# 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 argparse
import os
import re
import time
from pathlib import Path
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from models.vocoders.vocoder_inference import synthesis
from torch.utils.data import DataLoader
from utils.util import set_all_random_seed
from utils.util import load_config
def parse_vocoder(vocoder_dir):
r"""Parse vocoder config"""
vocoder_dir = os.path.abspath(vocoder_dir)
ckpt_list = [ckpt for ckpt in Path(vocoder_dir).glob("*.pt")]
ckpt_list.sort(key=lambda x: int(x.stem), reverse=True)
ckpt_path = str(ckpt_list[0])
vocoder_cfg = load_config(os.path.join(vocoder_dir, "args.json"), lowercase=True)
vocoder_cfg.model.bigvgan = vocoder_cfg.vocoder
return vocoder_cfg, ckpt_path
class BaseInference(object):
def __init__(self, cfg, args):
self.cfg = cfg
self.args = args
self.model_type = cfg.model_type
self.avg_rtf = list()
set_all_random_seed(10086)
os.makedirs(args.output_dir, exist_ok=True)
if torch.cuda.is_available():
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
torch.set_num_threads(10) # inference on 1 core cpu.
# Load acoustic model
self.model = self.create_model().to(self.device)
state_dict = self.load_state_dict()
self.load_model(state_dict)
self.model.eval()
# Load vocoder model if necessary
if self.args.checkpoint_dir_vocoder is not None:
self.get_vocoder_info()
def create_model(self):
raise NotImplementedError
def load_state_dict(self):
self.checkpoint_file = self.args.checkpoint_file
if self.checkpoint_file is None:
assert self.args.checkpoint_dir is not None
checkpoint_path = os.path.join(self.args.checkpoint_dir, "checkpoint")
checkpoint_filename = open(checkpoint_path).readlines()[-1].strip()
self.checkpoint_file = os.path.join(
self.args.checkpoint_dir, checkpoint_filename
)
self.checkpoint_dir = os.path.split(self.checkpoint_file)[0]
print("Restore acoustic model from {}".format(self.checkpoint_file))
raw_state_dict = torch.load(self.checkpoint_file, map_location=self.device)
self.am_restore_step = re.findall(r"step-(.+?)_loss", self.checkpoint_file)[0]
return raw_state_dict
def load_model(self, model):
raise NotImplementedError
def get_vocoder_info(self):
self.checkpoint_dir_vocoder = self.args.checkpoint_dir_vocoder
self.vocoder_cfg = os.path.join(
os.path.dirname(self.checkpoint_dir_vocoder), "args.json"
)
self.cfg.vocoder = load_config(self.vocoder_cfg, lowercase=True)
self.vocoder_tag = self.checkpoint_dir_vocoder.split("/")[-2].split(":")[-1]
self.vocoder_steps = self.checkpoint_dir_vocoder.split("/")[-1].split(".")[0]
def build_test_utt_data(self):
raise NotImplementedError
def build_testdata_loader(self, args, target_speaker=None):
datasets, collate = self.build_test_dataset()
self.test_dataset = datasets(self.cfg, args, target_speaker)
self.test_collate = collate(self.cfg)
self.test_batch_size = min(
self.cfg.train.batch_size, len(self.test_dataset.metadata)
)
test_loader = DataLoader(
self.test_dataset,
collate_fn=self.test_collate,
num_workers=self.args.num_workers,
batch_size=self.test_batch_size,
shuffle=False,
)
return test_loader
def inference_each_batch(self, batch_data):
raise NotImplementedError
def inference_for_batches(self, args, target_speaker=None):
###### Construct test_batch ######
loader = self.build_testdata_loader(args, target_speaker)
n_batch = len(loader)
now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
print(
"Model eval time: {}, batch_size = {}, n_batch = {}".format(
now, self.test_batch_size, n_batch
)
)
self.model.eval()
###### Inference for each batch ######
pred_res = []
with torch.no_grad():
for i, batch_data in enumerate(loader if n_batch == 1 else tqdm(loader)):
# Put the data to device
for k, v in batch_data.items():
batch_data[k] = batch_data[k].to(self.device)
y_pred, stats = self.inference_each_batch(batch_data)
pred_res += y_pred
return pred_res
def inference(self, feature):
raise NotImplementedError
def synthesis_by_vocoder(self, pred):
audios_pred = synthesis(
self.vocoder_cfg,
self.checkpoint_dir_vocoder,
len(pred),
pred,
)
return audios_pred
def __call__(self, utt):
feature = self.build_test_utt_data(utt)
start_time = time.time()
with torch.no_grad():
outputs = self.inference(feature)[0]
time_used = time.time() - start_time
rtf = time_used / (
outputs.shape[1]
* self.cfg.preprocess.hop_size
/ self.cfg.preprocess.sample_rate
)
print("Time used: {:.3f}, RTF: {:.4f}".format(time_used, rtf))
self.avg_rtf.append(rtf)
audios = outputs.cpu().squeeze().numpy().reshape(-1, 1)
return audios
def base_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--config", default="config.json", help="json files for configurations."
)
parser.add_argument("--use_ddp_inference", default=False)
parser.add_argument("--n_workers", default=1, type=int)
parser.add_argument("--local_rank", default=-1, type=int)
parser.add_argument(
"--batch_size", default=1, type=int, help="Batch size for inference"
)
parser.add_argument(
"--num_workers",
default=1,
type=int,
help="Worker number for inference dataloader",
)
parser.add_argument(
"--checkpoint_dir",
type=str,
default=None,
help="Checkpoint dir including model file and configuration",
)
parser.add_argument(
"--checkpoint_file", help="checkpoint file", type=str, default=None
)
parser.add_argument(
"--test_list", help="test utterance list for testing", type=str, default=None
)
parser.add_argument(
"--checkpoint_dir_vocoder",
help="Vocoder's checkpoint dir including model file and configuration",
type=str,
default=None,
)
parser.add_argument(
"--output_dir",
type=str,
default=None,
help="Output dir for saving generated results",
)
return parser
if __name__ == "__main__":
parser = base_parser()
args = parser.parse_args()
cfg = load_config(args.config)
# Build inference
inference = BaseInference(cfg, args)
inference()