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import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
import os
import argparse
import re
from fastspeech2 import FastSpeech2
from loss import FastSpeech2Loss
from dataset import Dataset
from text import text_to_sequence, sequence_to_text
import hparams as hp
import utils
import audio as Audio
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def get_FastSpeech2(num):
checkpoint_path = os.path.join(hp.checkpoint_path, "checkpoint_{}.pth.tar".format(num))
model = nn.DataParallel(FastSpeech2())
model.load_state_dict(torch.load(checkpoint_path)['model'])
model.requires_grad = False
model.eval()
return model
def evaluate(model, step, vocoder=None):
model.eval()
torch.manual_seed(0)
mean_mel, std_mel = torch.tensor(np.load(os.path.join(hp.preprocessed_path, "mel_stat.npy")), dtype=torch.float).to(device)
mean_f0, std_f0 = torch.tensor(np.load(os.path.join(hp.preprocessed_path, "f0_stat.npy")), dtype=torch.float).to(device)
mean_energy, std_energy = torch.tensor(np.load(os.path.join(hp.preprocessed_path, "energy_stat.npy")), dtype=torch.float).to(device)
eval_path = hp.eval_path
if not os.path.exists(eval_path):
os.makedirs(eval_path)
# Get dataset
dataset = Dataset("val.txt", sort=False)
loader = DataLoader(dataset, batch_size=hp.batch_size**2, shuffle=False, collate_fn=dataset.collate_fn, drop_last=False, num_workers=0, )
# Get loss function
Loss = FastSpeech2Loss().to(device)
# Evaluation
d_l = []
f_l = []
e_l = []
mel_l = []
mel_p_l = []
current_step = 0
idx = 0
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
# Get Data
id_ = data_of_batch["id"]
text = torch.from_numpy(data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).int().to(device)
log_D = torch.from_numpy(data_of_batch["log_D"]).int().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(data_of_batch["energy"]).float().to(device)
src_len = torch.from_numpy(data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
with torch.no_grad():
# Forward
mel_output, mel_postnet_output, log_duration_output, f0_output, energy_output, src_mask, mel_mask, out_mel_len = model(
text, src_len, mel_len, D, f0, energy, max_src_len, max_mel_len)
# Cal Loss
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
log_duration_output, log_D, f0_output, f0, energy_output, energy, mel_output, mel_postnet_output, mel_target, ~src_mask, ~mel_mask)
d_l.append(d_loss.item())
f_l.append(f_loss.item())
e_l.append(e_loss.item())
mel_l.append(mel_loss.item())
mel_p_l.append(mel_postnet_loss.item())
if idx == 0 and vocoder is not None:
# Run vocoding and plotting spectrogram only when the vocoder is defined
for k in range(1):
basename = id_[k]
gt_length = mel_len[k]
out_length = out_mel_len[k]
mel_target_torch = mel_target[k:k+1, :gt_length]
mel_target_ = mel_target[k, :gt_length]
mel_postnet_torch = mel_postnet_output[k:k+1, :out_length]
mel_postnet = mel_postnet_output[k, :out_length]
mel_target_torch = utils.de_norm(mel_target_torch, mean_mel, std_mel).transpose(1, 2).detach()
mel_target_ = utils.de_norm(mel_target_, mean_mel, std_mel).cpu().transpose(0, 1).detach()
mel_postnet_torch = utils.de_norm(mel_postnet_torch, mean_mel, std_mel).transpose(1, 2).detach()
mel_postnet = utils.de_norm(mel_postnet, mean_mel, std_mel).cpu().transpose(0, 1).detach()
if hp.vocoder == "vocgan":
utils.vocgan_infer(mel_target_torch, vocoder, path=os.path.join(hp.eval_path, 'eval_groundtruth_{}_{}.wav'.format(basename, hp.vocoder)))
utils.vocgan_infer(mel_postnet_torch, vocoder, path=os.path.join(hp.eval_path, 'eval_step_{}_{}_{}.wav'.format(step, basename, hp.vocoder)))
np.save(os.path.join(hp.eval_path, 'eval_step_{}_{}_mel.npy'.format(step, basename)), mel_postnet.numpy())
f0_ = f0[k, :gt_length]
energy_ = energy[k, :gt_length]
f0_output_ = f0_output[k, :out_length]
energy_output_ = energy_output[k, :out_length]
f0_ = utils.de_norm(f0_, mean_f0, std_f0).detach().cpu().numpy()
f0_output_ = utils.de_norm(f0_output, mean_f0, std_f0).detach().cpu().numpy()
energy_ = utils.de_norm(energy_, mean_energy, std_energy).detach().cpu().numpy()
energy_output_ = utils.de_norm(energy_output_, mean_energy, std_energy).detach().cpu().numpy()
utils.plot_data([(mel_postnet.numpy(), f0_output_, energy_output_), (mel_target_.numpy(), f0_, energy_)],
['Synthesized Spectrogram', 'Ground-Truth Spectrogram'], filename=os.path.join(hp.eval_path, 'eval_step_{}_{}.png'.format(step, basename)))
idx += 1
print("done")
current_step += 1
d_l = sum(d_l) / len(d_l)
f_l = sum(f_l) / len(f_l)
e_l = sum(e_l) / len(e_l)
mel_l = sum(mel_l) / len(mel_l)
mel_p_l = sum(mel_p_l) / len(mel_p_l)
str1 = "FastSpeech2 Step {},".format(step)
str2 = "Duration Loss: {}".format(d_l)
str3 = "F0 Loss: {}".format(f_l)
str4 = "Energy Loss: {}".format(e_l)
str5 = "Mel Loss: {}".format(mel_l)
str6 = "Mel Postnet Loss: {}".format(mel_p_l)
print("\n" + str1)
print(str2)
print(str3)
print(str4)
print(str5)
print(str6)
with open(os.path.join(hp.log_path, "eval.txt"), "a") as f_log:
f_log.write(str1 + "\n")
f_log.write(str2 + "\n")
f_log.write(str3 + "\n")
f_log.write(str4 + "\n")
f_log.write(str5 + "\n")
f_log.write(str6 + "\n")
f_log.write("\n")
model.train()
return d_l, f_l, e_l, mel_l, mel_p_l
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--step', type=int, default=30000)
args = parser.parse_args()
# Get model
model = get_FastSpeech2(args.step).to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of FastSpeech2 Parameters:', num_param)
# Load vocoder
if hp.vocoder == 'vocgan':
vocoder = utils.get_vocgan(ckpt_path=hp.vocoder_pretrained_model_path)
vocoder.to(device)
# Init directories
if not os.path.exists(hp.log_path):
os.makedirs(hp.log_path)
if not os.path.exists(hp.eval_path):
os.makedirs(hp.eval_path)
evaluate(model, args.step, vocoder)
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