def test_quartznet_model_training(self): """Integtaion test that instantiates a small Jasper model and tests training with the sample asr data. test_stft_conv_training tests the torch_stft path while test_jasper_training tests the torch.stft path inside of AudioToMelSpectrogramPreprocessor. Training is run for 3 forward and backward steps and asserts that loss after 3 steps is smaller than the loss at the first step. Note: Training is done with batch gradient descent as opposed to stochastic gradient descent due to CTC loss """ with open( os.path.abspath( os.path.join(os.path.dirname(__file__), "../../examples/asr/configs/jasper_an4.yaml")) ) as file: model_definition = self.yaml.load(file) dl = nemo_asr.AudioToTextDataLayer( manifest_filepath=self.manifest_filepath, labels=self.labels, batch_size=30) model = nemo_asr.models.ASRConvCTCModel( preprocessor_params=model_definition[ 'AudioToMelSpectrogramPreprocessor'], encoder_params=model_definition['JasperEncoder'], decoder_params=model_definition['JasperDecoderForCTC'], ) model.train() ctc_loss = nemo_asr.CTCLossNM(num_classes=len(self.labels)) # DAG audio_signal, a_sig_length, transcript, transcript_len = dl() log_probs, encoded_len = model(input_signal=audio_signal, length=a_sig_length) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) loss_list = [] callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss], print_func=partial(self.print_and_log_loss, loss_log_list=loss_list), step_freq=1) self.nf.train( [loss], callbacks=[callback], optimizer="sgd", optimization_params={ "max_steps": 3, "lr": 0.001 }, ) self.nf.reset_trainer() # Assert that training loss went down assert loss_list[-1] < loss_list[0]
def test_freeze_unfreeze_TrainableNM(self): path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../data/jasper_smaller.yaml")) with open(path) as file: jasper_model_definition = self.yaml.load(file) dl = nemo_asr.AudioToTextDataLayer( # featurizer_config=self.featurizer_config, manifest_filepath=self.manifest_filepath, labels=self.labels, batch_size=4, ) pre_process_params = { #'int_values': False, 'frame_splicing': 1, 'features': 64, 'window_size': 0.02, 'n_fft': 512, 'dither': 1e-05, 'window': 'hann', 'sample_rate': 16000, 'normalize': 'per_feature', 'window_stride': 0.01, } preprocessing = nemo_asr.AudioToMelSpectrogramPreprocessor(**pre_process_params) jasper_encoder = nemo_asr.JasperEncoder( feat_in=jasper_model_definition['AudioToMelSpectrogramPreprocessor']['features'], **jasper_model_definition['JasperEncoder'], ) jasper_decoder = nemo_asr.JasperDecoderForCTC(feat_in=1024, num_classes=len(self.labels)) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(self.labels)) jasper_encoder.freeze() jasper_encoder.unfreeze(set(['encoder.4.mconv.0.conv.weight'])) frozen_weight = jasper_encoder.encoder[1].mconv[0].conv.weight.detach().cpu().numpy() unfrozen_weight = jasper_encoder.encoder[4].mconv[0].conv.weight.detach().cpu().numpy() # jasper_decoder.unfreeze() # DAG audio_signal, a_sig_length, transcript, transcript_len = dl() processed_signal, p_length = preprocessing(input_signal=audio_signal, length=a_sig_length) encoded, encoded_len = jasper_encoder(audio_signal=processed_signal, length=p_length) # logging.info(jasper_encoder) log_probs = jasper_decoder(encoder_output=encoded) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss], print_func=lambda x: logging.info(f'Train Loss: {str(x[0].item())}'), ) optimizer = self.nf.get_trainer() optimizer.train( [loss], callbacks=[callback], optimizer="sgd", optimization_params={"max_steps": 5, "lr": 0.0003}, ) new_frozen_weight = jasper_encoder.encoder[1].mconv[0].conv.weight.data new_unfrozen_weight = jasper_encoder.encoder[4].mconv[0].conv.weight.data self.assertTrue(np.array_equal(frozen_weight, new_frozen_weight.detach().cpu().numpy())) self.assertFalse(np.array_equal(unfrozen_weight, new_unfrozen_weight.detach().cpu().numpy()))
def test_asr_with_zero_ds(self): logging.info("Testing ASR NMs with ZeroDS and without pre-processing") path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../data/jasper_smaller.yaml")) with open(path) as file: jasper_model_definition = self.yaml.load(file) dl = nemo.backends.pytorch.common.ZerosDataLayer( size=100, dtype=torch.FloatTensor, batch_size=4, output_ports={ # "processed_signal": NeuralType( # { # 0: AxisType(BatchTag), # 1: AxisType(SpectrogramSignalTag, dim=64), # 2: AxisType(ProcessedTimeTag, dim=64), # } # ), # "processed_length": NeuralType({0: AxisType(BatchTag)}), # "transcript": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag, dim=64)}), # "transcript_length": NeuralType({0: AxisType(BatchTag)}), "processed_signal": NeuralType( (AxisType(AxisKind.Batch), AxisType(AxisKind.Dimension, 64), AxisType(AxisKind.Time, 64)), SpectrogramType(), ), "processed_length": NeuralType(tuple('B'), LengthsType()), "transcript": NeuralType((AxisType(AxisKind.Batch), AxisType(AxisKind.Time, 64)), LabelsType()), "transcript_length": NeuralType(tuple('B'), LengthsType()), }, ) jasper_encoder = nemo_asr.JasperEncoder( feat_in=jasper_model_definition['AudioToMelSpectrogramPreprocessor']['features'], **jasper_model_definition["JasperEncoder"], ) jasper_decoder = nemo_asr.JasperDecoderForCTC(feat_in=1024, num_classes=len(self.labels)) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(self.labels)) # DAG processed_signal, p_length, transcript, transcript_len = dl() encoded, encoded_len = jasper_encoder(audio_signal=processed_signal, length=p_length) # logging.info(jasper_encoder) log_probs = jasper_decoder(encoder_output=encoded) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss], print_func=lambda x: logging.info(f'Train Loss: {str(x[0].item())}'), ) # Instantiate an optimizer to perform `train` action self.nf.train( [loss], callbacks=[callback], optimization_params={"num_epochs": 2, "lr": 0.0003}, optimizer="sgd", )
