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()