import os
import random
import pandas as pd
import torch
import librosa
import numpy as np
import soundfile as sf
from tqdm import tqdm
from .utils import scale_shift_re
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
"""
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
"""
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
# rescale the results from guidance (fixes overexposure)
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
return noise_cfg
@torch.no_grad()
def inference(autoencoder, unet, gt, gt_mask,
tokenizer, text_encoder,
params, noise_scheduler,
text_raw, neg_text=None,
audio_frames=500,
guidance_scale=3, guidance_rescale=0.0,
ddim_steps=50, eta=1, random_seed=2024,
device='cuda',
):
if neg_text is None:
neg_text = [""]
if tokenizer is not None:
text_batch = tokenizer(text_raw,
max_length=params['text_encoder']['max_length'],
padding="max_length", truncation=True, return_tensors="pt")
text, text_mask = text_batch.input_ids.to(device), text_batch.attention_mask.to(device).bool()
text = text_encoder(input_ids=text, attention_mask=text_mask).last_hidden_state
uncond_text_batch = tokenizer(neg_text,
max_length=params['text_encoder']['max_length'],
padding="max_length", truncation=True, return_tensors="pt")
uncond_text, uncond_text_mask = uncond_text_batch.input_ids.to(device), uncond_text_batch.attention_mask.to(device).bool()
uncond_text = text_encoder(input_ids=uncond_text,
attention_mask=uncond_text_mask).last_hidden_state
else:
text, text_mask = None, None
guidance_scale = None
codec_dim = params['model']['out_chans']
unet.eval()
if random_seed is not None:
generator = torch.Generator(device=device).manual_seed(random_seed)
else:
generator = torch.Generator(device=device)
generator.seed()
noise_scheduler.set_timesteps(ddim_steps)
# init noise
noise = torch.randn((1, codec_dim, audio_frames), generator=generator, device=device)
latents = noise
for t in noise_scheduler.timesteps:
latents = noise_scheduler.scale_model_input(latents, t)
if guidance_scale:
latents_combined = torch.cat([latents, latents], dim=0)
text_combined = torch.cat([text, uncond_text], dim=0)
text_mask_combined = torch.cat([text_mask, uncond_text_mask], dim=0)
if gt is not None:
gt_combined = torch.cat([gt, gt], dim=0)
gt_mask_combined = torch.cat([gt_mask, gt_mask], dim=0)
else:
gt_combined = None
gt_mask_combined = None
output_combined, _ = unet(latents_combined, t, text_combined, context_mask=text_mask_combined,
cls_token=None, gt=gt_combined, mae_mask_infer=gt_mask_combined)
output_text, output_uncond = torch.chunk(output_combined, 2, dim=0)
output_pred = output_uncond + guidance_scale * (output_text - output_uncond)
if guidance_rescale > 0.0:
output_pred = rescale_noise_cfg(output_pred, output_text,
guidance_rescale=guidance_rescale)
else:
output_pred, mae_mask = unet(latents, t, text, context_mask=text_mask,
cls_token=None, gt=gt, mae_mask_infer=gt_mask)
latents = noise_scheduler.step(model_output=output_pred, timestep=t,
sample=latents,
eta=eta, generator=generator).prev_sample
pred = scale_shift_re(latents, params['autoencoder']['scale'],
params['autoencoder']['shift'])
if gt is not None:
pred[~gt_mask] = gt[~gt_mask]
pred_wav = autoencoder(embedding=pred)
return pred_wav
@torch.no_grad()
def eval_udit(autoencoder, unet,
tokenizer, text_encoder,
params, noise_scheduler,
val_df, subset,
audio_frames, mae=False,
guidance_scale=3, guidance_rescale=0.0,
ddim_steps=50, eta=1, random_seed=2023,
device='cuda',
epoch=0, save_path='logs/eval/', val_num=5):
val_df = pd.read_csv(val_df)
val_df = val_df[val_df['split'] == subset]
if mae:
val_df = val_df[val_df['audio_length'] != 0]
save_path = save_path + str(epoch) + '/'
os.makedirs(save_path, exist_ok=True)
for i in tqdm(range(len(val_df))):
row = val_df.iloc[i]
text = [row['caption']]
if mae:
audio_path = params['data']['val_dir'] + str(row['audio_path'])
gt, sr = librosa.load(audio_path, sr=params['data']['sr'])
gt = gt / (np.max(np.abs(gt)) + 1e-9)
sf.write(save_path + text[0] + '_gt.wav', gt, samplerate=params['data']['sr'])
num_samples = 10 * sr
if len(gt) < num_samples:
padding = num_samples - len(gt)
gt = np.pad(gt, (0, padding), 'constant')
else:
gt = gt[:num_samples]
gt = torch.tensor(gt).unsqueeze(0).unsqueeze(1).to(device)
gt = autoencoder(audio=gt)
B, D, L = gt.shape
mask_len = int(L * 0.2)
gt_mask = torch.zeros(B, D, L).to(device)
for _ in range(2):
start = random.randint(0, L - mask_len)
gt_mask[:, :, start:start + mask_len] = 1
gt_mask = gt_mask.bool()
else:
gt = None
gt_mask = None
pred = inference(autoencoder, unet, gt, gt_mask,
tokenizer, text_encoder,
params, noise_scheduler,
text, neg_text=None,
audio_frames=audio_frames,
guidance_scale=guidance_scale, guidance_rescale=guidance_rescale,
ddim_steps=ddim_steps, eta=eta, random_seed=random_seed,
device=device)
pred = pred.cpu().numpy().squeeze(0).squeeze(0)
sf.write(save_path + text[0] + '.wav', pred, samplerate=params['data']['sr'])
if i + 1 >= val_num:
break