def test_freeze_unfreeze_TrainableNM(self): with open("tests/data/jasper_smaller.yaml") as file: jasper_model_definition = self.yaml.load(file) dl = nemo_asr.AudioToTextDataLayer( featurizer_config=self.featurizer_config, manifest_filepath=self.manifest_filepath, labels=self.labels, batch_size=4, ) pre_process_params = { 'int_values': False, 'frame_splicing': 1, 'features': 64, 'window_size': 0.02, 'n_fft': 512, 'dither': 1e-05, 'window': 'hann', 'sample_rate': 16000, 'normalize': 'per_feature', 'window_stride': 0.01, } preprocessing = nemo_asr.AudioToMelSpectrogramPreprocessor(**pre_process_params) jasper_encoder = nemo_asr.JasperEncoder( feat_in=jasper_model_definition['AudioToMelSpectrogramPreprocessor']['features'], **jasper_model_definition['JasperEncoder'], ) jasper_decoder = nemo_asr.JasperDecoderForCTC(feat_in=1024, num_classes=len(self.labels)) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(self.labels)) jasper_encoder.freeze() jasper_encoder.unfreeze(set(['encoder.4.conv.1.weight'])) jasper_decoder.unfreeze() # DAG audio_signal, a_sig_length, transcript, transcript_len = dl() processed_signal, p_length = preprocessing(input_signal=audio_signal, length=a_sig_length) encoded, encoded_len = jasper_encoder(audio_signal=processed_signal, length=p_length) # print(jasper_encoder) log_probs = jasper_decoder(encoder_output=encoded) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss], print_func=lambda x: print(f'Train Loss: {str(x[0].item())}'), ) # Instantiate an optimizer to perform `train` action neural_factory = nemo.core.NeuralModuleFactory( backend=nemo.core.Backend.PyTorch, local_rank=None, create_tb_writer=False, ) optimizer = neural_factory.get_trainer() optimizer.train( [loss], callbacks=[callback], optimizer="sgd", optimization_params={"num_epochs": 2, "lr": 0.0003}, )
def create_all_dags(args, neural_factory): yaml = YAML(typ="safe") with open(args.model_config) as f: jasper_params = yaml.load(f) vocab = load_vocab(args.vocab_file) sample_rate = jasper_params['sample_rate'] # Calculate num_workers for dataloader total_cpus = os.cpu_count() cpu_per_traindl = max(int(total_cpus / neural_factory.world_size), 1) # perturb_config = jasper_params.get('perturb', None) train_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"]) train_dl_params.update(jasper_params["AudioToTextDataLayer"]["train"]) del train_dl_params["train"] del train_dl_params["eval"] train_dl_params["normalize_transcripts"] = False data_layer = nemo_asr.AudioToTextDataLayer( manifest_filepath=args.train_dataset, sample_rate=sample_rate, labels=vocab, batch_size=args.batch_size, num_workers=cpu_per_traindl, **train_dl_params, # normalize_transcripts=False ) N = len(data_layer) steps_per_epoch = int(N / (args.batch_size * args.num_gpus)) nemo.logging.info('Have {0} examples to train on.'.format(N)) data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor( sample_rate=sample_rate, **jasper_params["AudioToMelSpectrogramPreprocessor"], ) multiply_batch_config = jasper_params.get('MultiplyBatch', None) if multiply_batch_config: multiply_batch = nemo_asr.MultiplyBatch(**multiply_batch_config) spectr_augment_config = jasper_params.get('SpectrogramAugmentation', None) if spectr_augment_config: data_spectr_augmentation = nemo_asr.SpectrogramAugmentation( **spectr_augment_config) eval_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"]) eval_dl_params.update(jasper_params["AudioToTextDataLayer"]["eval"]) eval_dl_params["normalize_transcripts"] = False del eval_dl_params["train"] del eval_dl_params["eval"] data_layers_eval = [] if args.eval_datasets: for eval_datasets in args.eval_datasets: data_layer_eval = nemo_asr.AudioToTextDataLayer( manifest_filepath=eval_datasets, sample_rate=sample_rate, labels=vocab, batch_size=args.eval_batch_size, num_workers=cpu_per_traindl, **eval_dl_params, ) data_layers_eval.append(data_layer_eval) else: nemo.logging.warning("There were no val datasets passed") jasper_encoder = nemo_asr.JasperEncoder( feat_in=jasper_params["AudioToMelSpectrogramPreprocessor"]["features"], **jasper_params["JasperEncoder"], ) jasper_decoder = nemo_asr.JasperDecoderForCTC( feat_in=jasper_params["JasperEncoder"]["jasper"][-1]["filters"], num_classes=len(vocab)) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(vocab)) greedy_decoder = nemo_asr.GreedyCTCDecoder() nemo.logging.info('================================') nemo.logging.info( f"Number of parameters in encoder: {jasper_encoder.num_weights}") nemo.logging.info( f"Number of parameters in decoder: {jasper_decoder.num_weights}") nemo.logging.info( f"Total number of parameters in model: " f"{jasper_decoder.num_weights + jasper_encoder.num_weights}") nemo.logging.info('================================') # Train DAG ( audio_signal_t, a_sig_length_t, transcript_t, transcript_len_t, ) = data_layer() processed_signal_t, p_length_t = data_preprocessor( input_signal=audio_signal_t, length=a_sig_length_t) if multiply_batch_config: ( processed_signal_t, p_length_t, transcript_t, transcript_len_t, ) = multiply_batch( in_x=processed_signal_t, in_x_len=p_length_t, in_y=transcript_t, in_y_len=transcript_len_t, ) if spectr_augment_config: processed_signal_t = data_spectr_augmentation( input_spec=processed_signal_t) encoded_t, encoded_len_t = jasper_encoder(audio_signal=processed_signal_t, length=p_length_t) log_probs_t = jasper_decoder(encoder_output=encoded_t) predictions_t = greedy_decoder(log_probs=log_probs_t) loss_t = ctc_loss( log_probs=log_probs_t, targets=transcript_t, input_length=encoded_len_t, target_length=transcript_len_t, ) # Callbacks needed to print info to console and Tensorboard train_callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss_t, predictions_t, transcript_t, transcript_len_t], print_func=partial(monitor_asr_train_progress, labels=vocab, eval_metric='CER'), step_freq=args.train_eval_freq, get_tb_values=lambda x: [("loss", x[0])], tb_writer=neural_factory.tb_writer, ) chpt_callback = nemo.core.CheckpointCallback( folder=neural_factory.checkpoint_dir, step_freq=args.checkpoint_save_freq, ) callbacks = [train_callback, chpt_callback] # assemble eval DAGs for i, eval_dl in enumerate(data_layers_eval): ( audio_signal_e, a_sig_length_e, transcript_e, transcript_len_e, ) = eval_dl() processed_signal_e, p_length_e = data_preprocessor( input_signal=audio_signal_e, length=a_sig_length_e) encoded_e, encoded_len_e = jasper_encoder( audio_signal=processed_signal_e, length=p_length_e) log_probs_e = jasper_decoder(encoder_output=encoded_e) predictions_e = greedy_decoder(log_probs=log_probs_e) loss_e = ctc_loss( log_probs=log_probs_e, targets=transcript_e, input_length=encoded_len_e, target_length=transcript_len_e, ) # create corresponding eval callback tagname = os.path.basename(args.eval_datasets[i]).split(".")[0] eval_callback = nemo.core.EvaluatorCallback( eval_tensors=[ loss_e, predictions_e, transcript_e, transcript_len_e, ], user_iter_callback=partial(process_evaluation_batch, labels=vocab), user_epochs_done_callback=partial(process_evaluation_epoch, eval_metric='CER', tag=tagname), eval_step=args.eval_freq, tb_writer=neural_factory.tb_writer, ) callbacks.append(eval_callback) return loss_t, callbacks, steps_per_epoch
