import os import time import librosa.display as lbd import matplotlib.pyplot as plt import torch import torch.multiprocessing from torch.cuda.amp import GradScaler from torch.cuda.amp import autocast from torch.nn.utils.rnn import pad_sequence from torch.utils.data.dataloader import DataLoader from tqdm import tqdm import numpy as np from Preprocessing.TextFrontend import ArticulatoryCombinedTextFrontend from Preprocessing.TextFrontend import get_language_id from Utility.WarmupScheduler import WarmupScheduler from Utility.utils import cumsum_durations from Utility.utils import delete_old_checkpoints from Utility.utils import get_most_recent_checkpoint from TrainingInterfaces.Text_to_Spectrogram.FastSpeech2.logger import FastSpeech2Logger #Added for logging from Preprocessing.Language_embedding import LanguageEmbedding #This function creates the logging directory and the returns the logger. def prepare_logger(log_directory): if not os.path.isdir(log_directory): os.makedirs(log_directory) os.chmod(log_directory, 0o775) logger = FastSpeech2Logger( log_directory) return logger @torch.no_grad() def plot_progress_spec(net, device, save_dir, step, lang, default_emb): tf = ArticulatoryCombinedTextFrontend(language=lang) sentence = "" if lang == "en": sentence = "This is a complex sentence, it even has a pause!" elif lang == "de": sentence = "Dies ist ein komplexer Satz, er hat sogar eine Pause!" elif lang == "at": sentence = "Dies ist ein komplexer Satz, er hat sogar eine Pause!" elif lang == "vd": sentence = "Dies ist ein komplexer Satz, er hat sogar eine Pause!" elif lang == "at-lab": sentence = "Dies ist ein komplexer Satz, er hat sogar eine Pause!" elif lang == "el": sentence = "Αυτή είναι μια σύνθετη πρόταση, έχει ακόμη και παύση!" elif lang == "es": sentence = "Esta es una oración compleja, ¡incluso tiene una pausa!" elif lang == "fi": sentence = "Tämä on monimutkainen lause, sillä on jopa tauko!" elif lang == "ru": sentence = "Это сложное предложение, в нем даже есть пауза!" elif lang == "hu": sentence = "Ez egy összetett mondat, még szünet is van benne!" elif lang == "nl": sentence = "Dit is een complexe zin, er zit zelfs een pauze in!" elif lang == "fr": sentence = "C'est une phrase complexe, elle a même une pause !" elif lang == "pt": sentence = "Esta é uma frase complexa, tem até uma pausa!" elif lang == "pl": sentence = "To jest zdanie złożone, ma nawet pauzę!" elif lang == "it": sentence = "Questa è una frase complessa, ha anche una pausa!" elif lang == "cmn": sentence = "这是一个复杂的句子，它甚至包含一个停顿。" elif lang == "vi": sentence = "Đây là một câu phức tạp, nó thậm chí còn chứa một khoảng dừng." if lang == "at-lab": sentence == "Heute ist schönes Frühlingswetter!" phoneme_vector = tf.string_to_tensor(sentence, path_to_wavfile="/data/vokquant/data/aridialect/aridialect_wav16000/alf_at_berlin_001.wav").squeeze(0).to(device) else: phoneme_vector = tf.string_to_tensor(sentence).squeeze(0).to(device) print("inference start:") emb = LanguageEmbedding() spec, durations, *_ = net.inference(text=phoneme_vector, return_duration_pitch_energy=True, utterance_embedding=default_emb, lang_emb=emb.get_emb_from_path(path_to_wavfile="/data/vokquant/data/aridialect/aridialect_wav16000/alf_at_berlin_001.wav" ).to(device)) spec = spec.transpose(0, 1).to("cpu").numpy() duration_splits, label_positions = cumsum_durations(durations.cpu().numpy()) if not os.path.exists(os.path.join(save_dir, "spec")): os.makedirs(os.path.join(save_dir, "spec")) fig, ax = plt.subplots(nrows=1, ncols=1) lbd.specshow(spec, ax=ax, sr=16000, cmap='GnBu', y_axis='mel', x_axis=None, hop_length=256) ax.yaxis.set_visible(False) ax.set_xticks(duration_splits, minor=True) ax.xaxis.grid(True, which='minor') ax.set_xticks(label_positions, minor=False) ax.set_xticklabels(tf.get_phone_string(sentence, for_plot_labels=True, path_to_wavfile="/data/vokquant/data/aridialect/aridialect_wav16000/alf_at_berlin_001.wav")) ax.set_title(sentence) plt.savefig(os.path.join(os.path.join(save_dir, "spec"), str(step) + ".png")) plt.clf() plt.close() print("inference done") def collate_and_pad(batch): # text, text_len, speech, speech_len, durations, energy, pitch, utterance condition,language_embedding, language_id if type([datapoint[8] for datapoint in batch][0]) == np.ndarray: lang_emb = [torch.from_numpy(datapoint[8]) for datapoint in batch] else: lang_emb = [datapoint[8] for datapoint in batch] return (pad_sequence([datapoint[0] for datapoint in batch], batch_first=True), torch.stack([datapoint[1] for datapoint in batch]).squeeze(1), pad_sequence([datapoint[2] for datapoint in batch], batch_first=True), torch.stack([datapoint[3] for datapoint in batch]).squeeze(1), pad_sequence([datapoint[4] for datapoint in batch], batch_first=True), pad_sequence([datapoint[5] for