import torch import torch.nn.functional as F from torch import optim from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter from synthesizer import audio from synthesizer.models.tacotron import Tacotron from synthesizer.synthesizer_dataset import SynthesizerDataset, collate_synthesizer from synthesizer.utils import ValueWindow, data_parallel_workaround from synthesizer.utils.plot import plot_spectrogram, plot_spectrogram_and_trace from synthesizer.utils.symbols import symbols from synthesizer.utils.text import sequence_to_text from vocoder.display import * from datetime import datetime import json import numpy as np from pathlib import Path import time import os def np_now(x: torch.Tensor): return x.detach().cpu().numpy() def time_string(): return datetime.now().strftime("%Y-%m-%d %H:%M") def train(run_id: str, syn_dir: str, models_dir: str, save_every: int, backup_every: int, log_every:int, force_restart:bool, hparams): syn_dir = Path(syn_dir) models_dir = Path(models_dir) models_dir.mkdir(exist_ok=True) model_dir = models_dir.joinpath(run_id) plot_dir = model_dir.joinpath("plots") wav_dir = model_dir.joinpath("wavs") mel_output_dir = model_dir.joinpath("mel-spectrograms") meta_folder = model_dir.joinpath("metas") model_dir.mkdir(exist_ok=True) plot_dir.mkdir(exist_ok=True) wav_dir.mkdir(exist_ok=True) mel_output_dir.mkdir(exist_ok=True) meta_folder.mkdir(exist_ok=True) weights_fpath = model_dir.joinpath(run_id).with_suffix(".pt") metadata_fpath = syn_dir.joinpath("train.txt") print("Checkpoint path: {}".format(weights_fpath)) print("Loading training data from: {}".format(metadata_fpath)) print("Using model: Tacotron") # Book keeping step = 0 time_window = ValueWindow(100) loss_window = ValueWindow(100) # From WaveRNN/train_tacotron.py if torch.cuda.is_available(): device = torch.device("cuda") for session in hparams.tts_schedule: _, _, _, batch_size = session if batch_size % torch.cuda.device_count() != 0: raise ValueError("`batch_size` must be evenly divisible by n_gpus!") else: device = torch.device("cpu") print("Using device:", device) # Instantiate Tacotron Model print("\nInitialising Tacotron Model...\n") num_chars = len(symbols) if weights_fpath.exists(): # for compatibility purpose, change symbols accordingly: loaded_shape = torch.load(str(weights_fpath), map_location=device)["model_state"]["encoder.embedding.weight"].shape if num_chars != loaded_shape[0]: print("WARNING: you are using compatible mode due to wrong sympols length, please modify varible _characters in `utils\symbols.py`") num_chars != loaded_shape[0] # Try to scan config file model_config_fpaths = list(weights_fpath.parent.rglob("*.json")) if len(model_config_fpaths)>0 and model_config_fpaths[0].exists(): with model_config_fpaths[0].open("r", encoding="utf-8") as f: hparams.loadJson(json.load(f)) else: # save a config hparams.dumpJson(weights_fpath.parent.joinpath(run_id).with_suffix(".json")) model = Tacotron(embed_dims=hparams.tts_embed_dims, num_chars=num_chars, encoder_dims=hparams.tts_encoder_dims, decoder_dims=hparams.tts_decoder_dims, n_mels=hparams.num_mels, fft_bins=hparams.num_mels, postnet_dims=hparams.tts_postnet_dims, encoder_K=hparams.tts_encoder_K, lstm_dims=hparams.tts_lstm_dims, postnet_K=hparams.tts_postnet_K, num_highways=hparams.tts_num_highways, dropout=hparams.tts_dropout, stop_threshold=hparams.tts_stop_threshold, speaker_embedding_size=hparams.speaker_embedding_size).to(device) # Initialize the optimizer optimizer = optim.Adam(model.parameters(), amsgrad=True) # Load the weights if force_restart or not weights_fpath.exists(): print("\nStarting the training of