def test_jasper_eval(self): with open( os.path.abspath( os.path.join(os.path.dirname(__file__), "../data/jasper_smaller.yaml"))) as file: jasper_model_definition = self.yaml.load(file) dl = nemo_asr.AudioToTextDataLayer( manifest_filepath=self.manifest_filepath, labels=self.labels, batch_size=4, ) pre_process_params = { 'frame_splicing': 1, 'features': 64, 'window_size': 0.02, 'n_fft': 512, 'dither': 1e-05, 'window': 'hann', 'sample_rate': 16000, 'normalize': 'per_feature', 'window_stride': 0.01, } preprocessing = nemo_asr.AudioToMelSpectrogramPreprocessor( **pre_process_params) jasper_encoder = nemo_asr.JasperEncoder( feat_in=jasper_model_definition[ 'AudioToMelSpectrogramPreprocessor']['features'], **jasper_model_definition['JasperEncoder'], ) jasper_decoder = nemo_asr.JasperDecoderForCTC(feat_in=1024, num_classes=len( self.labels)) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(self.labels)) greedy_decoder = nemo_asr.GreedyCTCDecoder() # DAG audio_signal, a_sig_length, transcript, transcript_len = dl() processed_signal, p_length = preprocessing(input_signal=audio_signal, length=a_sig_length) encoded, encoded_len = jasper_encoder(audio_signal=processed_signal, length=p_length) # logging.info(jasper_encoder) log_probs = jasper_decoder(encoder_output=encoded) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) predictions = greedy_decoder(log_probs=log_probs) from nemo.collections.asr.helpers import ( process_evaluation_batch, process_evaluation_epoch, ) eval_callback = nemo.core.EvaluatorCallback( eval_tensors=[loss, predictions, transcript, transcript_len], user_iter_callback=lambda x, y: process_evaluation_batch( x, y, labels=self.labels), user_epochs_done_callback=process_evaluation_epoch, ) # Instantiate an optimizer to perform `train` action self.nf.eval(callbacks=[eval_callback])
def test_stft_conv(self): with open( os.path.abspath( os.path.join(os.path.dirname(__file__), "../data/jasper_smaller.yaml"))) as file: jasper_model_definition = self.yaml.load(file) dl = nemo_asr.AudioToTextDataLayer( manifest_filepath=self.manifest_filepath, labels=self.labels, batch_size=4, ) pre_process_params = { 'frame_splicing': 1, 'features': 64, 'window_size': 0.02, 'n_fft': 512, 'dither': 1e-05, 'window': 'hann', 'sample_rate': 16000, 'normalize': 'per_feature', 'window_stride': 0.01, 'stft_conv': True, } preprocessing = nemo_asr.AudioToMelSpectrogramPreprocessor( **pre_process_params) jasper_encoder = nemo_asr.JasperEncoder( feat_in=jasper_model_definition[ 'AudioToMelSpectrogramPreprocessor']['features'], **jasper_model_definition['JasperEncoder'], ) jasper_decoder = nemo_asr.JasperDecoderForCTC(feat_in=1024, num_classes=len( self.labels)) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(self.labels)) # DAG audio_signal, a_sig_length, transcript, transcript_len = dl() processed_signal, p_length = preprocessing(input_signal=audio_signal, length=a_sig_length) encoded, encoded_len = jasper_encoder(audio_signal=processed_signal, length=p_length) # logging.info(jasper_encoder) log_probs = jasper_decoder(encoder_output=encoded) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss], print_func=lambda x: logging.info(str(x[0].item()))) # Instantiate an optimizer to perform `train` action optimizer = self.nf.get_trainer() optimizer.train( [loss], callbacks=[callback], optimizer="sgd", optimization_params={ "num_epochs": 10, "lr": 0.0003 }, )
def create_all_dags(args, neural_factory): ''' creates train and eval dags as well as their callbacks returns train loss tensor and callbacks''' # parse the config files yaml = YAML(typ="safe") with open(args.model_config) as f: quartz_params = yaml.load(f) try: vocab = quartz_params['labels'] sample_rate = quartz_params['sample_rate'] except KeyError: logging.error("Please make sure you are using older config format (the ones with -old suffix)") exit(1) # Calculate num_workers for dataloader total_cpus = os.cpu_count() cpu_per_traindl = max(int(total_cpus / neural_factory.world_size), 1) # create data layer for training train_dl_params = copy.deepcopy(quartz_params["AudioToTextDataLayer"]) train_dl_params.update(quartz_params["AudioToTextDataLayer"]["train"]) del train_dl_params["train"] del train_dl_params["eval"] # del train_dl_params["normalize_transcripts"] data_layer_train = nemo_asr.AudioToTextDataLayer( manifest_filepath=args.train_dataset, sample_rate=sample_rate, labels=vocab, batch_size=args.batch_size, num_workers=cpu_per_traindl, **train_dl_params, # normalize_transcripts=False ) N = len(data_layer_train) steps_per_epoch = int(N / (args.batch_size * args.iter_per_step * args.num_gpus)) # create separate data layers for eval # we need separate eval dags for separate eval datasets # but all other modules in these dags will be shared eval_dl_params = copy.deepcopy(quartz_params["AudioToTextDataLayer"]) eval_dl_params.update(quartz_params["AudioToTextDataLayer"]["eval"]) del eval_dl_params["train"] del eval_dl_params["eval"] data_layers_eval = [] if args.eval_datasets: for eval_dataset in args.eval_datasets: data_layer_eval = nemo_asr.AudioToTextDataLayer( manifest_filepath=eval_dataset, sample_rate=sample_rate, labels=vocab, batch_size=args.eval_batch_size, num_workers=cpu_per_traindl, **eval_dl_params, ) data_layers_eval.append(data_layer_eval) else: logging.warning("There were no val datasets passed") # create shared modules data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor( sample_rate=sample_rate, **quartz_params["AudioToMelSpectrogramPreprocessor"], ) # (QuartzNet uses the Jasper baseline encoder and decoder) encoder = nemo_asr.JasperEncoder( feat_in=quartz_params["AudioToMelSpectrogramPreprocessor"]["features"], **quartz_params["JasperEncoder"], ) decoder = nemo_asr.JasperDecoderForCTC( feat_in=quartz_params["JasperEncoder"]["jasper"][-1]["filters"], num_classes=len(vocab), ) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(vocab)) greedy_decoder = nemo_asr.GreedyCTCDecoder() # create augmentation modules (only used for training) if their configs # are present multiply_batch_config = quartz_params.get('MultiplyBatch', None) if multiply_batch_config: multiply_batch = nemo_asr.MultiplyBatch(**multiply_batch_config) spectr_augment_config = quartz_params.get('SpectrogramAugmentation', None) if spectr_augment_config: data_spectr_augmentation = nemo_asr.SpectrogramAugmentation(**spectr_augment_config) # assemble train DAG (audio_signal_t, a_sig_length_t, transcript_t, transcript_len_t,) = data_layer_train() processed_signal_t, p_length_t = data_preprocessor(input_signal=audio_signal_t, length=a_sig_length_t) if multiply_batch_config: (processed_signal_t, p_length_t, transcript_t, transcript_len_t,) = multiply_batch( in_x=processed_signal_t, in_x_len=p_length_t, in_y=transcript_t, in_y_len=transcript_len_t, ) if spectr_augment_config: processed_signal_t = data_spectr_augmentation(input_spec=processed_signal_t) encoded_t, encoded_len_t = encoder(audio_signal=processed_signal_t, length=p_length_t) log_probs_t = decoder(encoder_output=encoded_t) predictions_t = greedy_decoder(log_probs=log_probs_t) loss_t = ctc_loss( log_probs=log_probs_t, targets=transcript_t, input_length=encoded_len_t, target_length=transcript_len_t, ) # create train callbacks train_callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss_t, predictions_t, transcript_t, transcript_len_t], print_func=partial(monitor_asr_train_progress, labels=vocab), get_tb_values=lambda x: [["loss", x[0]]], tb_writer=neural_factory.tb_writer, ) callbacks = [train_callback] if args.checkpoint_dir or args.load_dir: chpt_callback = nemo.core.CheckpointCallback( folder=args.checkpoint_dir, load_from_folder=args.load_dir, step_freq=args.checkpoint_save_freq, ) callbacks.append(chpt_callback) # assemble eval DAGs for i, eval_dl in enumerate(data_layers_eval): (audio_signal_e, a_sig_length_e, transcript_e, transcript_len_e,) = eval_dl() processed_signal_e, p_length_e = data_preprocessor(input_signal=audio_signal_e, length=a_sig_length_e) encoded_e, encoded_len_e = encoder(audio_signal=processed_signal_e, length=p_length_e) log_probs_e = decoder(encoder_output=encoded_e) predictions_e = greedy_decoder(log_probs=log_probs_e) loss_e = ctc_loss( log_probs=log_probs_e, targets=transcript_e, input_length=encoded_len_e, target_length=transcript_len_e, ) # create corresponding eval callback tagname = os.path.basename(args.eval_datasets[i]).split(".")[0] eval_callback = nemo.core.EvaluatorCallback( eval_tensors=[loss_e, predictions_e, transcript_e, transcript_len_e,], user_iter_callback=partial(process_evaluation_batch, labels=vocab), user_epochs_done_callback=partial(process_evaluation_epoch, tag=tagname), eval_step=args.eval_freq, tb_writer=neural_factory.tb_writer, ) callbacks.append(eval_callback) return loss_t, callbacks, steps_per_epoch
def create_dags(model_config_file, vocab, args, nf): # Create a data_layer for training. data_layer = nemo_asr.AudioToTextDataLayer.import_from_config( model_config_file, "AudioToTextDataLayer_train", overwrite_params={ "manifest_filepath": args.train_dataset, "batch_size": args.batch_size }, ) num_samples = len(data_layer) steps_per_epoch = math.ceil( num_samples / (data_layer.batch_size * args.iter_per_step * nf.world_size)) total_steps = steps_per_epoch * args.num_epochs logging.info("Train samples=", num_samples, "num_steps=", total_steps) # Create a data_layer for evaluation. data_layer_eval = nemo_asr.AudioToTextDataLayer.import_from_config( model_config_file, "AudioToTextDataLayer_eval", overwrite_params={"manifest_filepath": args.eval_datasets}, ) num_samples = len(data_layer_eval) logging.info(f"Eval samples={num_samples}") # Instantiate data processor. data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor.import_from_config( model_config_file, "AudioToMelSpectrogramPreprocessor") # Instantiate JASPER encoder-decoder modules. jasper_encoder = nemo_asr.JasperEncoder.import_from_config( model_config_file, "JasperEncoder") jasper_decoder = nemo_asr.JasperDecoderForCTC.import_from_config( model_config_file, "JasperDecoderForCTC", overwrite_params={"num_classes": len(vocab)}) # Instantiate losses. ctc_loss = nemo_asr.CTCLossNM(num_classes=len(vocab)) greedy_decoder = nemo_asr.GreedyCTCDecoder() # Create a training graph. audio, audio_len, transcript, transcript_len = data_layer() processed, processed_len = data_preprocessor(input_signal=audio, length=audio_len) encoded, encoded_len = jasper_encoder(audio_signal=processed, length=processed_len) log_probs = jasper_decoder(encoder_output=encoded) predictions = greedy_decoder(log_probs=log_probs) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) # Create an evaluation graph. audio_e, audio_len_e, transcript_e, transcript_len_e = data_layer_eval() processed_e, processed_len_e = data_preprocessor(input_signal=audio_e, length=audio_len_e) encoded_e, encoded_len_e = jasper_encoder(audio_signal=processed_e, length=processed_len_e) log_probs_e = jasper_decoder(encoder_output=encoded_e) predictions_e = greedy_decoder(log_probs=log_probs_e) loss_e = ctc_loss( log_probs=log_probs_e, targets=transcript_e, input_length=encoded_len_e, target_length=transcript_len_e, ) logging.info("Num of params in encoder: {0}".format( jasper_encoder.num_weights)) # Callbacks to print info to console and Tensorboard. train_callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss, predictions, transcript, transcript_len], print_func=partial(monitor_asr_train_progress, labels=vocab), get_tb_values=lambda x: [["loss", x[0]]], tb_writer=nf.tb_writer, ) checkpointer_callback = nemo.core.CheckpointCallback( folder=nf.checkpoint_dir, step_freq=args.checkpoint_save_freq) eval_tensors = [loss_e, predictions_e, transcript_e, transcript_len_e] eval_callback = nemo.core.EvaluatorCallback( eval_tensors=eval_tensors, user_iter_callback=partial(process_evaluation_batch, labels=vocab), user_epochs_done_callback=process_evaluation_epoch, eval_step=args.eval_freq, tb_writer=nf.tb_writer, eval_at_start=not args.do_not_eval_at_start, ) callbacks = [train_callback, checkpointer_callback, eval_callback] # Return entities required by the actual training. return ( loss, eval_tensors, callbacks, total_steps, log_probs_e, encoded_len_e, )
