wehub-resource-sync
121 lines
5.3 KiB
Python
121 lines
5.3 KiB
Python
import pathlib
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import yaml
|
|
from librosa.filters import mel as librosa_mel_fn
|
|
|
|
from basics.base_vocoder import BaseVocoder
|
|
from modules.vocoders.registry import register_vocoder
|
|
from utils.hparams import hparams
|
|
|
|
|
|
class DotDict(dict):
|
|
def __getattr__(*args):
|
|
val = dict.get(*args)
|
|
return DotDict(val) if type(val) is dict else val
|
|
|
|
__setattr__ = dict.__setitem__
|
|
__delattr__ = dict.__delitem__
|
|
|
|
|
|
def load_model(model_path: pathlib.Path, device='cpu'):
|
|
config_file = model_path.with_name('config.yaml')
|
|
with open(config_file, "r") as config:
|
|
args = yaml.safe_load(config)
|
|
args = DotDict(args)
|
|
|
|
# load model
|
|
print(' [Loading] ' + str(model_path))
|
|
model = torch.jit.load(model_path, map_location=torch.device(device))
|
|
model.eval()
|
|
|
|
return model, args
|
|
|
|
|
|
@register_vocoder
|
|
class DDSP(BaseVocoder):
|
|
def __init__(self, device='cpu'):
|
|
self.device = device
|
|
model_path = pathlib.Path(hparams['vocoder_ckpt'])
|
|
assert model_path.exists(), 'DDSP model file is not found!'
|
|
self.model, self.args = load_model(model_path, device=self.device)
|
|
|
|
def to_device(self, device):
|
|
pass
|
|
|
|
def get_device(self):
|
|
return self.device
|
|
|
|
def spec2wav_torch(self, mel, f0): # mel: [B, T, bins] f0: [B, T]
|
|
if self.args.data.sampling_rate != hparams['audio_sample_rate']:
|
|
print('Mismatch parameters: hparams[\'audio_sample_rate\']=', hparams['audio_sample_rate'], '!=',
|
|
self.args.data.sampling_rate, '(vocoder)')
|
|
if self.args.data.n_mels != hparams['audio_num_mel_bins']:
|
|
print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=', hparams['audio_num_mel_bins'], '!=',
|
|
self.args.data.n_mels, '(vocoder)')
|
|
if self.args.data.n_fft != hparams['fft_size']:
|
|
print('Mismatch parameters: hparams[\'fft_size\']=', hparams['fft_size'], '!=', self.args.data.n_fft,
|
|
'(vocoder)')
|
|
if self.args.data.win_length != hparams['win_size']:
|
|
print('Mismatch parameters: hparams[\'win_size\']=', hparams['win_size'], '!=', self.args.data.win_length,
|
|
'(vocoder)')
|
|
if self.args.data.block_size != hparams['hop_size']:
|
|
print('Mismatch parameters: hparams[\'hop_size\']=', hparams['hop_size'], '!=', self.args.data.block_size,
|
|
'(vocoder)')
|
|
if self.args.data.mel_fmin != hparams['fmin']:
|
|
print('Mismatch parameters: hparams[\'fmin\']=', hparams['fmin'], '!=', self.args.data.mel_fmin,
|
|
'(vocoder)')
|
|
if self.args.data.mel_fmax != hparams['fmax']:
|
|
print('Mismatch parameters: hparams[\'fmax\']=', hparams['fmax'], '!=', self.args.data.mel_fmax,
|
|
'(vocoder)')
|
|
with torch.no_grad():
|
|
mel = mel.to(self.device)
|
|
mel_base = hparams.get('mel_base', 10)
|
|
if mel_base != 'e':
|
|
assert mel_base in [10, '10'], "mel_base must be 'e', '10' or 10."
|
|
else:
|
|
# log mel to log10 mel
|
|
mel = 0.434294 * mel
|
|
f0 = f0.unsqueeze(-1).to(self.device)
|
|
signal, _, (s_h, s_n) = self.model(mel, f0)
|
|
signal = signal.view(-1)
|
|
return signal
|
|
|
|
def spec2wav(self, mel, f0):
|
|
if self.args.data.sampling_rate != hparams['audio_sample_rate']:
|
|
print('Mismatch parameters: hparams[\'audio_sample_rate\']=', hparams['audio_sample_rate'], '!=',
|
|
self.args.data.sampling_rate, '(vocoder)')
|
|
if self.args.data.n_mels != hparams['audio_num_mel_bins']:
|
|
print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=', hparams['audio_num_mel_bins'], '!=',
|
|
self.args.data.n_mels, '(vocoder)')
|
|
if self.args.data.n_fft != hparams['fft_size']:
|
|
print('Mismatch parameters: hparams[\'fft_size\']=', hparams['fft_size'], '!=', self.args.data.n_fft,
|
|
'(vocoder)')
|
|
if self.args.data.win_length != hparams['win_size']:
|
|
print('Mismatch parameters: hparams[\'win_size\']=', hparams['win_size'], '!=', self.args.data.win_length,
|
|
'(vocoder)')
|
|
if self.args.data.block_size != hparams['hop_size']:
|
|
print('Mismatch parameters: hparams[\'hop_size\']=', hparams['hop_size'], '!=', self.args.data.block_size,
|
|
'(vocoder)')
|
|
if self.args.data.mel_fmin != hparams['fmin']:
|
|
print('Mismatch parameters: hparams[\'fmin\']=', hparams['fmin'], '!=', self.args.data.mel_fmin,
|
|
'(vocoder)')
|
|
if self.args.data.mel_fmax != hparams['fmax']:
|
|
print('Mismatch parameters: hparams[\'fmax\']=', hparams['fmax'], '!=', self.args.data.mel_fmax,
|
|
'(vocoder)')
|
|
with torch.no_grad():
|
|
mel = torch.FloatTensor(mel).unsqueeze(0).to(self.device)
|
|
mel_base = hparams.get('mel_base', 10)
|
|
if mel_base != 'e':
|
|
assert mel_base in [10, '10'], "mel_base must be 'e', '10' or 10."
|
|
else:
|
|
# log mel to log10 mel
|
|
mel = 0.434294 * mel
|
|
f0 = torch.FloatTensor(f0).unsqueeze(0).unsqueeze(-1).to(self.device)
|
|
signal, _, (s_h, s_n) = self.model(mel, f0)
|
|
signal = signal.view(-1)
|
|
wav_out = signal.cpu().numpy()
|
|
return wav_out
|