|
|
import json |
|
|
import torch |
|
|
from tqdm import tqdm |
|
|
import torchaudio |
|
|
import librosa |
|
|
import os |
|
|
import math |
|
|
import numpy as np |
|
|
from get_melvaehifigan48k import build_pretrained_models |
|
|
import tools.torch_tools as torch_tools |
|
|
|
|
|
class Tango: |
|
|
def __init__(self, \ |
|
|
device="cuda:0"): |
|
|
|
|
|
self.sample_rate = 48000 |
|
|
self.device = device |
|
|
|
|
|
self.vae, self.stft = build_pretrained_models() |
|
|
self.vae, self.stft = self.vae.eval().to(device), self.stft.eval().to(device) |
|
|
|
|
|
def mel_spectrogram_to_waveform(self, mel_spectrogram): |
|
|
if mel_spectrogram.dim() == 4: |
|
|
mel_spectrogram = mel_spectrogram.squeeze(1) |
|
|
|
|
|
waveform = self.vocoder(mel_spectrogram) |
|
|
|
|
|
waveform = waveform.cpu().float() |
|
|
return waveform |
|
|
|
|
|
def sound2sound_generate_longterm(self, fname, batch_size=1, duration=10.24, 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(orig_samples.shape[-1]<int(duration*48000)): |
|
|
orig_samples = orig_samples.repeat(1,math.ceil(int(duration*48000)/float(orig_samples.shape[-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!=48000):orig_samples = torchaudio.functional.resample(orig_samples, fs, 48000) |
|
|
|
|
|
resampled_audios = orig_samples[[0],0:int(duration*48000)+480].clamp(-1,1) |
|
|
orig_samples = orig_samples[[0],0:int(duration*48000)] |
|
|
|
|
|
mel, _, _ = torch_tools.wav_to_fbank2(resampled_audios, -1, fn_STFT=self.stft) |
|
|
mel = mel.unsqueeze(1).to(self.device) |
|
|
|
|
|
audio = self.vae.decode_to_waveform(mel) |
|
|
audio = torch.from_numpy(audio) |
|
|
|
|
|
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 |
|
|
|
