mirror of https://github.com/coqui-ai/TTS.git
579 lines
22 KiB
Python
579 lines
22 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import os
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import sys
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import time
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import traceback
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from random import randrange
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import numpy as np
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import torch
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# DISTRIBUTED
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from torch.nn.parallel import DistributedDataParallel as DDP_th
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from torch.utils.data import DataLoader
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from torch.utils.data.distributed import DistributedSampler
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from TTS.tts.datasets.preprocess import load_meta_data
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from TTS.tts.datasets.TTSDataset import MyDataset
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from TTS.tts.layers.losses import SpeedySpeechLoss
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from TTS.tts.utils.generic_utils import setup_model
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from TTS.tts.utils.io import save_best_model, save_checkpoint
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from TTS.tts.utils.measures import alignment_diagonal_score
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from TTS.tts.utils.speakers import parse_speakers
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from TTS.tts.utils.synthesis import synthesis
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from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
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from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
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from TTS.utils.arguments import init_training
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.distribute import init_distributed, reduce_tensor
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from TTS.utils.generic_utils import KeepAverage, count_parameters, remove_experiment_folder, set_init_dict
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from TTS.utils.radam import RAdam
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from TTS.utils.training import NoamLR, setup_torch_training_env
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use_cuda, num_gpus = setup_torch_training_env(True, False)
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def setup_loader(ap, r, is_val=False, verbose=False):
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if is_val and not config.run_eval:
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loader = None
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else:
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dataset = MyDataset(
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r,
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config.text_cleaner,
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compute_linear_spec=False,
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meta_data=meta_data_eval if is_val else meta_data_train,
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ap=ap,
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tp=config.characters,
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add_blank=config["add_blank"],
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batch_group_size=0 if is_val else config.batch_group_size * config.batch_size,
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min_seq_len=config.min_seq_len,
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max_seq_len=config.max_seq_len,
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phoneme_cache_path=config.phoneme_cache_path,
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use_phonemes=config.use_phonemes,
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phoneme_language=config.phoneme_language,
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enable_eos_bos=config.enable_eos_bos_chars,
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use_noise_augment=not is_val,
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verbose=verbose,
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speaker_mapping=speaker_mapping
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if config.use_speaker_embedding and config.use_external_speaker_embedding_file
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else None,
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)
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if config.use_phonemes and config.compute_input_seq_cache:
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# precompute phonemes to have a better estimate of sequence lengths.
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dataset.compute_input_seq(config.num_loader_workers)
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dataset.sort_items()
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sampler = DistributedSampler(dataset) if num_gpus > 1 else None
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loader = DataLoader(
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dataset,
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batch_size=config.eval_batch_size if is_val else config.batch_size,
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shuffle=False,
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collate_fn=dataset.collate_fn,
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drop_last=False,
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sampler=sampler,
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num_workers=config.num_val_loader_workers if is_val else config.num_loader_workers,
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pin_memory=False,
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)
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return loader
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def format_data(data):
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# setup input data
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text_input = data[0]
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text_lengths = data[1]
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speaker_names = data[2]
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mel_input = data[4].permute(0, 2, 1) # B x D x T
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mel_lengths = data[5]
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item_idx = data[7]
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attn_mask = data[9]
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avg_text_length = torch.mean(text_lengths.float())
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avg_spec_length = torch.mean(mel_lengths.float())
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if config.use_speaker_embedding:
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if config.use_external_speaker_embedding_file:
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# return precomputed embedding vector
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speaker_c = data[8]
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else:
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# return speaker_id to be used by an embedding layer
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speaker_c = [speaker_mapping[speaker_name] for speaker_name in speaker_names]
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speaker_c = torch.LongTensor(speaker_c)
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else:
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speaker_c = None
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# compute durations from attention mask
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durations = torch.zeros(attn_mask.shape[0], attn_mask.shape[2])
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for idx, am in enumerate(attn_mask):
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# compute raw durations
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c_idxs = am[:, : text_lengths[idx], : mel_lengths[idx]].max(1)[1]
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# c_idxs, counts = torch.unique_consecutive(c_idxs, return_counts=True)
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c_idxs, counts = torch.unique(c_idxs, return_counts=True)
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dur = torch.ones([text_lengths[idx]]).to(counts.dtype)
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dur[c_idxs] = counts
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# smooth the durations and set any 0 duration to 1
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# by cutting off from the largest duration indeces.
