219 lines
9.8 KiB
Python
219 lines
9.8 KiB
Python
import torch
|
|
from torch.nn import functional as F
|
|
|
|
from utils.hparams import hparams
|
|
from utils.pitch_utils import f0_to_coarse, denorm_f0
|
|
|
|
|
|
class Batch2Loss:
|
|
'''
|
|
pipeline: batch -> insert1 -> module1 -> insert2 -> module2 -> insert3 -> module3 -> insert4 -> module4 -> loss
|
|
'''
|
|
|
|
@staticmethod
|
|
def insert1(pitch_midi, midi_dur, is_slur, # variables
|
|
midi_embed, midi_dur_layer, is_slur_embed): # modules
|
|
'''
|
|
add embeddings for midi, midi_dur, slur
|
|
'''
|
|
midi_embedding = midi_embed(pitch_midi)
|
|
midi_dur_embedding, slur_embedding = 0, 0
|
|
if midi_dur is not None:
|
|
midi_dur_embedding = midi_dur_layer(midi_dur[:, :, None]) # [B, T, 1] -> [B, T, H]
|
|
if is_slur is not None:
|
|
slur_embedding = is_slur_embed(is_slur)
|
|
return midi_embedding, midi_dur_embedding, slur_embedding
|
|
|
|
@staticmethod
|
|
def module1(fs2_encoder, # modules
|
|
txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding): # variables
|
|
'''
|
|
get *encoder_out* == fs2_encoder(*txt_tokens*, some embeddings)
|
|
'''
|
|
encoder_out = fs2_encoder(txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding)
|
|
return encoder_out
|
|
|
|
@staticmethod
|
|
def insert2(encoder_out, spk_embed_id, spk_embed_dur_id, spk_embed_f0_id, src_nonpadding, # variables
|
|
spk_embed_proj): # modules
|
|
'''
|
|
1. add embeddings for pspk, spk_dur, sk_f0
|
|
2. get *dur_inp* ~= *encoder_out* + *spk_embed_dur*
|
|
'''
|
|
# 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_id']:
|
|
spk_embed = spk_embed_proj(spk_embed_id)[:, None, :]
|
|
spk_embed_dur = spk_embed_f0 = spk_embed
|
|
else:
|
|
spk_embed_dur = spk_embed_f0 = spk_embed = 0
|
|
|
|
# add dur
|
|
dur_inp = (encoder_out + var_embed + spk_embed_dur) * src_nonpadding
|
|
return var_embed, spk_embed, spk_embed_dur, spk_embed_f0, dur_inp
|
|
|
|
@staticmethod
|
|
def module2(dur_predictor, length_regulator, # modules
|
|
dur_input, mel2ph, txt_tokens, all_vowel_tokens, ret, midi_dur=None): # variables
|
|
'''
|
|
1. get *dur* ~= dur_predictor(*dur_inp*)
|
|
2. (mel2ph is None): get *mel2ph* ~= length_regulater(*dur*)
|
|
'''
|
|
src_padding = (txt_tokens == 0)
|
|
dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach())
|
|
|
|
if mel2ph is None:
|
|
dur, xs = dur_predictor.inference(dur_input, src_padding)
|
|
ret['dur'] = xs
|
|
dur = xs.squeeze(-1).exp() - 1.0
|
|
for i in range(len(dur)):
|
|
for j in range(len(dur[i])):
|
|
if txt_tokens[i, j] in all_vowel_tokens:
|
|
if j < len(dur[i]) - 1 and txt_tokens[i, j + 1] not in all_vowel_tokens:
|
|
dur[i, j] = midi_dur[i, j] - dur[i, j + 1]
|
|
if dur[i, j] < 0:
|
|
dur[i, j] = 0
|
|
dur[i, j + 1] = midi_dur[i, j]
|
|
else:
|
|
dur[i, j] = midi_dur[i, j]
|
|
dur[:, 0] = dur[:, 0] + 0.5
|
|
dur_acc = F.pad(torch.round(torch.cumsum(dur, axis=1)), (1, 0))
|
|
dur = torch.clamp(dur_acc[:, 1:] - dur_acc[:, :-1], min=0).long()
|
|
ret['dur_choice'] = dur
|
|
mel2ph = length_regulator(dur, src_padding).detach()
|
|
else:
|
|
ret['dur'] = dur_predictor(dur_input, src_padding)
|
|
ret['mel2ph'] = mel2ph
|
|
|
|
return mel2ph
|
|
|
|
@staticmethod
|
|
def insert3(encoder_out, mel2ph, var_embed, spk_embed_f0, src_nonpadding, tgt_nonpadding): # variables
|
|
'''
|
|
1. get *decoder_inp* ~= gather *encoder_out* according to *mel2ph*
|
|
2. get *pitch_inp* ~= *decoder_inp* + *spk_embed_f0*
|
|
3. get *pitch_inp_ph* ~= *encoder_out* + *spk_embed_f0*
|
|
'''
|
|
decoder_inp = F.pad(encoder_out, [0, 0, 1, 0])
|
|
mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]])
|
|
decoder_inp = decoder_inp_origin = torch.gather(decoder_inp, 1, mel2ph_) # [B, T, H]
|
|
|
|
pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding
|
|
pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding
|
|
return decoder_inp, pitch_inp, pitch_inp_ph
|
|
|
|
@staticmethod
|
|
def module3(pitch_predictor, pitch_embed, energy_predictor, energy_embed, # modules
|
|
pitch_inp, pitch_inp_ph, f0, uv, energy, mel2ph, is_training, ret): # variables
|
|
'''
|
|
1. get *ret['pitch_pred']*, *ret['energy_pred']* ~= pitch_predictor(*pitch_inp*), energy_predictor(*pitch_inp*)
|
|
2. get *pitch_embedding* ~= pitch_embed(f0_to_coarse(denorm_f0(*f0* or *pitch_pred*))
|
|
3. get *energy_embedding* ~= energy_embed(energy_to_coarse(*energy* or *energy_pred*))
|
|
'''
|
|
|
|
def add_pitch(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 = 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_embedding = pitch_embed(pitch)
|
|
return pitch_embedding
|
|
|
|
decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
|
|
|
|
pitch_padding = (mel2ph == 0)
|
|
|
|
if hparams['pitch_ar']:
|
|
ret['pitch_pred'] = pitch_pred = pitch_predictor(decoder_inp, f0 if is_training else None)
|
|
if f0 is None:
|
|
f0 = pitch_pred[:, :, 0]
|
|
else:
|
|
ret['pitch_pred'] = pitch_pred = 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_embedding = pitch_embed(pitch)
|
|
return pitch_embedding
|
|
|
|
def add_energy(decoder_inp, energy, ret):
|
|
decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
|
|
ret['energy_pred'] = energy_pred = energy_predictor(decoder_inp)[:, :, 0]
|
|
if energy is None:
|
|
energy = energy_pred
|
|
energy = torch.clamp(energy * 256 // 4, max=255).long() # energy_to_coarse
|
|
energy_embedding = energy_embed(energy)
|
|
return energy_embedding
|
|
|
|
# add pitch and energy embed
|
|
nframes = mel2ph.size(1)
|
|
|
|
pitch_embedding = 0
|
|
if hparams['use_pitch_embed']:
|
|
if f0 is not None:
|
|
delta_l = nframes - f0.size(1)
|
|
if delta_l > 0:
|
|
f0 = torch.cat((f0, torch.FloatTensor([[x[-1]] * delta_l for x in f0]).to(f0.device)), 1)
|
|
f0 = f0[:, :nframes]
|
|
if uv is not None:
|
|
delta_l = nframes - uv.size(1)
|
|
if delta_l > 0:
|
|
uv = torch.cat((uv, torch.FloatTensor([[x[-1]] * delta_l for x in uv]).to(uv.device)), 1)
|
|
uv = uv[:, :nframes]
|
|
pitch_embedding = add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph)
|
|
|
|
energy_embedding = 0
|
|
if hparams['use_energy_embed']:
|
|
if energy is not None:
|
|
delta_l = nframes - energy.size(1)
|
|
if delta_l > 0:
|
|
energy = torch.cat(
|
|
(energy, torch.FloatTensor([[x[-1]] * delta_l for x in energy]).to(energy.device)), 1)
|
|
energy = energy[:, :nframes]
|
|
energy_embedding = add_energy(pitch_inp, energy, ret)
|
|
|
|
return pitch_embedding, energy_embedding
|
|
|
|
@staticmethod
|
|
def insert4(decoder_inp, pitch_embedding, energy_embedding, spk_embed, ret, tgt_nonpadding):
|
|
'''
|
|
*decoder_inp* ~= *decoder_inp* + embeddings for spk, pitch, energy
|
|
'''
|
|
ret['decoder_inp'] = decoder_inp = (
|
|
decoder_inp + pitch_embedding + energy_embedding + spk_embed) * tgt_nonpadding
|
|
return decoder_inp
|
|
|
|
@staticmethod
|
|
def module4(diff_main_loss, # modules
|
|
norm_spec, decoder_inp_t, ret, K_step, batch_size, device): # variables
|
|
'''
|
|
training diffusion using spec as input and decoder_inp as condition.
|
|
|
|
Args:
|
|
norm_spec: (normalized) spec
|
|
decoder_inp_t: (transposed) decoder_inp
|
|
Returns:
|
|
ret['diff_loss']
|
|
'''
|
|
t = torch.randint(0, K_step, (batch_size,), device=device).long()
|
|
norm_spec = norm_spec.transpose(1, 2)[:, None, :, :] # [B, 1, M, T]
|
|
ret['diff_loss'] = diff_main_loss(norm_spec, t, cond=decoder_inp_t)
|
|
# nonpadding = (mel2ph != 0).float()
|
|
# ret['diff_loss'] = self.p_losses(x, t, cond, nonpadding=nonpadding)
|