MockingBird/hifigan/inference.py
2021-09-08 11:17:20 +08:00

74 lines
1.7 KiB
Python

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 hifigan.env import AttrDict
from hifigan.meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
from hifigan.models import Generator
import soundfile as sf
generator = None # type: Generator
_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 load_model(weights_fpath, verbose=True):
global generator, _device
if verbose:
print("Building hifigan")
with open("./hifigan/config_16k_.json") as f:
data = f.read()
json_config = json.loads(data)
h = AttrDict(json_config)
torch.manual_seed(h.seed)
if torch.cuda.is_available():
# _model = _model.cuda()
_device = torch.device('cuda')
else:
_device = torch.device('cpu')
generator = Generator(h).to(_device)
state_dict_g = load_checkpoint(
weights_fpath, _device
)
generator.load_state_dict(state_dict_g['generator'])
generator.eval()
generator.remove_weight_norm()
def is_loaded():
return generator is not None
def infer_waveform(mel, progress_callback=None):
if generator is None:
raise Exception("Please load hifi-gan in memory before using it")
mel = torch.FloatTensor(mel).to(_device)
mel = mel.unsqueeze(0)
with torch.no_grad():
y_g_hat = generator(mel)
audio = y_g_hat.squeeze()
audio = audio.cpu().numpy()
return audio