mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
|
|
|
|
import os
|
|
import json
|
|
import torch
|
|
from utils.util import AttrDict
|
|
from vocoder.fregan.generator import FreGAN
|
|
|
|
generator = None # type: FreGAN
|
|
output_sample_rate = 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 load_model(weights_fpath, config_fpath=None, verbose=True):
|
|
global generator, _device, output_sample_rate
|
|
|
|
if verbose:
|
|
print("Building fregan")
|
|
|
|
if config_fpath == None:
|
|
model_config_fpaths = list(weights_fpath.parent.rglob("*.json"))
|
|
if len(model_config_fpaths) > 0:
|
|
config_fpath = model_config_fpaths[0]
|
|
else:
|
|
config_fpath = "./vocoder/fregan/config.json"
|
|
with open(config_fpath) as f:
|
|
data = f.read()
|
|
json_config = json.loads(data)
|
|
h = AttrDict(json_config)
|
|
output_sample_rate = h.sampling_rate
|
|
torch.manual_seed(h.seed)
|
|
|
|
if torch.cuda.is_available():
|
|
# _model = _model.cuda()
|
|
_device = torch.device('cuda')
|
|
else:
|
|
_device = torch.device('cpu')
|
|
|
|
generator = FreGAN(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 fre-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, output_sample_rate
|
|
|