import torch import utils from utils.hparams import hparams from .diff.net import DiffNet from .diff.shallow_diffusion_tts import GaussianDiffusion, OfflineGaussianDiffusion from .diffspeech_task import DiffSpeechTask from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder from modules.fastspeech.pe import PitchExtractor from modules.fastspeech.fs2 import FastSpeech2 from modules.diffsinger_midi.fs2 import FastSpeech2MIDI from modules.fastspeech.tts_modules import mel2ph_to_dur from usr.diff.candidate_decoder import FFT from utils.pitch_utils import denorm_f0 from tasks.tts.fs2_utils import FastSpeechDataset from tasks.tts.fs2 import FastSpeech2Task import numpy as np import os import torch.nn.functional as F DIFF_DECODERS = { 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']), 'fft': lambda hp: FFT( hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']), } class DiffSingerTask(DiffSpeechTask): def __init__(self): super(DiffSingerTask, self).__init__() self.dataset_cls = FastSpeechDataset self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() if hparams.get('pe_enable') is not None and hparams['pe_enable']: self.pe = PitchExtractor().cuda() utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True) self.pe.eval() def build_tts_model(self): # import torch # from tqdm import tqdm # v_min = torch.ones([80]) * 100 # v_max = torch.ones([80]) * -100 # for i, ds in enumerate(tqdm(self.dataset_cls('train'))): # v_max = torch.max(torch.max(ds['mel'].reshape(-1, 80), 0)[0], v_max) # v_min = torch.min(torch.min(ds['mel'].reshape(-1, 80), 0)[0], v_min) # if i % 100 == 0: # print(i, v_min, v_max) # print('final', v_min, v_max) mel_bins = hparams['audio_num_mel_bins'] self.model = GaussianDiffusion( phone_encoder=self.phone_encoder, out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), timesteps=hparams['timesteps'], K_step=hparams['K_step'], loss_type=hparams['diff_loss_type'], spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], ) if hparams['fs2_ckpt'] != '': utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) # self.model.fs2.decoder = None for k, v in self.model.fs2.named_parameters(): v.requires_grad = False def validation_step(self, sample, batch_idx): outputs = {} txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] energy = sample['energy'] # fs2_mel = sample['fs2_mels'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') mel2ph = sample['mel2ph'] f0 = sample['f0'] uv = sample['uv'] outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = utils.tensors_to_scalars(outputs) if batch_idx < hparams['num_valid_plots']: model_out = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True) if hparams.get('pe_enable') is not None and hparams['pe_enable']: gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel else: gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) pred_f0 = model_out.get('f0_denorm') self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}') self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}') return outputs class ShallowDiffusionOfflineDataset(FastSpeechDataset): def __getitem__(self, index): sample = super(ShallowDiffusionOfflineDataset, self).__getitem__(index) item = self._get_item(index) if self.prefix != 'train' and hparams['fs2_ckpt'] != '': fs2_ckpt = os.path.dirname(hparams['fs2_ckpt']) item_name = item['item_name'] fs2_mel = torch.Tensor(np.load(f'{fs2_ckpt}/P_mels_npy/{item_name}.npy')) # ~M generated by FFT-singer. sample['fs2_mel'] = fs2_mel return sample def collater(self, samples): batch = super(ShallowDiffusionOfflineDataset, self).collater(samples) if self.prefix != 'train' and hparams['fs2_ckpt'] != '': batch['fs2_mels'] = utils.collate_2d([s['fs2_mel'] for s in samples], 0.0) return batch class DiffSingerOfflineTask(DiffSingerTask): def __init__(self): super(DiffSingerOfflineTask, self).__init__() self.dataset_cls = ShallowDiffusionOfflineDataset def build_tts_model(self): mel_bins = hparams['audio_num_mel_bins'] self.model = OfflineGaussianDiffusion( phone_encoder=self.phone_encoder, out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), timesteps=hparams['timesteps'], K_step=hparams['K_step'], loss_type=hparams['diff_loss_type'], spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], ) # if hparams['fs2_ckpt'] != '': # utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) # self.model.fs2.decoder = None def run_model(self, model, sample, return_output=False, infer=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] mel2ph = sample['mel2ph'] # [B, T_s] f0 = sample['f0'] uv = sample['uv'] energy = sample['energy'] fs2_mel = None #sample['fs2_mels'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=[target, fs2_mel], f0=f0, uv=uv, energy=energy, infer=infer) losses = {} if 'diff_loss' in output: losses['mel'] = output['diff_loss'] # self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) # if hparams['use_pitch_embed']: # self.add_pitch_loss(output, sample, losses) if hparams['use_energy_embed']: self.add_energy_loss(output['energy_pred'], energy, losses) if not