datapoint in batch], batch_first=True), pad_sequence([datapoint[6] for datapoint in batch], batch_first=True), torch.stack([datapoint[7] for datapoint in batch]).squeeze(), torch.stack(lang_emb).squeeze(), torch.stack([datapoint[9] for datapoint in batch])) def train_loop(net, train_dataset, device, save_directory, batch_size=32, steps=300000, epochs_per_save=1, lang="en", lr=0.0001, warmup_steps=4000, path_to_checkpoint=None, fine_tune=False, resume=False): """ Args: resume: whether to resume from the most recent checkpoint warmup_steps: how long the learning rate should increase before it reaches the specified value steps: How many steps to train lr: The initial learning rate for the optimiser path_to_checkpoint: reloads a checkpoint to continue training from there fine_tune: whether to load everything from a checkpoint, or only the model parameters lang: language of the synthesis net: Model to train train_dataset: Pytorch Dataset Object for train data device: Device to put the loaded tensors on save_directory: Where to save the checkpoints batch_size: How many elements should be loaded at once epochs_per_save: how many epochs to train in between checkpoints """ net = net.to(device) logger = prepare_logger(log_directory=os.path.join(save_directory,'logs')) #Create the logger and the logging dir torch.multiprocessing.set_sharing_strategy('file_system') train_loader = DataLoader(batch_size=batch_size, dataset=train_dataset, drop_last=True, num_workers=8, pin_memory=True, shuffle=True, prefetch_factor=8, collate_fn=collate_and_pad, persistent_workers=True) default_embedding = None for index in range(20): # slicing is not implemented for datasets, so this detour is needed. if default_embedding is None: default_embedding = train_dataset[index][7].squeeze() else: default_embedding = default_embedding + train_dataset[index][7].squeeze() default_embedding = (default_embedding / len(train_dataset)).to(device) # default speaker embedding for inference is the average of the first 20 speaker embeddings. So if you use multiple datasets combined, # put a single speaker one with the nicest voice first into the concat dataset. step_counter = 0 optimizer = torch.optim.Adam(net.parameters(), lr=lr) scheduler = WarmupScheduler(optimizer, warmup_steps=warmup_steps) scaler = GradScaler() epoch = 0 if resume: path_to_checkpoint = get_most_recent_checkpoint(checkpoint_dir=save_directory) if path_to_checkpoint is not None: check_dict = torch.load(path_to_checkpoint, map_location=device) net.load_state_dict(check_dict["model"]) if not fine_tune: optimizer.load_state_dict(check_dict["optimizer"]) scheduler.load_state_dict(check_dict["scheduler"]) step_counter = check_dict["step_counter"] scaler.load_state_dict(check_dict["scaler"]) start_time = time.time() while True: net.train() epoch += 1 optimizer.zero_grad() train_losses_this_epoch = list() for batch in tqdm(train_loader): with autocast(): # print("text_lengths=batch[1]", str(batch[1])) prints the text lengths which is the dimension used for encoder (max value per bath is used, and most probably everything is zero padded) train_loss = net(text_tensors=batch[0].to(device), text_lengths=batch[1].to(device), gold_speech=batch[2].to(device), speech_lengths=batch[3].to(device), gold_durations=batch[4].to(device), gold_pitch=batch[6].to(device), # mind the switched order gold_energy=batch[5].to(device), # mind the switched order utterance_embedding=batch[7].to(device), lang_embs=batch[8].to(device), return_mels=False) train_losses_this_epoch.append(train_loss.item()) optimizer.zero_grad() scaler.scale(train_loss).backward() del train_loss step_counter += 1 scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(net.parameters(), 1.0, error_if_nonfinite=False) scaler.step(optimizer) scaler.update() scheduler.step() net.eval() if epoch % epochs_per_save == 0: torch.save({ "model": net.state_dict(), "optimizer": optimizer.state_dict(), "step_counter": step_counter, "scaler": scaler.state_dict(), "scheduler": scheduler.state_dict(), "default_emb": default_embedding, }, os.path.join(save_directory, "checkpoint_{}.pt".format(step_counter))) delete_old_checkpoints(save_directory, keep=5) plot_progress_spec(net, device, save_dir=save_directory, step=step_counter, lang=lang, default_emb=default_embedding) if step_counter > steps: # DONE return print("Epoch: {}".format(epoch)) print("Train Loss: {}".format(sum(train_losses_this_epoch) / len(train_losses_this_epoch))) print("Time elapsed: {} Minutes".format(round((time.time() - start_time) / 60))) print("Steps: {}".format(step_counter)) logger.log_training(sum(train_losses_this_epoch) / len(train_losses_this_epoch),step_counter) #We add the loss of the specific step to the log net.train()