iteration += 1 if (batch_id + 1) % hp.train.log_interval == 0: mesg = "{0}\tEpoch:{1}[{2}/{3}],Iteration:{4}\tLoss:{5:.4f}\tTLoss:{6:.4f}\t\n".format(time.ctime(), e+1, batch_id+1, len(train_dataset)//hp.train.N, iteration,loss, total_loss / (batch_id + 1)) print(mesg) if hp.train.log_file is not None: with open(hp.train.log_file,'a') as f: f.write(mesg) if hp.train.checkpoint_dir is not None and (e + 1) % hp.train.checkpoint_interval == 0: embedder_net.eval().cpu() ckpt_model_filename = "ckpt_epoch_" + str(e+1) + "_batch_id_" + str(batch_id+1) + ".pth" ckpt_model_path = os.path.join(hp.train.checkpoint_dir, ckpt_model_filename) torch.save(embedder_net.state_dict(), ckpt_model_path) embedder_net.to(device).train() ###save model embedder_net.eval().cpu() save_model_filename = "final_epoch_" + str(e + 1) + "_batch_id_" + str(batch_id + 1) + ".model" save_model_path = os.path.join(hp.train.checkpoint_dir, save_model_filename) torch.save(embedder_net.state_dict(), save_model_path) print("\nDone, trained model saved at", save_model_path) with torch.no_grad(): avg_EER = 0 i = 0 for e in range(hp.test.epochs): batch_avg_EER = 0 for batch_id, mel_db_batch in enumerate(test_loader):
def train(model_path): device = torch.device(hp.device) train_dataset = SpeakerDatasetTIMITPreprocessed() train_loader = DataLoader(train_dataset, batch_size=hp.train.N, shuffle=True, num_workers=hp.train.num_workers, drop_last=True) embedder_net = SpeechEmbedder().to(device) if hp.train.restore: embedder_net.load_state_dict(torch.load(model_path)) ge2e_loss = GE2ELoss(device) #Both net and loss have trainable parameters optimizer = torch.optim.SGD([{ 'params': embedder_net.parameters() }, { 'params': ge2e_loss.parameters() }], lr=hp.train.lr) os.makedirs(hp.train.checkpoint_dir, exist_ok=True) embedder_net.train() iteration = 0 for e in range(hp.train.epochs): total_loss = 0 for batch_id, mel_db_batch in enumerate(train_loader): mel_db_batch = mel_db_batch.to(device) mel_db_batch = torch.reshape( mel_db_batch, (hp.train.N * hp.train.M, mel_db_batch.size(2), mel_db_batch.size(3))) perm = random.sample(range(0, hp.train.N * hp.train.M), hp.train.N * hp.train.M) unperm = list(perm) for i, j in enumerate(perm): unperm[j] = i mel_db_batch = mel_db_batch[perm] #gradient accumulates optimizer.zero_grad() embeddings = embedder_net(mel_db_batch) embeddings = embeddings[unperm] embeddings = torch.reshape( embeddings, (hp.train.N, hp.train.M, embeddings.size(1))) #get loss, call backward, step optimizer loss = ge2e_loss( embeddings) #wants (Speaker, Utterances, embedding) loss.backward() torch.nn.utils.clip_grad_norm_(embedder_net.parameters(), 3.0) torch.nn.utils.clip_grad_norm_(ge2e_loss.parameters(), 1.0) optimizer.step() total_loss = total_loss + loss iteration += 1 if (batch_id + 1) % hp.train.log_interval == 0: mesg = "{0}\tEpoch:{1}[{2}/{3}],Iteration:{4}\tLoss:{5:.4f}\tTLoss:{6:.4f}\t\n".format( time.ctime(), e + 1, batch_id + 1, len(train_dataset) // hp.train.N, iteration, loss, total_loss / (batch_id + 1)) print(mesg) if hp.train.log_file is not None: ''' if os.path.exists(hp.train.log_file): os.mknod(hp.train.log_file) ''' with open(hp.train.log_file, 'w') as f: f.write(mesg) if hp.train.checkpoint_dir is not None and ( e + 1) % hp.train.checkpoint_interval == 0: embedder_net.eval().cpu() ckpt_model_filename = "ckpt_epoch_" + str( e + 1) + "_batch_id_" + str(batch_id + 1) + ".pth" ckpt_model_path = os.path.join(hp.train.checkpoint_dir, ckpt_model_filename) torch.save(embedder_net.state_dict(), ckpt_model_path) embedder_net.to(device).train() #save model embedder_net.eval().cpu() save_model_filename = "final_epoch_" + str(e + 1) + "_batch_id_" + str( batch_id + 1) + ".model" save_model_path = os.path.join(hp.train.checkpoint_dir, save_model_filename) torch.save(embedder_net.state_dict(), save_model_path) print("\nDone, trained model saved at", save_model_path)
