remove hardcode and unused part

hifigan/config_16k_.json

@@ -27,11 +27,5 @@
     "fmax": 7600,
     "fmax_for_loss": null,
 
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
+    "num_workers": 4
 }

hifigan/inference.py

@@ -71,28 +71,3 @@ def infer_waveform(mel, progress_callback=None):
 
     return audio
 
-
-
-# if __name__ == "__main__":
-
-#     mel = np.load("./mel-T0055G0184S0349.wav_00.npy")
-#     # mel = torch.FloatTensor(mel.T).to(device)
-#     # mel = mel.unsqueeze(0)
-    
-#     load_model("../../../TTS/Vocoder/outputs/hifi-gan/models/g_00930000")
-#     audio = infer_waveform(mel)
-
-#     sf.write("b.wav", audio, samplerate=16000)
-
-
-    # with torch.no_grad():
-    #     y_g_hat = generator(mel)
-    #     audio = y_g_hat.squeeze()
-
-
-    # audio = audio.cpu().numpy()
-    # sf.write("a.wav", audio, samplerate=16000)
-
-
-# import IPython.display as ipd
-# ipd.Audio(audio, rate=16000)

hifigan/test.py

@@ -1,58 +0,0 @@
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import glob
-import os
-import argparse
-import json
-import torch
-import numpy as np
-from scipy.io.wavfile import write
-from env import AttrDict
-from meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
-from models import Generator
-import soundfile as sf
-
-
-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
-
-
-h = None
-device = None
-
-
-with open("config_16k_.json") as f:
-    data = f.read()
-json_config = json.loads(data)
-h = AttrDict(json_config)
-torch.manual_seed(h.seed)
-device = torch.device("cpu")
-
-
-generator = Generator(h).to(device)
-state_dict_g = load_checkpoint("../../../TTS/Vocoder/outputs/hifi-gan/models/g_00930000", device)
-generator.load_state_dict(state_dict_g['generator'])
-generator.eval()
-generator.remove_weight_norm()
-
-
-mel = np.load("./mel-T0055G0184S0349.wav_00.npy")
-mel = torch.FloatTensor(mel.T).to(device)
-mel = mel.unsqueeze(0)
-
-
-with torch.no_grad():
-    y_g_hat = generator(mel)
-    audio = y_g_hat.squeeze()
-
-
-audio = audio.cpu().numpy()
-sf.write("a.wav", audio, samplerate=16000)
-
-
-# import IPython.display as ipd
-# ipd.Audio(audio, rate=16000)