mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-17 00:23:14 +00:00
Brandon Thomas
94 lines
2.5 KiB
Python
94 lines
2.5 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import glob
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import os
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import numpy as np
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import argparse
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import json
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import torch
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from scipy.io.wavfile import write
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from env import AttrDict
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from meldataset import MAX_WAV_VALUE
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from models import Generator
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h = None
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device = None
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def load_checkpoint(filepath, device):
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assert os.path.isfile(filepath)
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print("Loading '{}'".format(filepath))
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checkpoint_dict = torch.load(filepath, map_location=device)
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print("Complete.")
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return checkpoint_dict
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def scan_checkpoint(cp_dir, prefix):
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pattern = os.path.join(cp_dir, prefix + "*")
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cp_list = glob.glob(pattern)
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if len(cp_list) == 0:
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return ""
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return sorted(cp_list)[-1]
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def inference(a):
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generator = Generator(h).to(device)
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state_dict_g = load_checkpoint(a.checkpoint_file, device)
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generator.load_state_dict(state_dict_g["generator"])
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filelist = os.listdir(a.input_mels_dir)
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os.makedirs(a.output_dir, exist_ok=True)
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generator.eval()
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generator.remove_weight_norm()
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with torch.no_grad():
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for i, filname in enumerate(filelist):
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if ".npy" not in filname:
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continue
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x = np.load(os.path.join(a.input_mels_dir, filname))
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x = torch.FloatTensor(x).to(device)
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y_g_hat = generator(x)
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audio = y_g_hat.squeeze()
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audio = audio * MAX_WAV_VALUE
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audio = audio.cpu().numpy().astype("int16")
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output_file = os.path.join(
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a.output_dir, os.path.splitext(filname)[0] + ".wav"
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)
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write(output_file, h.sampling_rate, audio)
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print(output_file)
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def main():
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print("Initializing Inference Process..")
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parser = argparse.ArgumentParser()
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parser.add_argument("--input_mels_dir", default="test_mel_files")
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parser.add_argument("--output_dir", default="generated_files_from_mel")
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parser.add_argument("--checkpoint_file", required=True)
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a = parser.parse_args()
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config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json")
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with open(config_file) as f:
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data = f.read()
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global h
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json_config = json.loads(data)
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h = AttrDict(json_config)
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torch.manual_seed(h.seed)
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global device
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if torch.cuda.is_available():
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torch.cuda.manual_seed(h.seed)
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device = torch.device("cuda")
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else:
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device = torch.device("cpu")
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inference(a)
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if __name__ == "__main__":
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main()
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