def test_simple_dags(self): # module instantiation with open("tests/data/jasper_smaller.yaml") as file: jasper_model_definition = self.yaml.load(file) labels = jasper_model_definition['labels'] data_layer = nemo_asr.AudioToTextDataLayer( manifest_filepath=self.manifest_filepath, labels=labels, batch_size=4, ) data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor( **jasper_model_definition['AudioToMelSpectrogramPreprocessor']) jasper_encoder = nemo_asr.JasperEncoder( feat_in=jasper_model_definition[ 'AudioToMelSpectrogramPreprocessor']['features'], **jasper_model_definition['JasperEncoder'], ) jasper_decoder = nemo_asr.JasperDecoderForCTC(feat_in=1024, num_classes=len(labels)) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(labels)) greedy_decoder = nemo_asr.GreedyCTCDecoder() # DAG definition ( audio_signal, audio_signal_len, transcript, transcript_len, ) = data_layer() processed_signal, processed_signal_len = data_preprocessor( input_signal=audio_signal, length=audio_signal_len) spec_augment = nemo_asr.SpectrogramAugmentation(rect_masks=5) aug_signal = spec_augment(input_spec=processed_signal) encoded, encoded_len = jasper_encoder(audio_signal=aug_signal, length=processed_signal_len) log_probs = jasper_decoder(encoder_output=encoded) predictions = greedy_decoder(log_probs=log_probs) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) def wrong(): with open("tests/data/jasper_smaller.yaml") as file: jasper_config = self.yaml.load(file) labels = jasper_config['labels'] data_layer = nemo_asr.AudioToTextDataLayer( manifest_filepath=self.manifest_filepath, labels=labels, batch_size=4, ) data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor( **jasper_config['AudioToMelSpectrogramPreprocessor']) jasper_encoder = nemo_asr.JasperEncoder( feat_in=jasper_config['AudioToMelSpectrogramPreprocessor'] ['features'], **jasper_config['JasperEncoder'], ) jasper_decoder = nemo_asr.JasperDecoderForCTC( feat_in=1024, num_classes=len(labels)) # DAG definition ( audio_signal, audio_signal_len, transcript, transcript_len, ) = data_layer() processed_signal, processed_signal_len = data_preprocessor( input_signal=audio_signal, length=audio_signal_len) spec_augment = nemo_asr.SpectrogramAugmentation(rect_masks=5) aug_signal = spec_augment(input_spec=processed_signal) encoded, encoded_len = jasper_encoder(audio_signal=aug_signal, length=processed_signal_len) log_probs = jasper_decoder(encoder_output=processed_signal) self.assertRaises(NeuralPortNmTensorMismatchError, wrong)
def test_stft_conv_training(self): """Integtaion test that instantiates a small Jasper model and tests training with the sample asr data. test_stft_conv_training tests the torch_stft path while test_jasper_training tests the torch.stft path inside of AudioToMelSpectrogramPreprocessor. Training is run for 3 forward and backward steps and asserts that loss after 3 steps is smaller than the loss at the first step. Note: Training is done with batch gradient descent as opposed to stochastic gradient descent due to CTC loss """ with open( os.path.abspath( os.path.join(os.path.dirname(__file__), "../data/jasper_smaller.yaml"))) as file: jasper_model_definition = self.yaml.load(file) dl = nemo_asr.AudioToTextDataLayer( manifest_filepath=self.manifest_filepath, labels=self.labels, batch_size=30) pre_process_params = { 'frame_splicing': 1, 'features': 64, 'window_size': 0.02, 'n_fft': 512, 'dither': 1e-05, 'window': 'hann', 'sample_rate': 16000, 'normalize': 'per_feature', 'window_stride': 0.01, 'stft_conv': True, } preprocessing = nemo_asr.AudioToMelSpectrogramPreprocessor( **pre_process_params) jasper_encoder = nemo_asr.JasperEncoder( feat_in=jasper_model_definition[ 'AudioToMelSpectrogramPreprocessor']['features'], **jasper_model_definition['JasperEncoder'], ) jasper_decoder = nemo_asr.JasperDecoderForCTC(feat_in=1024, num_classes=len( self.labels)) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(self.labels)) # DAG audio_signal, a_sig_length, transcript, transcript_len = dl() processed_signal, p_length = preprocessing(input_signal=audio_signal, length=a_sig_length) encoded, encoded_len = jasper_encoder(audio_signal=processed_signal, length=p_length) # logging.info(jasper_encoder) log_probs = jasper_decoder(encoder_output=encoded) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) loss_list = [] callback = SimpleLossLoggerCallback(tensors=[loss], print_func=partial( self.print_and_log_loss, loss_log_list=loss_list), step_freq=1) self.nf.train( [loss], callbacks=[callback], optimizer="sgd", optimization_params={ "max_steps": 3, "lr": 0.001 }, ) self.nf.reset_trainer() # Assert that training loss went down assert loss_list[-1] < loss_list[0]
