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remove hardcode and unused part

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This commit is contained in:
peijiyang 2021-09-08 11:17:20 +08:00
parent c4a8c72b83
commit b60b75ea89
3 changed files with 1 additions and 90 deletions
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8
hifigan/config_16k_.json
View File

@ -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
}

25
hifigan/inference.py
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@ -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)

58
hifigan/test.py
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@ -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)