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extra_frames = dur.sum() - mel_lengths[idx]
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largest_idxs = torch.argsort(-dur)[:extra_frames]
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dur[largest_idxs] -= 1
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assert (
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dur.sum() == mel_lengths[idx]
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), f" [!] total duration {dur.sum()} vs spectrogram length {mel_lengths[idx]}"
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durations[idx, : text_lengths[idx]] = dur
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# dispatch data to GPU
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if use_cuda:
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text_input = text_input.cuda(non_blocking=True)
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text_lengths = text_lengths.cuda(non_blocking=True)
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mel_input = mel_input.cuda(non_blocking=True)
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mel_lengths = mel_lengths.cuda(non_blocking=True)
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if speaker_c is not None:
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speaker_c = speaker_c.cuda(non_blocking=True)
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attn_mask = attn_mask.cuda(non_blocking=True)
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durations = durations.cuda(non_blocking=True)
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return (
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text_input,
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text_lengths,
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mel_input,
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mel_lengths,
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speaker_c,
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avg_text_length,
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avg_spec_length,
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attn_mask,
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durations,
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item_idx,
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)
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def train(data_loader, model, criterion, optimizer, scheduler, ap, global_step, epoch):
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model.train()
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epoch_time = 0
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keep_avg = KeepAverage()
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if use_cuda:
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batch_n_iter = int(len(data_loader.dataset) / (config.batch_size * num_gpus))
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else:
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batch_n_iter = int(len(data_loader.dataset) / config.batch_size)
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end_time = time.time()
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c_logger.print_train_start()
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scaler = torch.cuda.amp.GradScaler() if config.mixed_precision else None
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for num_iter, data in enumerate(data_loader):
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start_time = time.time()
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# format data
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(
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text_input,
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text_lengths,
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mel_targets,
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mel_lengths,
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speaker_c,
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avg_text_length,
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avg_spec_length,
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_,
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dur_target,
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_,
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) = format_data(data)
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loader_time = time.time() - end_time
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global_step += 1
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optimizer.zero_grad()
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# forward pass model
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with torch.cuda.amp.autocast(enabled=config.mixed_precision):
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decoder_output, dur_output, alignments = model.forward(
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text_input, text_lengths, mel_lengths, dur_target, g=speaker_c
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)
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# compute loss
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loss_dict = criterion(
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decoder_output, mel_targets, mel_lengths, dur_output, torch.log(1 + dur_target), text_lengths
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)
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# backward pass with loss scaling
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if config.mixed_precision:
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scaler.scale(loss_dict["loss"]).backward()
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scaler.unscale_(optimizer)
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grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)
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scaler.step(optimizer)
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scaler.update()
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else:
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loss_dict["loss"].backward()
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grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)
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optimizer.step()
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# setup lr
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if config.noam_schedule:
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scheduler.step()
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# current_lr
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current_lr = optimizer.param_groups[0]["lr"]
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# compute alignment error (the lower the better )
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align_error = 1 - alignment_diagonal_score(alignments, binary=True)
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loss_dict["align_error"] = align_error
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step_time = time.time() - start_time
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epoch_time += step_time
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# aggregate losses from processes
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if num_gpus > 1:
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loss_dict["loss_l1"] = reduce_tensor(loss_dict["loss_l1"].data, num_gpus)
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loss_dict["loss_ssim"] = reduce_tensor(loss_dict["loss_ssim"].data, num_gpus)
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loss_dict["loss_dur"] = reduce_tensor(loss_dict["loss_dur"].data, num_gpus)
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loss_dict["loss"] = reduce_tensor(loss_dict["loss"].data, num_gpus)
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# detach loss values
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loss_dict_new = dict()
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for key, value in loss_dict.items():
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if isinstance(value, (int, float)):
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loss_dict_new[key] = value
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else:
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loss_dict_new[key] = value.item()
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loss_dict = loss_dict_new
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# update avg stats
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update_train_values = dict()
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for key, value in loss_dict.items():
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update_train_values["avg_" + key] = value
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update_train_values["avg_loader_time"] = loader_time
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update_train_values["avg_step_time"] = step_time