Tacotron from scratch\n") model.save(weights_fpath) # Embeddings metadata char_embedding_fpath = meta_folder.joinpath("CharacterEmbeddings.tsv") with open(char_embedding_fpath, "w", encoding="utf-8") as f: for symbol in symbols: if symbol == " ": symbol = "\\s" # For visual purposes, swap space with \s f.write("{}\n".format(symbol)) else: print("\nLoading weights at %s" % weights_fpath) model.load(weights_fpath, device, optimizer) print("Tacotron weights loaded from step %d" % model.step) # Initialize the dataset metadata_fpath = syn_dir.joinpath("train.txt") mel_dir = syn_dir.joinpath("mels") embed_dir = syn_dir.joinpath("embeds") dataset = SynthesizerDataset(metadata_fpath, mel_dir, embed_dir, hparams) test_loader = DataLoader(dataset, batch_size=1, shuffle=True, pin_memory=True) # tracing training step sw = SummaryWriter(log_dir=model_dir.joinpath("logs")) for i, session in enumerate(hparams.tts_schedule): current_step = model.get_step() r, lr, max_step, batch_size = session training_steps = max_step - current_step # Do we need to change to the next session? if current_step >= max_step: # Are there no further sessions than the current one? if i == len(hparams.tts_schedule) - 1: # We have completed training. Save the model and exit model.save(weights_fpath, optimizer) break else: # There is a following session, go to it continue model.r = r # Begin the training simple_table([(f"Steps with r={r}", str(training_steps // 1000) + "k Steps"), ("Batch Size", batch_size), ("Learning Rate", lr), ("Outputs/Step (r)", model.r)]) for p in optimizer.param_groups: p["lr"] = lr if hparams.tts_finetune_layers is not None and len(hparams.tts_finetune_layers) > 0: model.finetune_partial(hparams.tts_finetune_layers) data_loader = DataLoader(dataset, collate_fn=collate_synthesizer, batch_size=batch_size, #change if you got graphic card OOM num_workers=2, shuffle=True, pin_memory=True) total_iters = len(dataset) steps_per_epoch = np.ceil(total_iters / batch_size).astype(np.int32) epochs = np.ceil(training_steps / steps_per_epoch).astype(np.int32) for epoch in range(1, epochs+1): for i, (texts, mels, embeds, idx) in enumerate(data_loader, 1): start_time = time.time() # Generate stop tokens for training stop = torch.ones(mels.shape[0], mels.shape[2]) for j, k in enumerate(idx): stop[j, :int(dataset.metadata[k][4])-1] = 0 texts = texts.to(device) mels = mels.to(device) embeds = embeds.to(device) stop = stop.to(device) # Forward pass # Parallelize model onto GPUS using workaround due to python bug if device.type == "cuda" and torch.cuda.device_count() > 1: m1_hat, m2_hat, attention, stop_pred = data_parallel_workaround(model, texts, mels, embeds) else: m1_hat, m2_hat, attention, stop_pred = model(texts, mels, embeds) # Backward pass m1_loss = F.mse_loss(m1_hat, mels) + F.l1_loss(m1_hat, mels) m2_loss = F.mse_loss(m2_hat, mels) stop_loss = F.binary_cross_entropy(stop_pred, stop) loss = m1_loss + m2_loss + stop_loss optimizer.zero_grad() loss.backward() if hparams.tts_clip_grad_norm is not None: grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), hparams.tts_clip_grad_norm) if np.isnan(grad_norm.cpu()): print("grad_norm was NaN!") optimizer.step() time_window.append(time.time() - start_time) loss_window.append(loss.item()) step = model.get_step() k = step // 1000 msg = f"| Epoch: {epoch}/{epochs} ({i}/{steps_per_epoch}) | Loss: {loss_window.average:#.4} | {1./time_window.average:#.2} steps/s | Step: {k}k | " stream(msg) if log_every != 0 and step % log_every == 0 : sw.add_scalar("training/loss", loss_window.average, step) # Backup or save model as appropriate if backup_every != 0 and