import argparse, os, sys, glob import pathlib directory = pathlib.Path(os.getcwd()) print(directory) sys.path.append(str(directory)) import torch import numpy as np from omegaconf import OmegaConf from PIL import Image from tqdm import tqdm, trange from ldm.util import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler import pandas as pd from torch.utils.data import DataLoader from tqdm import tqdm from icecream import ic from pathlib import Path import yaml from vocoder.bigvgan.models import VocoderBigVGAN import soundfile # from pytorch_memlab import LineProfiler,profile def load_model_from_config(config, ckpt = None, verbose=True): model = instantiate_from_config(config.model) if ckpt: print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") sd = pl_sd["state_dict"] m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") print(m) if len(u) > 0 and verbose: print("unexpected keys:") print(u) else: print(f"Note chat no ckpt is loaded !!!") model.cuda() model.eval() return model def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( "--prompt_txt", type=str, nargs="?", default="prompt.txt", help="txt file with prompts in it" ) parser.add_argument( "--sample_rate", type=int, default="22050", help="sample rate of wav" ) parser.add_argument( "--inpaint", action='store_true', help="if test txt guided inpaint task" ) parser.add_argument( "--test-dataset", default="none", help="test which dataset: audiocaps/clotho/fsd50k" ) parser.add_argument( "--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2audio-samples" ) parser.add_argument( "--ddim_steps", type=int, default=100, help="number of ddim sampling steps", ) parser.add_argument( "--plms", action='store_true', help="use plms sampling", ) parser.add_argument( "--ddim_eta", type=float, default=0.0, help="ddim eta (eta=0.0 corresponds to deterministic sampling", ) parser.add_argument( "--n_iter", type=int, default=1, help="sample this often", ) parser.add_argument( "--H", type=int, default=80, help="image height, in pixel space", ) parser.add_argument( "--W", type=int, default=848, help="image width, in pixel space", ) parser.add_argument( "--n_samples", type=int, default=1, help="how many samples to produce for the given prompt", ) parser.add_argument( "--scale", type=float, default=5.0, # if it's 1, only condition is taken into consideration help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", ) parser.add_argument( "-r", "--resume", type=str, const=True, default="", nargs="?", help="resume from logdir or checkpoint in logdir", ) parser.add_argument( "-b", "--base", type=str, help="paths to base configs. Loaded from left-to-right. " "Parameters can be overwritten or added with command-line options of the form `--key value`.", default="", ) parser.add_argument( "--vocoder-ckpt", type=str, help="paths to vocoder checkpoint", default='vocoder/logs/audioset', ) return parser.parse_args() class GenSamples: def __init__(self,opt,sampler,model,outpath,vocoder = None,save_mel = True,save_wav = True) -> None: self.opt = opt self.sampler = sampler self.model = model self.outpath = outpath if save_wav: assert vocoder is not None self.vocoder = vocoder self.save_mel = save_mel self.save_wav = save_wav self.channel_dim = self.model.channels def gen_test_sample(self,prompt,mel_name = None,wav_name = None):# prompt is {'ori_caption':’xxx‘,'struct_caption':'xxx'} uc = None record_dicts = [] # if os.path.exists(os.path.join(self.outpath,mel_name+f'_0.npy')): # return record_dicts if self.opt.scale != 1.0: emptycap = {'ori_caption':self.opt.n_samples*[""],'struct_caption':self.opt.n_samples*[""]} uc = self.model.get_learned_conditioning(emptycap) for n in range(self.opt.n_iter):# trange(self.opt.n_iter, desc="Sampling"): for k,v in prompt.items(): prompt[k] = self.opt.n_samples * [v] c = self.model.get_learned_conditioning(prompt)# shape:[1,77,1280],即还没有变成句子embedding，仍是每个单词的embedding if self.channel_dim>0: shape = [self.channel_dim, self.opt.H, self.opt.W] # (z_dim, 80//2^x, 848//2^x) else: shape = [self.opt.H, self.opt.W] samples_ddim, _ = self.sampler.sample(S=self.opt.ddim_steps, conditioning=c, batch_size=self.opt.n_samples, shape=shape, verbose=False, unconditional_guidance_scale=self.opt.scale, unconditional_conditioning=uc, # quantize_x0=use_quantize, eta=self.opt.ddim_eta) x_samples_ddim = self.model.decode_first_stage(samples_ddim) for idx,spec in enumerate(x_samples_ddim): spec = spec.squeeze(0).cpu().numpy() record_dict = {'caption':prompt['ori_caption'][0]} if self.save_mel: mel_path = os.path.join(self.outpath,mel_name+f'_{idx}.npy') np.save(mel_path,spec) record_dict['mel_path'] = mel_path if self.save_wav: wav = self.vocoder.vocode(spec) wav_path = os.path.join(self.outpath,wav_name+f'_{idx}.wav') soundfile.write(wav_path, wav, self.opt.sample_rate) record_dict['audio_path'] = wav_path record_dicts.append(record_dict) return record_dicts def main(): opt = parse_args() config = OmegaConf.load(opt.base) # print("-------quick debug no load ckpt---------") # model = instantiate_from_config(config['model'])# for quick debug model = load_model_from_config(config, opt.resume) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model.to(device) if opt.plms: sampler = PLMSSampler(model) else: sampler = DDIMSampler(model) os.makedirs(opt.outdir, exist_ok=True) if 'mel' in opt.vocoder_ckpt: vocoder = VocoderMelGan(opt.vocoder_ckpt,device) elif 'hifi' in opt.vocoder_ckpt: vocoder = VocoderHifigan(opt.vocoder_ckpt,device) elif 'bigv' in opt.vocoder_ckpt: vocoder = VocoderBigVGAN(opt.vocoder_ckpt,device) generator = GenSamples(opt,sampler,model,opt.outdir,vocoder,save_mel = False,save_wav = True) csv_dicts = [] with torch.no_grad(): with model.ema_scope(): if opt.test_dataset != 'none': if opt.test_dataset == 'audiocaps': test_dataset = instantiate_from_config(config['test_dataset']) elif opt.test_dataset == 'clotho': test_dataset = instantiate_from_config(config['test_dataset2']) elif opt.test_dataset == 'fsd50k': test_dataset = instantiate_from_config(config['test_dataset3']) elif opt.test_dataset == 'musiccap': test_dataset = instantiate_from_config(config['test_dataset']) print(f"Dataset: {type(test_dataset)} LEN: {len(test_dataset)}") for item in tqdm(test_dataset): import ipdb # ipdb.set_trace() prompt,f_name = item['caption'],item['f_name'] vname_num_split_index = f_name.rfind('_')# file_names[b]:video_name+'_'+num v_n,num = f_name[:vname_num_split_index],f_name[vname_num_split_index+1:] mel_name = f'{v_n}_sample_{num}' wav_name = f'{v_n}_sample_{num}' # write_gt_wav(v_n,opt.test_dataset2,opt.outdir,opt.sample_rate) csv_dicts.extend(generator.gen_test_sample(prompt,mel_name=mel_name,wav_name=wav_name)) df = pd.DataFrame.from_dict(csv_dicts) df.to_csv(os.path.join(opt.outdir,'result.csv'),sep='\t',index=False) else: with open(opt.prompt_txt,'r') as f: prompts = f.readlines() for prompt in prompts: wav_name = f'{prompt.strip().replace(" ", "-")}' generator.gen_test_sample(prompt,wav_name=wav_name) print(f"Your samples are ready and waiting four you here: \n{opt.outdir} \nEnjoy.") if __name__ == "__main__": main()