Spaces:
Running
Running
File size: 11,660 Bytes
26925fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 |
from modules.commons.common_layers import *
from modules.commons.common_layers import Embedding
from modules.fastspeech.tts_modules import FastspeechDecoder, DurationPredictor, LengthRegulator, PitchPredictor, \
EnergyPredictor, FastspeechEncoder
from utils.cwt import cwt2f0
from utils.hparams import hparams
from utils.pitch_utils import f0_to_coarse, denorm_f0, norm_f0
FS_ENCODERS = {
'fft': lambda hp, embed_tokens, d: FastspeechEncoder(
embed_tokens, hp['hidden_size'], hp['enc_layers'], hp['enc_ffn_kernel_size'],
num_heads=hp['num_heads']),
}
FS_DECODERS = {
'fft': lambda hp: FastspeechDecoder(
hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
}
class FastSpeech2(nn.Module):
def __init__(self, dictionary, out_dims=None):
super().__init__()
self.dictionary = dictionary
self.padding_idx = dictionary.pad()
self.enc_layers = hparams['enc_layers']
self.dec_layers = hparams['dec_layers']
self.hidden_size = hparams['hidden_size']
self.encoder_embed_tokens = self.build_embedding(self.dictionary, self.hidden_size)
self.encoder = FS_ENCODERS[hparams['encoder_type']](hparams, self.encoder_embed_tokens, self.dictionary)
self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
self.out_dims = out_dims
if out_dims is None:
self.out_dims = hparams['audio_num_mel_bins']
self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True)
if hparams['use_spk_id']:
self.spk_embed_proj = Embedding(hparams['num_spk'] + 1, self.hidden_size)
if hparams['use_split_spk_id']:
self.spk_embed_f0 = Embedding(hparams['num_spk'] + 1, self.hidden_size)
self.spk_embed_dur = Embedding(hparams['num_spk'] + 1, self.hidden_size)
elif hparams['use_spk_embed']:
self.spk_embed_proj = Linear(256, self.hidden_size, bias=True)
predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
self.dur_predictor = DurationPredictor(
self.hidden_size,
n_chans=predictor_hidden,
n_layers=hparams['dur_predictor_layers'],
dropout_rate=hparams['predictor_dropout'], padding=hparams['ffn_padding'],
kernel_size=hparams['dur_predictor_kernel'])
self.length_regulator = LengthRegulator()
if hparams['use_pitch_embed']:
self.pitch_embed = Embedding(300, self.hidden_size, self.padding_idx)
if hparams['pitch_type'] == 'cwt':
h = hparams['cwt_hidden_size']
cwt_out_dims = 10
if hparams['use_uv']:
cwt_out_dims = cwt_out_dims + 1
self.cwt_predictor = nn.Sequential(
nn.Linear(self.hidden_size, h),
PitchPredictor(
h,
n_chans=predictor_hidden,
n_layers=hparams['predictor_layers'],
dropout_rate=hparams['predictor_dropout'], odim=cwt_out_dims,
padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel']))
self.cwt_stats_layers = nn.Sequential(
nn.Linear(self.hidden_size, h), nn.ReLU(),
nn.Linear(h, h), nn.ReLU(), nn.Linear(h, 2)
)
else:
self.pitch_predictor = PitchPredictor(
self.hidden_size,
n_chans=predictor_hidden,
n_layers=hparams['predictor_layers'],
dropout_rate=hparams['predictor_dropout'],
odim=2 if hparams['pitch_type'] == 'frame' else 1,
padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel'])
if hparams['use_energy_embed']:
self.energy_embed = Embedding(256, self.hidden_size, self.padding_idx)
self.energy_predictor = EnergyPredictor(
self.hidden_size,
n_chans=predictor_hidden,
n_layers=hparams['predictor_layers'],
dropout_rate=hparams['predictor_dropout'], odim=1,
padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel'])
def build_embedding(self, dictionary, embed_dim):
num_embeddings = len(dictionary)
emb = Embedding(num_embeddings, embed_dim, self.padding_idx)
return emb
def forward(self, txt_tokens, mel2ph=None, spk_embed=None,
ref_mels=None, f0=None, uv=None, energy=None, skip_decoder=False,
spk_embed_dur_id=None, spk_embed_f0_id=None, infer=False, **kwargs):
ret = {}
encoder_out = self.encoder(txt_tokens) # [B, T, C]
src_nonpadding = (txt_tokens > 0).float()[:, :, None]
# add ref style embed
# Not implemented
# variance encoder
var_embed = 0
# encoder_out_dur denotes encoder outputs for duration predictor
# in speech adaptation, duration predictor use old speaker embedding
if hparams['use_spk_embed']:
spk_embed_dur = spk_embed_f0 = spk_embed = self.spk_embed_proj(spk_embed)[:, None, :]
elif hparams['use_spk_id']:
spk_embed_id = spk_embed
if spk_embed_dur_id is None:
spk_embed_dur_id = spk_embed_id
if spk_embed_f0_id is None:
spk_embed_f0_id = spk_embed_id
spk_embed = self.spk_embed_proj(spk_embed_id)[:, None, :]
spk_embed_dur = spk_embed_f0 = spk_embed
if hparams['use_split_spk_id']:
spk_embed_dur = self.spk_embed_dur(spk_embed_dur_id)[:, None, :]
spk_embed_f0 = self.spk_embed_f0(spk_embed_f0_id)[:, None, :]
else:
spk_embed_dur = spk_embed_f0 = spk_embed = 0
# add dur
dur_inp = (encoder_out + var_embed + spk_embed_dur) * src_nonpadding
mel2ph = self.add_dur(dur_inp, mel2ph, txt_tokens, ret)
decoder_inp = F.pad(encoder_out, [0, 0, 1, 0])
mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]])
decoder_inp_origin = decoder_inp = torch.gather(decoder_inp, 1, mel2ph_) # [B, T, H]
tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
# add pitch and energy embed
pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding
if hparams['use_pitch_embed']:
pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding
decoder_inp = decoder_inp + self.add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph)
if hparams['use_energy_embed']:
decoder_inp = decoder_inp + self.add_energy(pitch_inp, energy, ret)
ret['decoder_inp'] = decoder_inp = (decoder_inp + spk_embed) * tgt_nonpadding
if skip_decoder:
return ret
ret['mel_out'] = self.run_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs)
return ret
def add_dur(self, dur_input, mel2ph, txt_tokens, ret):
"""
:param dur_input: [B, T_txt, H]
:param mel2ph: [B, T_mel]
:param txt_tokens: [B, T_txt]
:param ret:
:return:
"""
src_padding = txt_tokens == 0
dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach())
if mel2ph is None:
dur, xs = self.dur_predictor.inference(dur_input, src_padding)
ret['dur'] = xs
ret['dur_choice'] = dur
mel2ph = self.length_regulator(dur, src_padding).detach()
# from modules.fastspeech.fake_modules import FakeLengthRegulator
# fake_lr = FakeLengthRegulator()
# fake_mel2ph = fake_lr(dur, (1 - src_padding.long()).sum(-1))[..., 0].detach()
# print(mel2ph == fake_mel2ph)
else:
ret['dur'] = self.dur_predictor(dur_input, src_padding)
ret['mel2ph'] = mel2ph
return mel2ph
def add_energy(self, decoder_inp, energy, ret):
decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
ret['energy_pred'] = energy_pred = self.energy_predictor(decoder_inp)[:, :, 0]
if energy is None:
energy = energy_pred
energy = torch.clamp(energy * 256 // 4, max=255).long()
energy_embed = self.energy_embed(energy)
return energy_embed
def add_pitch(self, decoder_inp, f0, uv, mel2ph, ret, encoder_out=None):
if hparams['pitch_type'] == 'ph':
pitch_pred_inp = encoder_out.detach() + hparams['predictor_grad'] * (encoder_out - encoder_out.detach())
pitch_padding = encoder_out.sum().abs() == 0
ret['pitch_pred'] = pitch_pred = self.pitch_predictor(pitch_pred_inp)
if f0 is None:
f0 = pitch_pred[:, :, 0]
ret['f0_denorm'] = f0_denorm = denorm_f0(f0, None, hparams, pitch_padding=pitch_padding)
pitch = f0_to_coarse(f0_denorm) # start from 0 [B, T_txt]
pitch = F.pad(pitch, [1, 0])
pitch = torch.gather(pitch, 1, mel2ph) # [B, T_mel]
pitch_embed = self.pitch_embed(pitch)
return pitch_embed
decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
pitch_padding = mel2ph == 0
if hparams['pitch_type'] == 'cwt':
pitch_padding = None
ret['cwt'] = cwt_out = self.cwt_predictor(decoder_inp)
stats_out = self.cwt_stats_layers(encoder_out[:, 0, :]) # [B, 2]
mean = ret['f0_mean'] = stats_out[:, 0]
std = ret['f0_std'] = stats_out[:, 1]
cwt_spec = cwt_out[:, :, :10]
if f0 is None:
std = std * hparams['cwt_std_scale']
f0 = self.cwt2f0_norm(cwt_spec, mean, std, mel2ph)
if hparams['use_uv']:
assert cwt_out.shape[-1] == 11
uv = cwt_out[:, :, -1] > 0
elif hparams['pitch_ar']:
ret['pitch_pred'] = pitch_pred = self.pitch_predictor(decoder_inp, f0 if self.training else None)
if f0 is None:
f0 = pitch_pred[:, :, 0]
else:
ret['pitch_pred'] = pitch_pred = self.pitch_predictor(decoder_inp)
if f0 is None:
f0 = pitch_pred[:, :, 0]
if hparams['use_uv'] and uv is None:
uv = pitch_pred[:, :, 1] > 0
ret['f0_denorm'] = f0_denorm = denorm_f0(f0, uv, hparams, pitch_padding=pitch_padding)
if pitch_padding is not None:
f0[pitch_padding] = 0
pitch = f0_to_coarse(f0_denorm) # start from 0
pitch_embed = self.pitch_embed(pitch)
return pitch_embed
def run_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs):
x = decoder_inp # [B, T, H]
x = self.decoder(x)
x = self.mel_out(x)
return x * tgt_nonpadding
def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph):
f0 = cwt2f0(cwt_spec, mean, std, hparams['cwt_scales'])
f0 = torch.cat(
[f0] + [f0[:, -1:]] * (mel2ph.shape[1] - f0.shape[1]), 1)
f0_norm = norm_f0(f0, None, hparams)
return f0_norm
def out2mel(self, out):
return out
@staticmethod
def mel_norm(x):
return (x + 5.5) / (6.3 / 2) - 1
@staticmethod
def mel_denorm(x):
return (x + 1) * (6.3 / 2) - 5.5
|