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keep_avg.update_values(update_train_values)
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# print training progress
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if global_step % config.print_step == 0:
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log_dict = {
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"avg_spec_length": [avg_spec_length, 1], # value, precision
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"avg_text_length": [avg_text_length, 1],
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"step_time": [step_time, 4],
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"loader_time": [loader_time, 2],
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"current_lr": current_lr,
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}
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c_logger.print_train_step(batch_n_iter, num_iter, global_step, log_dict, loss_dict, keep_avg.avg_values)
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if args.rank == 0:
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# Plot Training Iter Stats
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# reduce TB load
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if global_step % config.tb_plot_step == 0:
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iter_stats = {"lr": current_lr, "grad_norm": grad_norm, "step_time": step_time}
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iter_stats.update(loss_dict)
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tb_logger.tb_train_iter_stats(global_step, iter_stats)
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if global_step % config.save_step == 0:
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if config.checkpoint:
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# save model
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save_checkpoint(
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model,
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optimizer,
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global_step,
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epoch,
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1,
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OUT_PATH,
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model_characters,
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model_loss=loss_dict["loss"],
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)
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# wait all kernels to be completed
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torch.cuda.synchronize()
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# Diagnostic visualizations
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idx = np.random.randint(mel_targets.shape[0])
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pred_spec = decoder_output[idx].detach().data.cpu().numpy().T
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gt_spec = mel_targets[idx].data.cpu().numpy().T
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align_img = alignments[idx].data.cpu()
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figures = {
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"prediction": plot_spectrogram(pred_spec, ap),
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"ground_truth": plot_spectrogram(gt_spec, ap),
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"alignment": plot_alignment(align_img),
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}
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tb_logger.tb_train_figures(global_step, figures)
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# Sample audio
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train_audio = ap.inv_melspectrogram(pred_spec.T)
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tb_logger.tb_train_audios(global_step, {"TrainAudio": train_audio}, config.audio["sample_rate"])
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end_time = time.time()
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# print epoch stats
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c_logger.print_train_epoch_end(global_step, epoch, epoch_time, keep_avg)
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# Plot Epoch Stats
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if args.rank == 0:
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epoch_stats = {"epoch_time": epoch_time}
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epoch_stats.update(keep_avg.avg_values)
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tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
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if config.tb_model_param_stats:
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tb_logger.tb_model_weights(model, global_step)
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return keep_avg.avg_values, global_step
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@torch.no_grad()
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def evaluate(data_loader, model, criterion, ap, global_step, epoch):
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model.eval()
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epoch_time = 0
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keep_avg = KeepAverage()
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c_logger.print_eval_start()
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if data_loader is not None:
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for num_iter, data in enumerate(data_loader):
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start_time = time.time()
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# format data
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text_input, text_lengths, mel_targets, mel_lengths, speaker_c, _, _, _, dur_target, _ = format_data(data)
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# forward pass model
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with torch.cuda.amp.autocast(enabled=config.mixed_precision):
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decoder_output, dur_output, alignments = model.forward(
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text_input, text_lengths, mel_lengths, dur_target, g=speaker_c
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)
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# compute loss
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loss_dict = criterion(
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decoder_output, mel_targets, mel_lengths, dur_output, torch.log(1 + dur_target), text_lengths
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)
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# step time
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step_time = time.time() - start_time
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epoch_time += step_time
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# compute alignment score
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align_error = 1 - alignment_diagonal_score(alignments, binary=True)
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loss_dict["align_error"] = align_error
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# aggregate losses from processes
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if num_gpus > 1:
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loss_dict["loss_l1"] = reduce_tensor(loss_dict["loss_l1"].data, num_gpus)
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loss_dict["loss_ssim"] = reduce_tensor(loss_dict["loss_ssim"].data, num_gpus)
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loss_dict["loss_dur"] = reduce_tensor(loss_dict["loss_dur"].data, num_gpus)
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loss_dict["loss"] = reduce_tensor(loss_dict["loss"].data, num_gpus)
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# detach loss values
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loss_dict_new = dict()
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for key, value in loss_dict.items():
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if isinstance(value, (int, float)):
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loss_dict_new[key] = value
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else:
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loss_dict_new[key] = value.item()
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loss_dict = loss_dict_new
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# update avg stats
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update_train_values = dict()
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for key, value in loss_dict.items():
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update_train_values["avg_" + key] = value
