orig_samples.shape[-1]<audio.shape[-1]):
|
| 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)
|
| else:
|
| orig_samples = orig_samples[:,0:audio.shape[-1]]
|
| output = torch.cat([orig_samples.detach().cpu(),audio.detach().cpu()],0)
|
| return output
|
|
|
