import spaces 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.scheduling_lcm import LCMSampler 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 soundfile as sf import yaml import datetime from vocoder.bigvgan.models import VocoderBigVGAN import soundfile # from pytorch_memlab import LineProfiler,profile import gradio import gradio as gr 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 class GenSamples: def __init__(self,sampler,model,outpath,vocoder = None,save_mel = True,save_wav = True, original_inference_steps=None, ddim_steps=2, scale=5, num_samples=1) -> None: 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 self.original_inference_steps = original_inference_steps self.ddim_steps = ddim_steps self.scale = scale self.num_samples = num_samples 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.scale != 1.0: emptycap = {'ori_caption':self.num_samples*[""],'struct_caption':self.num_samples*[""]} uc = self.model.get_learned_conditioning(emptycap) for n in range(1):# trange(self.opt.n_iter, desc="Sampling"): for k,v in prompt.items(): prompt[k] = self.num_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, 20, 312] # (z_dim, 80//2^x, 848//2^x) else: shape = [20, 312] samples_ddim, _ = self.sampler.sample(S=self.ddim_steps, conditioning=c, batch_size=self.num_samples, shape=shape, verbose=False, guidance_scale=self.scale, original_inference_steps=self.original_inference_steps ) 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, 16000) record_dict['audio_path'] = wav_path record_dicts.append(record_dict) return record_dicts @spaces.GPU(enable_queue=True) def infer(ori_prompt, ddim_steps, num_samples, scale, seed): np.random.seed(seed) torch.manual_seed(seed) prompt = dict(ori_caption=ori_prompt,struct_caption=f'<{ori_prompt}& all>') config = OmegaConf.load("configs/audiolcm.yaml") # print("-------quick debug no load ckpt---------") # model = instantiate_from_config(config['model'])# for quick debug model = load_model_from_config(config, "./model/000184.ckpt") device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model.to(device) sampler = LCMSampler(model) os.makedirs("results/test", exist_ok=True) vocoder = VocoderBigVGAN("./model/vocoder",device) generator = GenSamples(sampler,model,"results/test",vocoder,save_mel = False,save_wav = True, original_inference_steps=config.model.params.num_ddim_timesteps, ddim_steps=ddim_steps, scale=scale, num_samples=num_samples) csv_dicts = [] with torch.no_grad(): with model.ema_scope(): wav_name = f'{prompt["ori_caption"].strip().replace(" ", "-")}' generator.gen_test_sample(prompt,wav_name=wav_name) print(f"Your samples are ready and waiting four you here: \nresults/test \nEnjoy.") return "results/test/"+wav_name+"_0.wav" def my_inference_function(text_prompt, ddim_steps, num_samples, scale, seed): file_path = infer(text_prompt, ddim_steps, num_samples, scale, seed) return file_path with gr.Blocks() as demo: with gr.Row(): gr.Markdown("## AudioLCM：Text-to-Audio Generation with Latent Consistency Models") with gr.Row(): with gr.Column(): prompt = gr.Textbox(label="Prompt: Input your text here. ") run_button = gr.Button() with gr.Accordion("Advanced options", open=False): num_samples = gr.Slider( label="Select from audios num.This number control the number of candidates \ (e.g., generate three audios and choose the best to show you). A Larger value usually lead to \ better quality with heavier computation", minimum=1, maximum=10, value=1, step=1) ddim_steps = gr.Slider(label="ddim_steps", minimum=1, maximum=50, value=2, step=1) scale = gr.Slider( label="Guidance Scale:(Large => more relevant to text but the quality may drop)", minimum=0.1, maximum=8.0, value=5.0, step=0.1 ) seed = gr.Slider( label="Seed:Change this value (any integer number) will lead to a different generation result.", minimum=0, maximum=2147483647, step=1, value=44, ) with gr.Column(): outaudio = gr.Audio() run_button.click(fn=my_inference_function, inputs=[ prompt,ddim_steps, num_samples, scale, seed], outputs=[outaudio]) with gr.Row(): with gr.Column(): gr.Examples( examples = [['An engine revving and then tires squealing',2,1,5,55],['A group of people laughing followed by farting',2,1,5,55], ['Duck quacking repeatedly',2,1,5,88],['A man speaks as birds chirp and dogs bark',2,1,5,55],['Continuous snoring of a person',2,1,5,55]], inputs = [prompt,ddim_steps, num_samples, scale, seed], outputs = [outaudio] ) with gr.Column(): pass demo.launch() # gradio_interface = gradio.Interface( # fn = my_inference_function, # inputs = "text", # outputs = "audio" # ) # gradio_interface.launch()