def train(model_path): FNULL = open(os.devnull, 'w') device = torch.device(hp.device) if hp.data.data_preprocessed: train_dataset = SpeakerDatasetTIMITPreprocessed(is_training=True) test_dataset = SpeakerDatasetTIMITPreprocessed(is_training=False) else: train_dataset = SpeakerDatasetTIMIT(is_training=True) test_dataset = SpeakerDatasetTIMIT(is_training=False) train_loader = DataLoader(train_dataset, batch_size=hp.train.N, shuffle=True, num_workers=hp.train.num_workers, drop_last=True) test_loader = DataLoader(test_dataset, batch_size=hp.test.N, shuffle=True, num_workers=hp.test.num_workers, drop_last=True) embedder_net = SpeechEmbedder().to(device) if hp.train.restore: subprocess.call([ 'gsutil', 'cp', 'gs://edinquake/asr/baseline_TIMIT/model_best.pkl', model_path ], stdout=FNULL, stderr=subprocess.STDOUT) embedder_net.load_state_dict(torch.load(model_path)) ge2e_loss = GE2ELoss(device) #Both net and loss have trainable parameters optimizer = torch.optim.SGD([{ 'params': embedder_net.parameters() }, { 'params': ge2e_loss.parameters() }], lr=hp.train.lr) os.makedirs(hp.train.checkpoint_dir, exist_ok=True) iteration = 0 best_validate = float('inf') print('***Started training at {}***'.format(datetime.now())) for e in range(hp.train.epochs): total_loss = 0 progress_bar = tqdm(train_loader, desc='| Epoch {:03d}'.format(e), leave=False, disable=False) embedder_net.train() # Iterate over the training set for batch_id, mel_db_batch in enumerate(progress_bar): mel_db_batch = mel_db_batch.to(device) mel_db_batch = torch.reshape( mel_db_batch, (hp.train.N * hp.train.M, mel_db_batch.size(2), mel_db_batch.size(3))) perm = random.sample(range(0, hp.train.N * hp.train.M), hp.train.N * hp.train.M) unperm = list(perm) for i, j in enumerate(perm): unperm[j] = i mel_db_batch = mel_db_batch[perm] #gradient accumulates optimizer.zero_grad() embeddings = embedder_net(mel_db_batch) embeddings = embeddings[unperm] embeddings = torch.reshape( embeddings, (hp.train.N, hp.train.M, embeddings.size(1))) #get loss, call backward, step optimizer loss = ge2e_loss( embeddings) #wants (Speaker, Utterances, embedding) loss.backward() torch.nn.utils.clip_grad_norm_(embedder_net.parameters(), 3.0) torch.nn.utils.clip_grad_norm_(ge2e_loss.parameters(), 1.0) optimizer.step() total_loss = total_loss + loss.item() iteration += 1 # Update statistics for progress bar progress_bar.set_postfix(iteration=iteration, loss=loss.item(), total_loss=total_loss / (batch_id + 1)) print('| Epoch {:03d}: total_loss {}'.format(e, total_loss)) # Perform validation embedder_net.eval() validation_loss = 0 for batch_id, mel_db_batch in enumerate(test_loader): mel_db_batch = mel_db_batch.to(device) mel_db_batch = torch.reshape( mel_db_batch, (hp.test.N * hp.test.M, mel_db_batch.size(2), mel_db_batch.size(3))) perm = random.sample(range(0, hp.test.N * hp.test.M), hp.test.N * hp.test.M) unperm = list(perm) for i, j in enumerate(perm): unperm[j] = i mel_db_batch = mel_db_batch[perm] embeddings = embedder_net(mel_db_batch) embeddings = embeddings[unperm] embeddings = torch.reshape( embeddings, (hp.test.N, hp.test.M, embeddings.size(1))) #get loss loss = ge2e_loss( embeddings) #wants (Speaker, Utterances, embedding) validation_loss += loss.item() validation_loss /= len(test_loader) print('validation_loss: {}'.format(validation_loss)) if validation_loss <= best_validate: best_validate = validation_loss # Save best filename = 'model_best.pkl' ckpt_model_path = os.path.join(hp.train.checkpoint_dir, filename) torch.save(embedder_net.state_dict(), ckpt_model_path) subprocess.call([ 'gsutil', 'cp', ckpt_model_path, 'gs://edinquake/asr/baseline_TIMIT/model_best.pkl' ], stdout=FNULL, stderr=subprocess.STDOUT) filename = 'model_last.pkl' ckpt_model_path = os.path.join(hp.train.checkpoint_dir, filename) torch.save(embedder_net.state_dict(), ckpt_model_path)