text_to_speech/tasks/tts/speech_base.py

import filecmp
import os
import traceback
import numpy as np
import pandas as pd
import torch
import torch.distributed as dist
import torch.nn.functional as F
import torch.optim
import torch.utils.data
import yaml
from tqdm import tqdm
import utils
from tasks.tts.dataset_utils import BaseSpeechDataset
from tasks.tts.utils. import parse_mel_losses, parse_dataset_configs, load_data_preprocessor, load_data_binarizer
from tasks.tts.vocoder_infer.base_vocoder import BaseVocoder, get_vocoder_cls
from text_to_speech.utils.audio.align import mel2token_to_dur
from text_to_speech.utils.audio.io import save_wav
from text_to_speech.utils.audio.pitch_extractors import extract_pitch_simple
from text_to_speech.utils.commons.base_task import BaseTask
from text_to_speech.utils.commons.ckpt_utils import load_ckpt
from text_to_speech.utils.commons.dataset_utils import data_loader, BaseConcatDataset
from text_to_speech.utils.commons.hparams import hparams
from text_to_speech.utils.commons.multiprocess_utils import MultiprocessManager
from text_to_speech.utils.commons.tensor_utils import tensors_to_scalars
from text_to_speech.utils.metrics.ssim import ssim
from text_to_speech.utils.nn.model_utils import print_arch
from text_to_speech.utils.nn.schedulers import RSQRTSchedule, NoneSchedule, WarmupSchedule
from text_to_speech.utils.nn.seq_utils import weights_nonzero_speech
from text_to_speech.utils.plot.plot import spec_to_figure
from text_to_speech.utils.text.text_encoder import build_token_encoder
import matplotlib.pyplot as plt


class SpeechBaseTask(BaseTask):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.dataset_cls = BaseSpeechDataset
        self.vocoder = None
        data_dir = hparams['binary_data_dir']
        if not hparams['use_word_input']:
            self.token_encoder = build_token_encoder(f'{data_dir}/phone_set.json')
        else:
            self.token_encoder = build_token_encoder(f'{data_dir}/word_set.json')
        self.padding_idx = self.token_encoder.pad()
        self.eos_idx = self.token_encoder.eos()
        self.seg_idx = self.token_encoder.seg()
        self.saving_result_pool = None
        self.saving_results_futures = None
        self.mel_losses = parse_mel_losses()
        self.max_tokens, self.max_sentences, \
        self.max_valid_tokens, self.max_valid_sentences = parse_dataset_configs()

    ##########################
    # datasets
    ##########################
    @data_loader
    def train_dataloader(self):
        if hparams['train_sets'] != '':
            train_sets = hparams['train_sets'].split("|")
            # check if all train_sets have the same spk map and dictionary
            binary_data_dir = hparams['binary_data_dir']
            file_to_cmp = ['phone_set.json']
            if os.path.exists(f'{binary_data_dir}/word_set.json'):
                file_to_cmp.append('word_set.json')
            if hparams['use_spk_id']:
                file_to_cmp.append('spk_map.json')
            for f in file_to_cmp:
                for ds_name in train_sets:
                    base_file = os.path.join(binary_data_dir, f)
                    ds_file = os.path.join(ds_name, f)
                    assert filecmp.cmp(base_file, ds_file), \
                        f'{f} in {ds_name} is not same with that in {binary_data_dir}.'
            train_dataset = BaseConcatDataset([
                self.dataset_cls(prefix='train', shuffle=True, data_dir=ds_name) for ds_name in train_sets])
        else:
            train_dataset = self.dataset_cls(prefix=hparams['train_set_name'], shuffle=True)
        return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences,
                                     endless=hparams['endless_ds'])

    @data_loader
    def val_dataloader(self):
        valid_dataset = self.dataset_cls(prefix=hparams['valid_set_name'], shuffle=False)
        return self.build_dataloader(valid_dataset, False, self.max_valid_tokens, self.max_valid_sentences,
                                     batch_by_size=False)

    @data_loader
    def test_dataloader(self):
        test_dataset = self.dataset_cls(prefix=hparams['test_set_name'], shuffle=False)
        self.test_dl = self.build_dataloader(
            test_dataset, False, self.max_valid_tokens, self.max_valid_sentences, batch_by_size=False)
        return self.test_dl

    def build_dataloader(self, dataset, shuffle, max_tokens=None, max_sentences=None,
                         required_batch_size_multiple=-1, endless=False, batch_by_size=True):
        devices_cnt = torch.cuda.device_count()
        if devices_cnt == 0:
            devices_cnt = 1
        if required_batch_size_multiple == -1:
            required_batch_size_multiple = devices_cnt

        def shuffle_batches(batches):
            np.random.shuffle(batches)
            return batches

        if max_tokens is not None:
            max_tokens *= devices_cnt
        if max_sentences is not None:
            max_sentences *= devices_cnt
        indices = dataset.ordered_indices()
        if batch_by_size:
            batch_sampler = utils.commons.dataset_utils.batch_by_size(
                indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences,
                required_batch_size_multiple=required_batch_size_multiple,
            )
        else:
            batch_sampler = []
            for i in range(0, len(indices), max_sentences):
                batch_sampler.append(indices[i:i + max_sentences])

        if shuffle:
            batches = shuffle_batches(list(batch_sampler))
            if endless:
                batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))]
        else:
            batches = batch_sampler
            if endless:
                batches = [b for _ in range(1000) for b in batches]
        num_workers = dataset.num_workers
        if self.trainer.use_ddp:
            num_replicas = dist.get_world_size()
            rank = dist.get_rank()
            batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0]
        return torch.utils.data.DataLoader(dataset,
                                           collate_fn=dataset.collater,
                                           batch_sampler=batches,
                                           num_workers=num_workers,
                                           pin_memory=False)

