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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import time
import numpy as np
import torch
from tqdm import tqdm
import torch.nn as nn
from collections import OrderedDict
import json
from models.tta.autoencoder.autoencoder import AutoencoderKL
from models.tta.ldm.inference_utils.vocoder import Generator
from models.tta.ldm.audioldm import AudioLDM
from transformers import T5EncoderModel, AutoTokenizer
from diffusers import PNDMScheduler
import matplotlib.pyplot as plt
from scipy.io.wavfile import write
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
class AudioLDMInference:
def __init__(self, args, cfg):
self.cfg = cfg
self.args = args
self.build_autoencoderkl()
self.build_textencoder()
self.model = self.build_model()
self.load_state_dict()
self.build_vocoder()
self.out_path = self.args.output_dir
self.out_mel_path = os.path.join(self.out_path, "mel")
self.out_wav_path = os.path.join(self.out_path, "wav")
os.makedirs(self.out_mel_path, exist_ok=True)
os.makedirs(self.out_wav_path, exist_ok=True)
def build_autoencoderkl(self):
self.autoencoderkl = AutoencoderKL(self.cfg.model.autoencoderkl)
self.autoencoder_path = self.cfg.model.autoencoder_path
checkpoint = torch.load(self.autoencoder_path, map_location="cpu")
self.autoencoderkl.load_state_dict(checkpoint["model"])
self.autoencoderkl.cuda(self.args.local_rank)
self.autoencoderkl.requires_grad_(requires_grad=False)
self.autoencoderkl.eval()
def build_textencoder(self):
self.tokenizer = AutoTokenizer.from_pretrained("t5-base", model_max_length=512)
self.text_encoder = T5EncoderModel.from_pretrained("t5-base")
self.text_encoder.cuda(self.args.local_rank)
self.text_encoder.requires_grad_(requires_grad=False)
self.text_encoder.eval()
def build_vocoder(self):
config_file = os.path.join(self.args.vocoder_config_path)
with open(config_file) as f:
data = f.read()
json_config = json.loads(data)
h = AttrDict(json_config)
self.vocoder = Generator(h).to(self.args.local_rank)
checkpoint_dict = torch.load(
self.args.vocoder_path, map_location=self.args.local_rank
)
self.vocoder.load_state_dict(checkpoint_dict["generator"])
def build_model(self):
self.model = AudioLDM(self.cfg.model.audioldm)
return self.model
def load_state_dict(self):
self.checkpoint_path = self.args.checkpoint_path
checkpoint = torch.load(self.checkpoint_path, map_location="cpu")
self.model.load_state_dict(checkpoint["model"])
self.model.cuda(self.args.local_rank)
def get_text_embedding(self):
text = self.args.text
prompt = [text]
text_input = self.tokenizer(
prompt,
max_length=self.tokenizer.model_max_length,
truncation=True,
padding="do_not_pad",
return_tensors="pt",
)
text_embeddings = self.text_encoder(
text_input.input_ids.to(self.args.local_rank)
)[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = self.tokenizer(
[""] * 1, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(self.args.local_rank)
)[0]
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def inference(self):
text_embeddings = self.get_text_embedding()
print(text_embeddings.shape)
num_steps = self.args.num_steps
guidance_scale = self.args.guidance_scale
noise_scheduler = PNDMScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
skip_prk_steps=True,
set_alpha_to_one=False,
steps_offset=1,
prediction_type="epsilon",
)
noise_scheduler.set_timesteps(num_steps)
latents = torch.randn(
(
1,
self.cfg.model.autoencoderkl.z_channels,
80 // (2 ** (len(self.cfg.model.autoencoderkl.ch_mult) - 1)),
624 // (2 ** (len(self.cfg.model.autoencoderkl.ch_mult) - 1)),
)
).to(self.args.local_rank)
self.model.eval()
for t in tqdm(noise_scheduler.timesteps):
t = t.to(self.args.local_rank)
# expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
latent_model_input = torch.cat([latents] * 2)
latent_model_input = noise_scheduler.scale_model_input(
latent_model_input, timestep=t
)
# print(latent_model_input.shape)
# predict the noise residual
with torch.no_grad():
noise_pred = self.model(
latent_model_input, torch.cat([t.unsqueeze(0)] * 2), text_embeddings
)
# perform guidance
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (
noise_pred_text - noise_pred_uncond
)
# compute the previous noisy sample x_t -> x_t-1
latents = noise_scheduler.step(noise_pred, t, latents).prev_sample
# print(latents.shape)
latents_out = latents
print(latents_out.shape)
with torch.no_grad():
mel_out = self.autoencoderkl.decode(latents_out)
print(mel_out.shape)
melspec = mel_out[0, 0].cpu().detach().numpy()
plt.imsave(os.path.join(self.out_mel_path, self.args.text + ".png"), melspec)
self.vocoder.eval()
self.vocoder.remove_weight_norm()
with torch.no_grad():
melspec = np.expand_dims(melspec, 0)
melspec = torch.FloatTensor(melspec).to(self.args.local_rank)
y = self.vocoder(melspec)
audio = y.squeeze()
audio = audio * 32768.0
audio = audio.cpu().numpy().astype("int16")
write(os.path.join(self.out_wav_path, self.args.text + ".wav"), 16000, audio)
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