return_output: return losses else: return losses, output def validation_step(self, sample, batch_idx): outputs = {} txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] energy = sample['energy'] # fs2_mel = sample['fs2_mels'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') mel2ph = sample['mel2ph'] f0 = sample['f0'] uv = sample['uv'] outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = utils.tensors_to_scalars(outputs) if batch_idx < hparams['num_valid_plots']: fs2_mel = sample['fs2_mels'] model_out = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=[None, fs2_mel], infer=True) if hparams.get('pe_enable') is not None and hparams['pe_enable']: gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel else: gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) pred_f0 = model_out.get('f0_denorm') self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}') self.plot_mel(batch_idx, sample['mels'], fs2_mel, name=f'fs2mel_{batch_idx}') return outputs def test_step(self, sample, batch_idx): spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') txt_tokens = sample['txt_tokens'] energy = sample['energy'] if hparams['profile_infer']: pass else: mel2ph, uv, f0 = None, None, None if hparams['use_gt_dur']: mel2ph = sample['mel2ph'] if hparams['use_gt_f0']: f0 = sample['f0'] uv = sample['uv'] fs2_mel = sample['fs2_mels'] outputs = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=[None, fs2_mel], energy=energy, infer=True) sample['outputs'] = self.model.out2mel(outputs['mel_out']) sample['mel2ph_pred'] = outputs['mel2ph'] if hparams.get('pe_enable') is not None and hparams['pe_enable']: sample['f0'] = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel sample['f0_pred'] = self.pe(sample['outputs'])['f0_denorm_pred'] # pe predict from Pred mel else: sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams) sample['f0_pred'] = outputs.get('f0_denorm') return self.after_infer(sample) class MIDIDataset(FastSpeechDataset): def __getitem__(self, index): sample = super(MIDIDataset, self).__getitem__(index) item = self._get_item(index) sample['f0_midi'] = torch.FloatTensor(item['f0_midi']) sample['pitch_midi'] = torch.LongTensor(item['pitch_midi'])[:hparams['max_frames']] return sample def collater(self, samples): batch = super(MIDIDataset, self).collater(samples) batch['f0_midi'] = utils.collate_1d([s['f0_midi'] for s in samples], 0.0) batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0) # print((batch['pitch_midi'] == f0_to_coarse(batch['f0_midi'])).all()) return batch class OpencpopDataset(FastSpeechDataset): def __getitem__(self, index): sample = super(OpencpopDataset, self).__getitem__(index) item = self._get_item(index) sample['pitch_midi'] = torch.LongTensor(item['pitch_midi'])[:hparams['max_frames']] sample['midi_dur'] = torch.FloatTensor(item['midi_dur'])[:hparams['max_frames']] sample['is_slur'] = torch.LongTensor(item['is_slur'])[:hparams['max_frames']] sample['word_boundary'] = torch.LongTensor(item['word_boundary'])[:hparams['max_frames']] return sample def collater(self, samples): batch = super(OpencpopDataset, self).collater(samples) batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0) batch['midi_dur'] = utils.collate_1d([s['midi_dur'] for s in samples], 0) batch['is_slur'] = utils.collate_1d([s['is_slur'] for s in samples], 0) batch['word_boundary'] = utils.collate_1d([s['word_boundary'] for s in samples], 0) return batch class DiffSingerMIDITask(DiffSingerTask): def __init__(self): super(DiffSingerMIDITask, self).__init__() # self.dataset_cls = MIDIDataset self.dataset_cls = OpencpopDataset def run_model(self, model, sample, return_output=False, infer=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] # mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s] mel2ph = sample['mel2ph'] if hparams.get('switch_midi2f0_step') is not None and self.global_step > hparams['switch_midi2f0_step']: f0 = None uv = None else: f0 = sample['f0'] uv = sample['uv'] energy = sample['energy'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer, pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) losses = {} if 'diff_loss' in output: losses['mel'] = output['diff_loss'] self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses) if hparams['use_pitch_embed']: self.add_pitch_loss(output, sample, losses) if hparams['use_energy_embed']: self.add_energy_loss(output['energy_pred'], energy, losses) if not return_output: return losses else: return losses, output def validation_step(self, sample, batch_idx): outputs = {} txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] energy = sample['energy'] # fs2_mel = sample['fs2_mels'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') mel2ph = sample['mel2ph'] outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = utils.tensors_to_scalars(outputs) if batch_idx < hparams['num_valid_plots']: model_out = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=None, uv=None, energy=energy, ref_mels=None, infer=True, pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) if hparams.get('pe_enable') is