    ##########################
    # scheduler and optimizer
    ##########################
    def build_model(self):
        self.build_tts_model()
        if hparams['load_ckpt'] != '':
            load_ckpt(self.model, hparams['load_ckpt'])
        print_arch(self.model)
        return self.model

    def build_tts_model(self):
        raise NotImplementedError

    def build_scheduler(self, optimizer):
        if hparams['scheduler'] == 'rsqrt':
            return RSQRTSchedule(optimizer, hparams['lr'], hparams['warmup_updates'], hparams['hidden_size'])
        elif hparams['scheduler'] == 'warmup':
            return WarmupSchedule(optimizer, hparams['lr'], hparams['warmup_updates'])
        elif hparams['scheduler'] == 'step_lr':
            return torch.optim.lr_scheduler.StepLR(
                optimizer=optimizer, step_size=500, gamma=0.998)
        else:
            return NoneSchedule(optimizer, hparams['lr'])

    def build_optimizer(self, model):
        self.optimizer = optimizer = torch.optim.AdamW(
            model.parameters(),
            lr=hparams['lr'],
            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
            weight_decay=hparams['weight_decay'])

        return optimizer

    ##########################
    # training and validation
    ##########################
    def _training_step(self, sample, batch_idx, _):
        loss_output, _ = self.run_model(sample)
        total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad])
        loss_output['batch_size'] = sample['txt_tokens'].size()[0]
        return total_loss, loss_output

    def run_model(self, sample, infer=False):
        """

        :param sample: a batch of data
        :param infer: bool, run in infer mode
        :return:
            if not infer:
                return losses, model_out
            if infer:
                return model_out
        """
        raise NotImplementedError

    def validation_start(self):
        self.vocoder = get_vocoder_cls(hparams['vocoder'])()

    def validation_step(self, sample, batch_idx):
        outputs = {}
        outputs['losses'] = {}
        outputs['losses'], model_out = self.run_model(sample)
        outputs['total_loss'] = sum(outputs['losses'].values())
        outputs['nsamples'] = sample['nsamples']
        outputs = tensors_to_scalars(outputs)
        if self.global_step % hparams['valid_infer_interval'] == 0 \
                and batch_idx < hparams['num_valid_plots']:
            self.save_valid_result(sample, batch_idx, model_out)
        return outputs

    def validation_end(self, outputs):
        self.vocoder = None
        return super(SpeechBaseTask, self).validation_end(outputs)

    def save_valid_result(self, sample, batch_idx, model_out):
        raise NotImplementedError

    ##########################
    # losses
    ##########################
    def add_mel_loss(self, mel_out, target, losses, postfix=''):
        for loss_name, lambd in self.mel_losses.items():
            losses[f'{loss_name}{postfix}'] = getattr(self, f'{loss_name}_loss')(mel_out, target) * lambd

    def l1_loss(self, decoder_output, target):
        # decoder_output : B x T x n_mel
        # target : B x T x n_mel
        l1_loss = F.l1_loss(decoder_output, target, reduction='none')
        weights = weights_nonzero_speech(target)
        l1_loss = (l1_loss * weights).sum() / weights.sum()
        return l1_loss

    def mse_loss(self, decoder_output, target):
        # decoder_output : B x T x n_mel
        # target : B x T x n_mel
        assert decoder_output.shape == target.shape
        mse_loss = F.mse_loss(decoder_output, target, reduction='none')
        weights = weights_nonzero_speech(target)
        mse_loss = (mse_loss * weights).sum() / weights.sum()
        return mse_loss

    def ssim_loss(self, decoder_output, target, bias=6.0):
        # decoder_output : B x T x n_mel
        # target : B x T x n_mel
        assert decoder_output.shape == target.shape
        weights = weights_nonzero_speech(target)
        decoder_output = decoder_output[:, None] + bias
        target = target[:, None] + bias
        ssim_loss = 1 - ssim(decoder_output, target, size_average=False)
        ssim_loss = (ssim_loss * weights).sum() / weights.sum()
        return ssim_loss

