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on
L40S
Running
on
L40S
| 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) | |
| # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 | |
| 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([torch.zeros(orig_samples.shape[0], int(duration * fs)//2, dtype=orig_samples.dtype, device=orig_samples.device), orig_samples, torch.zeros(orig_samples.shape[0], int(duration * fs)//2, dtype=orig_samples.dtype, device=orig_samples.device)], -1).to(self.device) | |
| 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],int(4.64*48000):int(35.36*48000)+480].clamp(-1,1) | |
| 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) | |
| latents = torch.cat([self.vae.get_first_stage_encoding(self.vae.encode_first_stage(mel[[m]])) for m in range(mel.shape[0])],0) | |
| mel = self.vae.decode_first_stage(latents) | |
| 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 | |