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from utils.hparams import hparams
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.pitch_utils import f0_to_coarse, denorm_f0, norm_f0
import torch.nn as nn
from modules.commons.rel_transformer import RelTransformerEncoder, BERTRelTransformerEncoder
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)
if hparams.get("use_bert", False):
self.ph_encoder = BERTRelTransformerEncoder(len(self.dictionary), hparams['hidden_size'], hparams['hidden_size'],
hparams['ffn_hidden_size'], hparams['num_heads'], hparams['enc_layers'],
hparams['enc_ffn_kernel_size'], hparams['dropout'], prenet=hparams['enc_prenet'], pre_ln=hparams['enc_pre_ln'])
else:
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 = hparams['audio_num_mel_bins'] if out_dims is None else out_dims
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'],
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)
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,
kernel_size=hparams['predictor_kernel'])
if hparams.get('use_energy_embed', False):
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,
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 = {}
if hparams.get("use_bert", False):
encoder_out = self.encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=kwargs['ph2word'], ret=ret)
else:
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.get('use_energy_embed', False):
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
def expand_states(self, h, mel2ph):
h = F.pad(h, [0, 0, 1, 0])
mel2ph_ = mel2ph[..., None].repeat([1, 1, h.shape[-1]])
h = torch.gather(h, 1, mel2ph_) # [B, T, H]
return h
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