    def plot_mel(self, batch_idx, spec_out, spec_gt=None, name=None, title='', f0s=None, dur_info=None):
        vmin = hparams['mel_vmin']
        vmax = hparams['mel_vmax']
        if len(spec_out.shape) == 3:
            spec_out = spec_out[0]
        if isinstance(spec_out, torch.Tensor):
            spec_out = spec_out.cpu().numpy()
        if spec_gt is not None:
            if len(spec_gt.shape) == 3:
                spec_gt = spec_gt[0]
            if isinstance(spec_gt, torch.Tensor):
                spec_gt = spec_gt.cpu().numpy()
            max_len = max(len(spec_gt), len(spec_out))
            if max_len - len(spec_gt) > 0:
                spec_gt = np.pad(spec_gt, [[0, max_len - len(spec_gt)], [0, 0]], mode='constant',
                                 constant_values=vmin)
            if max_len - len(spec_out) > 0:
                spec_out = np.pad(spec_out, [[0, max_len - len(spec_out)], [0, 0]], mode='constant',
                                  constant_values=vmin)
            spec_out = np.concatenate([spec_out, spec_gt], -1)
        name = f'mel_val_{batch_idx}' if name is None else name
        self.logger.add_figure(name, spec_to_figure(
            spec_out, vmin, vmax, title=title, f0s=f0s, dur_info=dur_info), self.global_step)

    ##########################
    # testing
    ##########################
    def test_start(self):
        self.saving_result_pool = MultiprocessManager(int(os.getenv('N_PROC', os.cpu_count())))
        self.saving_results_futures = []
        self.gen_dir = os.path.join(
            hparams['work_dir'], f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}')
        self.vocoder: BaseVocoder = get_vocoder_cls(hparams['vocoder'])()
        os.makedirs(self.gen_dir, exist_ok=True)
        os.makedirs(f'{self.gen_dir}/wavs', exist_ok=True)
        os.makedirs(f'{self.gen_dir}/plot', exist_ok=True)
        if hparams.get('save_mel_npy', False):
            os.makedirs(f'{self.gen_dir}/mel_npy', exist_ok=True)

    def test_step(self, sample, batch_idx):
        """

        :param sample:
        :param batch_idx:
        :return:
        """
        assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference'
        outputs = self.run_model(sample, infer=True)
        text = sample['text'][0]
        item_name = sample['item_name'][0]
        tokens = sample['txt_tokens'][0].cpu().numpy()
        mel_gt = sample['mels'][0].cpu().numpy()
        mel_pred = outputs['mel_out'][0].cpu().numpy()
        str_phs = self.token_encoder.decode(tokens, strip_padding=True)
        base_fn = f'[{self.results_id:06d}][{item_name.replace("%", "_")}][%s]'
        if text is not None:
            base_fn += text.replace(":", "$3A")[:80]
        base_fn = base_fn.replace(' ', '_')
        gen_dir = self.gen_dir
        wav_pred = self.vocoder.spec2wav(mel_pred)
        self.saving_result_pool.add_job(self.save_result, args=[
            wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs])
        if hparams['save_gt']:
            wav_gt = self.vocoder.spec2wav(mel_gt)
            self.saving_result_pool.add_job(self.save_result, args=[
                wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs])
        print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}")
        return {
            'item_name': item_name,
            'text': text,
            'ph_tokens': self.token_encoder.decode(tokens.tolist()),
            'wav_fn_pred': base_fn % 'P',
            'wav_fn_gt': base_fn % 'G',
        }

    @staticmethod
    def save_result(wav_out, mel, base_fn, gen_dir, str_phs=None, mel2ph=None, alignment=None):
        save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'],
                 norm=hparams['out_wav_norm'])
        fig = plt.figure(figsize=(14, 10))
        spec_vmin = hparams['mel_vmin']
        spec_vmax = hparams['mel_vmax']
        heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax)
        fig.colorbar(heatmap)
        try:
            f0 = extract_pitch_simple(wav_out)
            f0 = f0 / 10 * (f0 > 0)
            plt.plot(f0, c='white', linewidth=1, alpha=0.6)
            if mel2ph is not None and str_phs is not None:
                decoded_txt = str_phs.split(" ")
                dur = mel2token_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy()
                dur = [0] + list(np.cumsum(dur))
                for i in range(len(dur) - 1):
                    shift = (i % 20) + 1
                    plt.text(dur[i], shift, decoded_txt[i])
                    plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black')
                    plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black',
                               alpha=1, linewidth=1)
            plt.tight_layout()
            plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png')
            plt.close(fig)
            if hparams.get('save_mel_npy', False):
                np.save(f'{gen_dir}/mel_npy/{base_fn}', mel)
            if alignment is not None:
                fig, ax = plt.subplots(figsize=(12, 16))
                im = ax.imshow(alignment, aspect='auto', origin='lower',
                               interpolation='none')
                decoded_txt = str_phs.split(" ")
                ax.set_yticks(np.arange(len(decoded_txt)))
                ax.set_yticklabels(list(decoded_txt), fontsize=6)
                fig.colorbar(im, ax=ax)
                fig.savefig(f'{gen_dir}/attn_plot/{base_fn}_attn.png', format='png')
                plt.close(fig)
        except Exception:
            traceback.print_exc()
        return None

    def test_end(self, outputs):
        pd.DataFrame(outputs).to_csv(f'{self.gen_dir}/meta.csv')
        for _1, _2 in tqdm(self.saving_result_pool.get_results(), total=len(self.saving_result_pool)):
            pass
        return {}