Introduce code from Rust monorepo. This is a mess

tts/hifigan/inference_e2e.py

@@ -0,0 +1,93 @@
+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
+from models import Generator
+
+h = None
+device = None
+
+
+def load_checkpoint(filepath, device):
+    assert os.path.isfile(filepath)
+    print("Loading '{}'".format(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):
+    generator = Generator(h).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):
+            if ".npy" not in filname:
+                continue
+            x = np.load(os.path.join(a.input_mels_dir, filname))
+            x = torch.FloatTensor(x).to(device)
+            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)
+    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)
+
+
+if __name__ == "__main__":
+    main()