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eval.py
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import shutil
import click
import warnings
import argparse
import torch
import os
import os.path as osp
import yaml
warnings.simplefilter('ignore')
# load packages
import random
from meldataset import build_dataloader
from modules.commons import *
from losses import *
from optimizers import build_optimizer
import time
from accelerate import Accelerator
from accelerate.utils import LoggerType
from accelerate import DistributedDataParallelKwargs
from torch.utils.tensorboard import SummaryWriter
import torchaudio
from torchmetrics.classification import MulticlassAccuracy
import logging
from accelerate.logging import get_logger
from speechtokenizer import SpeechTokenizer
from dac.nn.loss import MultiScaleSTFTLoss, MelSpectrogramLoss, GANLoss, L1Loss
from audiotools import AudioSignal
logger = get_logger(__name__, log_level="DEBUG")
# torch.autograd.set_detect_anomaly(True)
def main(args):
config_path = args.config_path
config = yaml.safe_load(open(config_path))
log_dir = config['log_dir'] + '/eval'
if not osp.exists(log_dir): os.makedirs(log_dir, exist_ok=True)
shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))
ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True, broadcast_buffers=False)
accelerator = Accelerator(project_dir=log_dir, split_batches=True, kwargs_handlers=[ddp_kwargs])
writer = SummaryWriter(log_dir + "/tensorboard")
# write logs
file_handler = logging.FileHandler(osp.join(log_dir, 'train.log'))
file_handler.setLevel(logging.DEBUG)
file_handler.setFormatter(logging.Formatter('%(levelname)s:%(asctime)s: %(message)s'))
logger.logger.addHandler(file_handler)
batch_size = config.get('batch_size', 10)
batch_length = config.get('batch_length', 120)
device = accelerator.device
epochs = config.get('epochs_1st', 200)
log_interval = config.get('log_interval', 10)
saving_epoch = config.get('save_freq', 2)
save_interval = config.get('save_interval', 1000)
data_params = config.get('data_params', None)
sr = config['preprocess_params'].get('sr', 16000)
train_path = data_params['train_data']
val_path = data_params['val_data']
root_path = data_params['root_path']
min_length = data_params['min_length']
max_frame_len = config.get('max_len', 80)
discriminator_iter_start = config['loss_params'].get('discriminator_iter_start', 0)
loss_params = config.get('loss_params', {})
val_dataloader = build_dataloader(val_path,
root_path,
min_length=min_length,
batch_size=batch_size,
batch_length=None,
num_workers=4,
rank=accelerator.local_process_index,
world_size=accelerator.num_processes,
dataset_config={},
device=device,
n_repeats=1,
return_dict=True)
with accelerator.main_process_first():
pitch_extractor = load_F0_models(config['F0_path']).to(device)
# load pretrained audio codec
w2v_config_path = './w2v_models/speechtokenizer_hubert_avg_config.json'
w2v_ckpt_path = './w2v_models/SpeechTokenizer.pt'
w2v_model = SpeechTokenizer.load_from_checkpoint(w2v_config_path, w2v_ckpt_path).to(device)
w2v_model.eval()
model_params = recursive_munch(config['model_params'])
model = build_model(model_params)
_ = [model[key].to(device) for key in model]
model, _, start_epoch, iters = load_checkpoint(model, None, config['pretrained_model'],
load_only_params=True, ignore_modules=[], is_distributed=accelerator.num_processes > 1)
_ = [model[key].eval() for key in model]
for i, batch in enumerate(val_dataloader):
waves = batch[0]
paths = batch[-1]
batch = [b.to(device, non_blocking=True) if type(b) == torch.Tensor else b for b in batch[1:-1]]
texts, input_lengths, mels, mel_input_length, speaker_labels, langs = batch
# note that the mel spec here must be sr=24000, n_fft=2048, hop_lenght=300, win_length=1200 to be compatible with f0 extractor
bsz = len(waves)
with torch.no_grad():
# put ground truth audio
writer.add_audio('full/gt_audio', waves[0], iters, sample_rate=16000)
# without timbre norm
z = model.encoder(torch.from_numpy(waves[0])[None, None, ...].to(device).float())
mel_prosody = mels[0:1, :20, :z.size(-1)]
z, quantized, commitment_loss, codebook_loss, timbre = model.quantizer(z, mel_prosody,
mels[0:1, :, :z.size(-1)],
mels[0:1, :, :z.size(-1)],
mel_input_length[0:1])
p_pred_wave = model.decoder(quantized[0])
c_pred_wave = model.decoder(quantized[1])
r_pred_wave = model.decoder(quantized[2])
pc_pred_wave = model.decoder(quantized[0] + quantized[1])
pr_pred_wave = model.decoder(quantized[0] + quantized[2])
pcr_pred_wave = model.decoder(quantized[0] + quantized[1] + quantized[2])
full_pred_wave = model.decoder(z)
writer.add_audio('partial/pred_audio_p', p_pred_wave[0], iters, sample_rate=16000)
writer.add_audio('partial/pred_audio_c', c_pred_wave[0], iters, sample_rate=16000)
writer.add_audio('partial/pred_audio_r', r_pred_wave[0], iters, sample_rate=16000)
writer.add_audio('partial/pred_audio_pc', pc_pred_wave[0], iters, sample_rate=16000)
writer.add_audio('partial/pred_audio_pr', pr_pred_wave[0], iters, sample_rate=16000)
writer.add_audio('partial/pred_audio_pcr', pcr_pred_wave[0], iters, sample_rate=16000)
writer.add_audio('full/pred_audio', full_pred_wave[0], iters, sample_rate=16000)
for bib in range(1, min(5, bsz)):
x = quantized[0] + quantized[1] + quantized[2]
z2 = model.encoder(torch.from_numpy(waves[bib])[None, None, ...].to(device).float())
mel_prosody2 = mels[1:2, :20, :z2.size(-1)]
z2, quantized2, commitment_loss2, codebook_loss2, timbre2 = model.quantizer(z2, mel_prosody2,
mels[bib:bib+1, :, :z2.size(-1)],
mels[bib:bib+1, :, :z2.size(-1)],
mel_input_length[bib:bib+1])
style2 = model.quantizer.timbre_linear(timbre2).unsqueeze(2) # (B, 2d, 1)
gamma, beta = style2.chunk(2, 1) # (B, d, 1)
x = x.transpose(1, 2)
x = model.quantizer.timbre_norm(x)
x = x.transpose(1, 2)
x = x * gamma + beta
vc_pred_wave = model.decoder(x)
writer.add_audio(f'vc_ref/audio_{bib}', waves[bib], iters, sample_rate=16000)
writer.add_audio(f'vc_pred/audio_{bib}', vc_pred_wave[0], iters, sample_rate=16000)
iters = iters + 1
if iters > 10:
exit()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--config_path', type=str, default='configs/config.yml')
args = parser.parse_args()
main(args)