def test_contextnet_ctc_training(self): """Integtaion test that instantiates a small ContextNet model and tests training with the sample asr data. Training is run for 3 forward and backward steps and asserts that loss after 3 steps is smaller than the loss at the first step. Note: Training is done with batch gradient descent as opposed to stochastic gradient descent due to CTC loss Checks SE-block with fixed context size and global context, residual_mode='stride_add' and 'stride_last' flags """ with open( os.path.abspath( os.path.join(os.path.dirname(__file__), "../data/contextnet_32.yaml"))) as f: contextnet_model_definition = self.yaml.load(f) dl = nemo_asr.AudioToTextDataLayer( manifest_filepath=self.manifest_filepath, labels=self.labels, batch_size=30) pre_process_params = { 'frame_splicing': 1, 'features': 80, 'window_size': 0.025, 'n_fft': 512, 'dither': 1e-05, 'window': 'hann', 'sample_rate': 16000, 'normalize': 'per_feature', 'window_stride': 0.01, } preprocessing = nemo_asr.AudioToMelSpectrogramPreprocessor( **pre_process_params) spec_aug = nemo_asr.SpectrogramAugmentation( **contextnet_model_definition['SpectrogramAugmentation']) contextnet_encoder = nemo_asr.ContextNetEncoder( feat_in=contextnet_model_definition[ 'AudioToMelSpectrogramPreprocessor']['features'], **contextnet_model_definition['ContextNetEncoder'], ) contextnet_decoder = nemo_asr.ContextNetDecoderForCTC(feat_in=32, hidden_size=16, num_classes=len( self.labels)) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(self.labels)) # DAG audio_signal, a_sig_length, transcript, transcript_len = dl() processed_signal, p_length = preprocessing(input_signal=audio_signal, length=a_sig_length) processed_signal = spec_aug(input_spec=processed_signal) encoded, encoded_len = contextnet_encoder( audio_signal=processed_signal, length=p_length) log_probs = contextnet_decoder(encoder_output=encoded) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) loss_list = [] callback = SimpleLossLoggerCallback(tensors=[loss], print_func=partial( self.print_and_log_loss, loss_log_list=loss_list), step_freq=1) self.nf.train( [loss], callbacks=[callback], optimizer="sgd", optimization_params={ "max_steps": 3, "lr": 0.001 }, ) self.nf.reset_trainer() # Assert that training loss went down assert loss_list[-1] < loss_list[0]
def create_dags(jasper_params, args, nf): vocab = jasper_params['labels'] # build train and eval model train_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"]) train_dl_params.update(jasper_params["AudioToTextDataLayer"]["train"]) del train_dl_params["train"] del train_dl_params["eval"] data_layer = nemo_asr.AudioToTextDataLayer( manifest_filepath=args.train_dataset, labels=vocab, batch_size=args.batch_size, **train_dl_params, ) num_samples = len(data_layer) steps_per_epoch = math.ceil( num_samples / (args.batch_size * args.iter_per_step * nf.world_size)) total_steps = steps_per_epoch * args.num_epochs logging.info("Train samples=", num_samples, "num_steps=", total_steps) data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor( **jasper_params["AudioToMelSpectrogramPreprocessor"]) # data_augmentation = nemo_asr.SpectrogramAugmentation( # **jasper_params['SpectrogramAugmentation'] # ) eval_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"]) eval_dl_params.update(jasper_params["AudioToTextDataLayer"]["eval"]) del eval_dl_params["train"] del eval_dl_params["eval"] data_layer_eval = nemo_asr.AudioToTextDataLayer( manifest_filepath=args.eval_datasets, labels=vocab, batch_size=args.eval_batch_size, **eval_dl_params, ) num_samples = len(data_layer_eval) logging.info(f"Eval samples={num_samples}") jasper_encoder = nemo_asr.JasperEncoder(**jasper_params["JasperEncoder"]) jasper_decoder = nemo_asr.JasperDecoderForCTC( num_classes=len(vocab), **jasper_params["JasperDecoderForCTC"]) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(vocab)) greedy_decoder = nemo_asr.GreedyCTCDecoder() # Training model audio, audio_len, transcript, transcript_len = data_layer() processed, processed_len = data_preprocessor(input_signal=audio, length=audio_len) encoded, encoded_len = jasper_encoder(audio_signal=processed, length=processed_len) log_probs = jasper_decoder(encoder_output=encoded) predictions = greedy_decoder(log_probs=log_probs) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) # Evaluation model audio_e, audio_len_e, transcript_e, transcript_len_e = data_layer_eval() processed_e, processed_len_e = data_preprocessor(input_signal=audio_e, length=audio_len_e) encoded_e, encoded_len_e = jasper_encoder(audio_signal=processed_e, length=processed_len_e) log_probs_e = jasper_decoder(encoder_output=encoded_e) predictions_e = greedy_decoder(log_probs=log_probs_e) loss_e = ctc_loss( log_probs=log_probs_e, targets=transcript_e, input_length=encoded_len_e, target_length=transcript_len_e, ) logging.info("Num of params in encoder: {0}".format( jasper_encoder.num_weights)) # Callbacks to print info to console and Tensorboard train_callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss, predictions, transcript, transcript_len], print_func=partial(monitor_asr_train_progress, labels=vocab), get_tb_values=lambda x: [["loss", x[0]]], tb_writer=nf.tb_writer, ) checkpointer_callback = nemo.core.CheckpointCallback( folder=nf.checkpoint_dir, step_freq=args.checkpoint_save_freq) eval_tensors = [loss_e, predictions_e, transcript_e, transcript_len_e] eval_callback = nemo.core.EvaluatorCallback( eval_tensors=eval_tensors, user_iter_callback=partial(process_evaluation_batch, labels=vocab), user_epochs_done_callback=process_evaluation_epoch, eval_step=args.eval_freq, tb_writer=nf.tb_writer, ) callbacks = [train_callback, checkpointer_callback, eval_callback] return ( loss, eval_tensors, callbacks, total_steps, vocab, log_probs_e, encoded_len_e, )
def main(): # Usage and Command line arguments parser = ArgumentParser() parser.add_argument( "--asr_model", type=str, default="QuartzNet15x5-En", required=True, help= "Pass: '******', 'QuartzNet15x5-Zh', or 'JasperNet10x5-En' to train from pre-trained models. To train from scratch pass path to modelfile ending with .yaml.", ) parser.add_argument( "--amp_opt_level", default="O0", type=str, choices=["O0", "O1", "O2", "O3"], help="See: https://nvidia.github.io/apex/amp.html", ) parser.add_argument("--train_dataset", type=str, required=True, default=None, help="training dataset path") parser.add_argument("--eval_datasets", type=str, nargs="*", help="evaluation datasets paths") parser.add_argument("--eval_freq", default=1000, type=int, help="Evaluation frequency") parser.add_argument("--eval_batch_size", type=int, default=8, help="batch size to use for evaluation") parser.add_argument("--local_rank", default=None, type=int, help="node rank for distributed