step % backup_every == 0 : backup_fpath = Path("{}/{}_{}.pt".format(str(weights_fpath.parent), run_id, step)) model.save(backup_fpath, optimizer) if save_every != 0 and step % save_every == 0 : # Must save latest optimizer state to ensure that resuming training # doesn't produce artifacts model.save(weights_fpath, optimizer) # Evaluate model to generate samples epoch_eval = hparams.tts_eval_interval == -1 and i == steps_per_epoch # If epoch is done step_eval = hparams.tts_eval_interval > 0 and step % hparams.tts_eval_interval == 0 # Every N steps if epoch_eval or step_eval: for sample_idx in range(hparams.tts_eval_num_samples): # At most, generate samples equal to number in the batch if sample_idx + 1 <= len(texts): # Remove padding from mels using frame length in metadata mel_length = int(dataset.metadata[idx[sample_idx]][4]) mel_prediction = np_now(m2_hat[sample_idx]).T[:mel_length] target_spectrogram = np_now(mels[sample_idx]).T[:mel_length] attention_len = mel_length // model.r # eval_loss = F.mse_loss(mel_prediction, target_spectrogram) # sw.add_scalar("validing/loss", eval_loss.item(), step) eval_model(attention=np_now(attention[sample_idx][:, :attention_len]), mel_prediction=mel_prediction, target_spectrogram=target_spectrogram, input_seq=np_now(texts[sample_idx]), step=step, plot_dir=plot_dir, mel_output_dir=mel_output_dir, wav_dir=wav_dir, sample_num=sample_idx + 1, loss=loss, hparams=hparams, sw=sw) MAX_SAVED_COUNT = 20 if (step / hparams.tts_eval_interval) % MAX_SAVED_COUNT == 0: # clean up and save last MAX_SAVED_COUNT; plots = next(os.walk(plot_dir), (None, None, []))[2] for plot in plots[-MAX_SAVED_COUNT:]: os.remove(plot_dir.joinpath(plot)) mel_files = next(os.walk(mel_output_dir), (None, None, []))[2] for mel_file in mel_files[-MAX_SAVED_COUNT:]: os.remove(mel_output_dir.joinpath(mel_file)) wavs = next(os.walk(wav_dir), (None, None, []))[2] for w in wavs[-MAX_SAVED_COUNT:]: os.remove(wav_dir.joinpath(w)) # Break out of loop to update training schedule if step >= max_step: break # Add line break after every epoch print("") def eval_model(attention, mel_prediction, target_spectrogram, input_seq, step, plot_dir, mel_output_dir, wav_dir, sample_num, loss, hparams, sw): # Save some results for evaluation attention_path = str(plot_dir.joinpath("attention_step_{}_sample_{}".format(step, sample_num))) # save_attention(attention, attention_path) save_and_trace_attention(attention, attention_path, sw, step) # save predicted mel spectrogram to disk (debug) mel_output_fpath = mel_output_dir.joinpath("mel-prediction-step-{}_sample_{}.npy".format(step, sample_num)) np.save(str(mel_output_fpath), mel_prediction, allow_pickle=False) # save griffin lim inverted wav for debug (mel -> wav) wav = audio.inv_mel_spectrogram(mel_prediction.T, hparams) wav_fpath = wav_dir.joinpath("step-{}-wave-from-mel_sample_{}.wav".format(step, sample_num)) audio.save_wav(wav, str(wav_fpath), sr=hparams.sample_rate) # save real and predicted mel-spectrogram plot to disk (control purposes) spec_fpath = plot_dir.joinpath("step-{}-mel-spectrogram_sample_{}.png".format(step, sample_num)) title_str = "{}, {}, step={}, loss={:.5f}".format("Tacotron", time_string(), step, loss) # plot_spectrogram(mel_prediction, str(spec_fpath), title=title_str, # target_spectrogram=target_spectrogram, # max_len=target_spectrogram.size // hparams.num_mels) plot_spectrogram_and_trace( mel_prediction, str(spec_fpath), title=title_str, target_spectrogram=target_spectrogram, max_len=target_spectrogram.size // hparams.num_mels, sw=sw, step=step) print("Input at step {}: {}".format(step, sequence_to_text(input_seq)))