not None and hparams['pe_enable']: gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel else: gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) pred_f0 = model_out.get('f0_denorm') self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}') self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}') if hparams['use_pitch_embed']: self.plot_pitch(batch_idx, sample, model_out) return outputs def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None): """ :param dur_pred: [B, T], float, log scale :param mel2ph: [B, T] :param txt_tokens: [B, T] :param losses: :return: """ B, T = txt_tokens.shape nonpadding = (txt_tokens != 0).float() dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding is_sil = torch.zeros_like(txt_tokens).bool() for p in self.sil_ph: is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0]) is_sil = is_sil.float() # [B, T_txt] # phone duration loss if hparams['dur_loss'] == 'mse': losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none') losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum() dur_pred = (dur_pred.exp() - 1).clamp(min=0) else: raise NotImplementedError # use linear scale for sent and word duration if hparams['lambda_word_dur'] > 0: idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1] # word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur) # midi_dur can be implied by add gt-ph_dur word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred) word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt) wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none') word_nonpadding = (word_dur_g > 0).float() wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum() losses['wdur'] = wdur_loss * hparams['lambda_word_dur'] if hparams['lambda_sent_dur'] > 0: sent_dur_p = dur_pred.sum(-1) sent_dur_g = dur_gt.sum(-1) sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean') losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] class AuxDecoderMIDITask(FastSpeech2Task): def __init__(self): super().__init__() # self.dataset_cls = MIDIDataset self.dataset_cls = OpencpopDataset def build_tts_model(self): if hparams.get('use_midi') is not None and hparams['use_midi']: self.model = FastSpeech2MIDI(self.phone_encoder) else: self.model = FastSpeech2(self.phone_encoder) def run_model(self, model, sample, return_output=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] mel2ph = sample['mel2ph'] # [B, T_s] f0 = sample['f0'] uv = sample['uv'] energy = sample['energy'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=target, f0=f0, uv=uv, energy=energy, infer=False, pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) losses = {} self.add_mel_loss(output['mel_out'], target, losses) self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses) if hparams['use_pitch_embed']: self.add_pitch_loss(output, sample, losses) if hparams['use_energy_embed']: self.add_energy_loss(output['energy_pred'], energy, losses) if not return_output: return losses else: return losses, output def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None): """ :param dur_pred: [B, T], float, log scale :param mel2ph: [B, T] :param txt_tokens: [B, T] :param losses: :return: """ B, T = txt_tokens.shape nonpadding = (txt_tokens != 0).float() dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding is_sil = torch.zeros_like(txt_tokens).bool() for p in self.sil_ph: is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0]) is_sil = is_sil.float() # [B, T_txt] # phone duration loss if hparams['dur_loss'] == 'mse': losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none') losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum() dur_pred = (dur_pred.exp() - 1).clamp(min=0) else: raise NotImplementedError # use linear scale for sent and word duration if hparams['lambda_word_dur'] > 0: idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1] # word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur) # midi_dur can be implied by add gt-ph_dur word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred) word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt) wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none') word_nonpadding = (word_dur_g > 0).float() wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum() losses['wdur'] = wdur_loss * hparams['lambda_word_dur'] if hparams['lambda_sent_dur'] > 0: sent_dur_p = dur_pred.sum(-1) sent_dur_g = dur_gt.sum(-1) sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean') losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] def validation_step(self, sample, batch_idx): outputs = {} outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] mel_out = self.model.out2mel(model_out['mel_out']) outputs = utils.tensors_to_scalars(outputs) # if sample['mels'].shape[0] == 1: # self.add_laplace_var(mel_out, sample['mels'], outputs) if batch_idx < hparams['num_valid_plots']: self.plot_mel(batch_idx, sample['mels'], mel_out) self.plot_dur(batch_idx, sample, model_out) if hparams['use_pitch_embed']: self.plot_pitch(batch_idx, sample, model_out) return outputs