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|
|
| from __future__ import absolute_import, division, print_function, unicode_literals |
|
|
| import glob |
| import os |
| import numpy as np |
| import argparse |
| import json |
| import torch |
| from scipy.io.wavfile import write |
| from env import AttrDict |
| from meldataset import MAX_WAV_VALUE |
| import librosa |
| from bigvgan import BigVGAN as Generator |
|
|
| h = None |
| device = None |
| torch.backends.cudnn.benchmark = False |
|
|
|
|
| def load_checkpoint(filepath, device): |
| assert os.path.isfile(filepath) |
| print(f"Loading '{filepath}'") |
| checkpoint_dict = torch.load(filepath, map_location=device) |
| print("Complete.") |
| return checkpoint_dict |
|
|
|
|
| def scan_checkpoint(cp_dir, prefix): |
| pattern = os.path.join(cp_dir, prefix + "*") |
| cp_list = glob.glob(pattern) |
| if len(cp_list) == 0: |
| return "" |
| return sorted(cp_list)[-1] |
|
|
|
|
| def inference(a, h): |
| generator = Generator(h, use_cuda_kernel=a.use_cuda_kernel).to(device) |
|
|
| state_dict_g = load_checkpoint(a.checkpoint_file, device) |
| generator.load_state_dict(state_dict_g["generator"]) |
|
|
| filelist = os.listdir(a.input_mels_dir) |
|
|
| os.makedirs(a.output_dir, exist_ok=True) |
|
|
| generator.eval() |
| generator.remove_weight_norm() |
| with torch.no_grad(): |
| for i, filname in enumerate(filelist): |
| x = np.load(os.path.join(a.input_mels_dir, filname), allow_pickle=True) |
|
|
| x = librosa.power_to_db(x) |
|
|
| x = torch.FloatTensor(x).to(device) |
| if len(x.shape) == 2: |
| x = x.unsqueeze(0) |
|
|
| y_g_hat = generator(x) |
|
|
| audio = y_g_hat.squeeze() |
| audio = audio * MAX_WAV_VALUE |
| audio = audio.cpu().numpy().astype("int16") |
|
|
| output_file = os.path.join( |
| a.output_dir, os.path.splitext(filname)[0] + ".wav" |
| ) |
| write(output_file, h.sampling_rate, audio) |
| print(output_file) |
|
|
|
|
| def main(): |
| print("Initializing Inference Process..") |
|
|
| parser = argparse.ArgumentParser() |
| parser.add_argument("--input_mels_dir", default="test_mel_files") |
| parser.add_argument("--output_dir", default="generated_files_from_mel") |
| parser.add_argument("--checkpoint_file", required=True) |
| parser.add_argument("--use_cuda_kernel", action="store_true", default=False) |
|
|
| a = parser.parse_args() |
|
|
| config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json") |
| with open(config_file) as f: |
| data = f.read() |
|
|
| global h |
| json_config = json.loads(data) |
| h = AttrDict(json_config) |
|
|
| torch.manual_seed(h.seed) |
| global device |
| if torch.cuda.is_available(): |
| torch.cuda.manual_seed(h.seed) |
| device = torch.device("cuda") |
| else: |
| device = torch.device("cpu") |
|
|
| inference(a, h) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
