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| 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 | |
| def mel_norm(x): | |
| return (x + 5.5) / (6.3 / 2) - 1 | |
| def mel_denorm(x): | |
| return (x + 1) * (6.3 / 2) - 5.5 | |