# 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()