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keep_avg.update_values(update_train_values)
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if config.print_eval:
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c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
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if args.rank == 0:
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# Diagnostic visualizations
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idx = np.random.randint(mel_targets.shape[0])
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pred_spec = decoder_output[idx].detach().data.cpu().numpy().T
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gt_spec = mel_targets[idx].data.cpu().numpy().T
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align_img = alignments[idx].data.cpu()
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eval_figures = {
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"prediction": plot_spectrogram(pred_spec, ap, output_fig=False),
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"ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False),
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"alignment": plot_alignment(align_img, output_fig=False),
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}
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# Sample audio
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eval_audio = ap.inv_melspectrogram(pred_spec.T)
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tb_logger.tb_eval_audios(global_step, {"ValAudio": eval_audio}, config.audio["sample_rate"])
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# Plot Validation Stats
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tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
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tb_logger.tb_eval_figures(global_step, eval_figures)
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if args.rank == 0 and epoch >= config.test_delay_epochs:
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if config.test_sentences_file:
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with open(config.test_sentences_file, "r") as f:
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test_sentences = [s.strip() for s in f.readlines()]
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else:
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test_sentences = [
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"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
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"Be a voice, not an echo.",
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"I'm sorry Dave. I'm afraid I can't do that.",
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"This cake is great. It's so delicious and moist.",
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"Prior to November 22, 1963.",
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]
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# test sentences
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test_audios = {}
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test_figures = {}
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print(" | > Synthesizing test sentences")
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if config.use_speaker_embedding:
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if config.use_external_speaker_embedding_file:
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speaker_embedding = speaker_mapping[list(speaker_mapping.keys())[randrange(len(speaker_mapping) - 1)]][
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"embedding"
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]
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speaker_id = None
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else:
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speaker_id = 0
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speaker_embedding = None
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else:
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speaker_id = None
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speaker_embedding = None
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for idx, test_sentence in enumerate(test_sentences):
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try:
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wav, alignment, _, postnet_output, _, _ = synthesis(
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model,
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test_sentence,
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config,
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use_cuda,
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ap,
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speaker_id=speaker_id,
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speaker_embedding=speaker_embedding,
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style_wav=None,
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truncated=False,
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enable_eos_bos_chars=config.enable_eos_bos_chars, # pylint: disable=unused-argument
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use_griffin_lim=True,
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do_trim_silence=False,
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)
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file_path = os.path.join(AUDIO_PATH, str(global_step))
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os.makedirs(file_path, exist_ok=True)
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file_path = os.path.join(file_path, "TestSentence_{}.wav".format(idx))
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ap.save_wav(wav, file_path)
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test_audios["{}-audio".format(idx)] = wav
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test_figures["{}-prediction".format(idx)] = plot_spectrogram(postnet_output, ap)
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test_figures["{}-alignment".format(idx)] = plot_alignment(alignment)
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except: # pylint: disable=bare-except
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print(" !! Error creating Test Sentence -", idx)
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traceback.print_exc()
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tb_logger.tb_test_audios(global_step, test_audios, config.audio["sample_rate"])
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tb_logger.tb_test_figures(global_step, test_figures)
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return keep_avg.avg_values
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# FIXME: move args definition/parsing inside of main?
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def main(args): # pylint: disable=redefined-outer-name
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# pylint: disable=global-variable-undefined
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global meta_data_train, meta_data_eval, symbols, phonemes, model_characters, speaker_mapping
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# Audio processor
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ap = AudioProcessor(**config.audio.to_dict())
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if config.characters is not None:
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symbols, phonemes = make_symbols(**config.characters.to_dict())
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# DISTRUBUTED
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if num_gpus > 1:
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init_distributed(args.rank, num_gpus, args.group_id, config.distributed["backend"], config.distributed["url"])
|
|
|
|
# set model characters
|
|
model_characters = phonemes if config.use_phonemes else symbols
|
|
num_chars = len(model_characters)
|
|
|
|
# load data instances
|
|
meta_data_train, meta_data_eval = load_meta_data(config.datasets, eval_split=True)
|
|
|
|
# set the portion of the data used for training if set in config.json
|
|
if config.has("train_portion"):
|
|
meta_data_train = meta_data_train[: int(len(meta_data_train) * config.train_portion)]
|
|
if config.has("eval_portion"):
|
|
meta_data_eval = meta_data_eval[: int(len(meta_data_eval) * config.eval_portion)]
|
|
|
|
# parse speakers
|
|
num_speakers, speaker_embedding_dim, speaker_mapping = parse_speakers(config, args, meta_data_train, OUT_PATH)
|
|
|
|
# setup model
|
|
model = setup_model(num_chars, num_speakers, config, speaker_embedding_dim=speaker_embedding_dim)
|
|
optimizer = RAdam(model.parameters(), lr=config.lr, weight_decay=0, betas=(0.9, 0.98), eps=1e-9)
|
|
criterion = SpeedySpeechLoss(config)
|
|
|
|
if args.restore_path:
|
|
print(f" > Restoring from {os.path.basename(args.restore_path)} ...")