training") parser.add_argument("--stats_freq", default=25, type=int, help="frequency with which to update train stats") parser.add_argument("--checkpoint_dir", default=None, type=str, help="Folder where to save checkpoints") parser.add_argument("--checkpoint_save_freq", required=False, type=int, help="how often to checkpoint") parser.add_argument("--optimizer", default="novograd", type=str) parser.add_argument("--warmup_ratio", default=0.02, type=float, help="learning rate warmup ratio") parser.add_argument("--batch_size", required=True, type=int, help="train batch size per GPU") parser.add_argument("--num_epochs", default=5, type=int, help="number of epochs to train") parser.add_argument("--lr", default=0.01, type=float) parser.add_argument("--beta1", default=0.95, type=float) parser.add_argument("--beta2", default=0.5, type=float) parser.add_argument("--weight_decay", default=0.001, type=float) parser.add_argument("--iter_per_step", default=1, type=int, help="number of grad accumulations per batch") parser.add_argument("--wandb_exp_name", default=None, type=str) parser.add_argument("--wandb_project", default=None, type=str) parser.add_argument("--max_train_audio_len", default=16.7, type=float, help="max audio length") parser.add_argument("--do_not_trim_silence", action="store_false", help="Add this flag to disable silence trimming") parser.add_argument("--do_not_normalize_text", action="store_false", help="Add this flag to set to False for non-English.") args = parser.parse_args() # Setup NeuralModuleFactory to control training # instantiate Neural Factory with supported backend nf = nemo.core.NeuralModuleFactory( local_rank=args. local_rank, # This is necessary for distributed training optimization_level=args. amp_opt_level, # This is necessary for mixed precision optimization cudnn_benchmark=True, ) # Instantiate the model which we'll train if args.asr_model.endswith('.yaml'): logging.info( f"Speech2Text: Will train from scratch using config from {args.asr_model}" ) asr_model = nemo_asr.models.ASRConvCTCModel.import_from_config( args.asr_model) else: logging.info(f"Speech2Text: Will fine-tune from {args.asr_model}") asr_model = nemo_asr.models.ASRConvCTCModel.from_pretrained( model_info=args.asr_model, local_rank=args.local_rank) if args.asr_model.strip().endswith('-Zh'): logging.info('USING CER') eval_metric = 'CER' else: eval_metric = 'WER' logging.info("\n\n") logging.info(f"Speech2Text: Training on {nf.world_size} GPUs.") logging.info(f"Training {type(asr_model)} model.") logging.info(f"Training CTC model with alphabet {asr_model.vocabulary}.") logging.info( f"Training CTC model with {asr_model.num_weights} weights.\n\n") train_data_layer = nemo_asr.AudioToTextDataLayer( manifest_filepath=args.train_dataset, labels=asr_model.vocabulary, batch_size=args.batch_size, trim_silence=args.do_not_trim_silence, max_duration=args.max_train_audio_len, shuffle=True, normalize_transcripts=args.do_not_normalize_text, ) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(asr_model.vocabulary)) greedy_decoder = nemo_asr.GreedyCTCDecoder() audio_signal, audio_signal_len, transcript, transcript_len = train_data_layer( ) log_probs, encoded_len = asr_model(input_signal=audio_signal, length=audio_signal_len) predictions = greedy_decoder(log_probs=log_probs) loss = ctc_loss(log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len) # Callbacks which we'll be using: callbacks = [] # SimpleLossLogger prints basic training stats (e.g. loss) to console train_callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss, predictions, transcript, transcript_len], step_freq=args.stats_freq, print_func=partial(monitor_asr_train_progress, labels=asr_model.vocabulary, eval_metric=eval_metric), ) callbacks.append(train_callback) if args.checkpoint_dir is not None and args.checkpoint_save_freq is not None: # Checkpoint callback saves checkpoints periodically checkpointer_callback = nemo.core.CheckpointCallback( folder=args.checkpoint_dir, step_freq=args.checkpoint_save_freq) callbacks.append(checkpointer_callback) if args.wandb_exp_name is not None and args.wandb_project is not None: # WandbCallback saves stats to Weights&Biases wandb_callback = nemo.core.WandBLogger( step_freq=args.stats_freq, wandb_name=args.wandb_exp_name, wandb_project=args.wandb_project, args=args) callbacks.append(wandb_callback) # Evaluation if args.eval_datasets is not None and args.eval_freq is not None: asr_model.eval() # switch model to evaluation mode logging.info(f"Will perform evaluation every {args.eval_freq} steps.") for ind, eval_dataset in enumerate(args.eval_datasets): eval_data_layer = nemo_asr.AudioToTextDataLayer( manifest_filepath=eval_dataset, labels=asr_model.vocabulary, batch_size=args.eval_batch_size, normalize_transcripts=args.do_not_normalize_text, ) audio_signal, audio_signal_len, transcript, transcript_len = eval_data_layer( ) log_probs, encoded_len = asr_model(input_signal=audio_signal, length=audio_signal_len) eval_predictions = greedy_decoder(log_probs=log_probs) eval_loss = ctc_loss(log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len) tag_name = os.path.basename(eval_dataset).split(".")[0] eval_callback = nemo.core.EvaluatorCallback( eval_tensors=[ eval_loss, eval_predictions, transcript, transcript_len ], user_iter_callback=partial(process_evaluation_batch, labels=asr_model.vocabulary), user_epochs_done_callback=partial(process_evaluation_epoch, tag=tag_name, eval_metric=eval_metric), eval_step=args.eval_freq, wandb_name=args.wandb_exp_name, wandb_project=args.wandb_project, ) callbacks.append(eval_callback) steps_in_epoch = len(train_data_layer) / ( args.batch_size * args.iter_per_step * nf.world_size) lr_policy = CosineAnnealing(total_steps=args.num_epochs * steps_in_epoch, warmup_ratio=args.warmup_ratio) nf.train( tensors_to_optimize=[loss], callbacks=callbacks, optimizer=args.optimizer, optimization_params={ "num_epochs": args.num_epochs, "lr": args.lr, "betas": (args.beta1, args.beta2), "weight_decay": args.weight_decay, }, batches_per_step=args.iter_per_step, lr_policy=lr_policy, )