|
|
checkpoint = torch.load(args.restore_path, map_location="cpu")
|
|
try:
|
|
# TODO: fix optimizer init, model.cuda() needs to be called before
|
|
# optimizer restore
|
|
optimizer.load_state_dict(checkpoint["optimizer"])
|
|
if config.reinit_layers:
|
|
raise RuntimeError
|
|
model.load_state_dict(checkpoint["model"])
|
|
except: # pylint: disable=bare-except
|
|
print(" > Partial model initialization.")
|
|
model_dict = model.state_dict()
|
|
model_dict = set_init_dict(model_dict, checkpoint["model"], config)
|
|
model.load_state_dict(model_dict)
|
|
del model_dict
|
|
|
|
for group in optimizer.param_groups:
|
|
group["initial_lr"] = config.lr
|
|
print(" > Model restored from step %d" % checkpoint["step"], flush=True)
|
|
args.restore_step = checkpoint["step"]
|
|
else:
|
|
args.restore_step = 0
|
|
|
|
if use_cuda:
|
|
model.cuda()
|
|
criterion.cuda()
|
|
|
|
# DISTRUBUTED
|
|
if num_gpus > 1:
|
|
model = DDP_th(model, device_ids=[args.rank])
|
|
|
|
if config.noam_schedule:
|
|
scheduler = NoamLR(optimizer, warmup_steps=config.warmup_steps, last_epoch=args.restore_step - 1)
|
|
else:
|
|
scheduler = None
|
|
|
|
num_params = count_parameters(model)
|
|
print("\n > Model has {} parameters".format(num_params), flush=True)
|
|
|
|
if args.restore_step == 0 or not args.best_path:
|
|
best_loss = float("inf")
|
|
print(" > Starting with inf best loss.")
|
|
else:
|
|
print(" > Restoring best loss from " f"{os.path.basename(args.best_path)} ...")
|
|
best_loss = torch.load(args.best_path, map_location="cpu")["model_loss"]
|
|
print(f" > Starting with loaded last best loss {best_loss}.")
|
|
keep_all_best = config.keep_all_best
|
|
keep_after = config.keep_after # void if keep_all_best False
|
|
|
|
# define dataloaders
|
|
train_loader = setup_loader(ap, 1, is_val=False, verbose=True)
|
|
eval_loader = setup_loader(ap, 1, is_val=True, verbose=True)
|
|
|
|
global_step = args.restore_step
|
|
for epoch in range(0, config.epochs):
|
|
c_logger.print_epoch_start(epoch, config.epochs)
|
|
train_avg_loss_dict, global_step = train(
|
|
train_loader, model, criterion, optimizer, scheduler, ap, global_step, epoch
|
|
)
|
|
eval_avg_loss_dict = evaluate(eval_loader, model, criterion, ap, global_step, epoch)
|
|
c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
|
|
target_loss = train_avg_loss_dict["avg_loss"]
|
|
if config.run_eval:
|
|
target_loss = eval_avg_loss_dict["avg_loss"]
|
|
best_loss = save_best_model(
|
|
target_loss,
|
|
best_loss,
|
|
model,
|
|
optimizer,
|
|
global_step,
|
|
epoch,
|
|
config.r,
|
|
OUT_PATH,
|
|
model_characters,
|
|
keep_all_best=keep_all_best,
|
|
keep_after=keep_after,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args, config, OUT_PATH, AUDIO_PATH, c_logger, tb_logger = init_training(sys.argv)
|
|
|
|
try:
|
|
main(args)
|
|
except KeyboardInterrupt:
|
|
remove_experiment_folder(OUT_PATH)
|
|
try:
|
|
sys.exit(0)
|
|
except SystemExit:
|
|
os._exit(0) # pylint: disable=protected-access
|
|
except Exception: # pylint: disable=broad-except
|
|
remove_experiment_folder(OUT_PATH)
|
|
traceback.print_exc()
|
|
sys.exit(1)
|