data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor.import_from_config( config_path, "AudioToMelSpectrogramPreprocessor" ) # Create the Jasper_4x1 encoder as specified, and a CTC decoder encoder = nemo_asr.JasperEncoder.import_from_config( config_path, "JasperEncoder" ) decoder = nemo_asr.JasperDecoderForCTC.import_from_config( config_path, "JasperDecoderForCTC", overwrite_params={"num_classes": len(labels)} ) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(labels)) greedy_decoder = nemo_asr.GreedyCTCDecoder() # --- Assemble Training DAG --- # audio_signal, audio_signal_len, transcript, transcript_len = data_layer_train() processed_signal, processed_signal_len = data_preprocessor( input_signal=audio_signal, length=audio_signal_len) encoded, encoded_len = encoder( audio_signal=processed_signal, length=processed_signal_len) log_probs = decoder(encoder_output=encoded) preds = greedy_decoder(log_probs=log_probs) # Training predictions
def create_all_dags(args, neural_factory): yaml = YAML(typ="safe") with open(args.model_config) as f: jasper_params = yaml.load(f) vocab = jasper_params["labels"] sample_rate = jasper_params["sample_rate"] # Calculate num_workers for dataloader total_cpus = os.cpu_count() cpu_per_traindl = max(int(total_cpus / neural_factory.world_size), 1) # perturb_config = jasper_params.get('perturb', None) train_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"]) train_dl_params.update(jasper_params["AudioToTextDataLayer"]["train"]) del train_dl_params["train"] del train_dl_params["eval"] # del train_dl_params["normalize_transcripts"] if args.dataset: d_path = Path(args.dataset) if not args.train_dataset: args.train_dataset = str(d_path / Path("train_manifest.json")) if not args.eval_datasets: args.eval_datasets = [str(d_path / Path("test_manifest.json"))] data_loader_layer = nemo_asr.AudioToTextDataLayer if args.remote_data: train_dl_params["rpyc_host"] = args.remote_data data_loader_layer = RpycAudioToTextDataLayer # data_layer = data_loader_layer( # manifest_filepath=args.train_dataset, # sample_rate=sample_rate, # labels=vocab, # batch_size=args.batch_size, # num_workers=cpu_per_traindl, # **train_dl_params, # # normalize_transcripts=False # ) # # N = len(data_layer) # steps_per_epoch = math.ceil( # N / (args.batch_size * args.iter_per_step * args.num_gpus) # ) # logging.info("Have {0} examples to train on.".format(N)) # data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor( sample_rate=sample_rate, **jasper_params["AudioToMelSpectrogramPreprocessor"]) # multiply_batch_config = jasper_params.get("MultiplyBatch", None) # if multiply_batch_config: # multiply_batch = nemo_asr.MultiplyBatch(**multiply_batch_config) # # spectr_augment_config = jasper_params.get("SpectrogramAugmentation", None) # if spectr_augment_config: # data_spectr_augmentation = nemo_asr.SpectrogramAugmentation( # **spectr_augment_config # ) # eval_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"]) eval_dl_params.update(jasper_params["AudioToTextDataLayer"]["eval"]) if args.remote_data: eval_dl_params["rpyc_host"] = args.remote_data del eval_dl_params["train"] del eval_dl_params["eval"] data_layers_eval = [] # if args.eval_datasets: for eval_datasets in args.eval_datasets: data_layer_eval = data_loader_layer( manifest_filepath=eval_datasets, sample_rate=sample_rate, labels=vocab, batch_size=args.eval_batch_size, num_workers=cpu_per_traindl, **eval_dl_params, ) data_layers_eval.append(data_layer_eval) # else: # logging.warning("There were no val datasets passed") jasper_encoder = nemo_asr.JasperEncoder( feat_in=jasper_params["AudioToMelSpectrogramPreprocessor"]["features"], **jasper_params["JasperEncoder"], ) jasper_encoder.restore_from(args.encoder_checkpoint, local_rank=0) jasper_decoder = nemo_asr.JasperDecoderForCTC( feat_in=jasper_params["JasperEncoder"]["jasper"][-1]["filters"], num_classes=len(vocab), ) jasper_decoder.restore_from(args.decoder_checkpoint, local_rank=0) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(vocab)) greedy_decoder = nemo_asr.GreedyCTCDecoder() # logging.info("================================") # logging.info(f"Number of parameters in encoder: {jasper_encoder.num_weights}") # logging.info(f"Number of parameters in decoder: {jasper_decoder.num_weights}") # logging.info( # f"Total number of parameters in model: " # f"{jasper_decoder.num_weights + jasper_encoder.num_weights}" # ) # logging.info("================================") # # # Train DAG # (audio_signal_t, a_sig_length_t, transcript_t, transcript_len_t) = data_layer() # processed_signal_t, p_length_t = data_preprocessor( # input_signal=audio_signal_t, length=a_sig_length_t # ) # # if multiply_batch_config: # ( # processed_signal_t, # p_length_t, # transcript_t, # transcript_len_t, # ) = multiply_batch( # in_x=processed_signal_t, # in_x_len=p_length_t, # in_y=transcript_t, # in_y_len=transcript_len_t, # ) # # if spectr_augment_config: # processed_signal_t = data_spectr_augmentation(input_spec=processed_signal_t) # # encoded_t, encoded_len_t = jasper_encoder( # audio_signal=processed_signal_t, length=p_length_t # ) # log_probs_t = jasper_decoder(encoder_output=encoded_t) # predictions_t = greedy_decoder(log_probs=log_probs_t) # loss_t = ctc_loss( # log_probs=log_probs_t, # targets=transcript_t, # input_length=encoded_len_t, # target_length=transcript_len_t, # ) # # # Callbacks needed to print info to console and Tensorboard # train_callback = nemo.core.SimpleLossLoggerCallback( # tensors=[loss_t, predictions_t, transcript_t, transcript_len_t], # print_func=partial(monitor_asr_train_progress, labels=vocab), # get_tb_values=lambda x: [("loss", x[0])], # tb_writer=neural_factory.tb_writer, # ) # # chpt_callback = nemo.core.CheckpointCallback( # folder=neural_factory.checkpoint_dir, # load_from_folder=args.load_dir, # step_freq=args.checkpoint_save_freq, # checkpoints_to_keep=30, # ) # # callbacks = [train_callback, chpt_callback] callbacks = [] # assemble eval DAGs for i, eval_dl in enumerate(data_layers_eval): (audio_signal_e, a_sig_length_e, transcript_e, transcript_len_e) = eval_dl() processed_signal_e, p_length_e = data_preprocessor( input_signal=audio_signal_e, length=a_sig_length_e) encoded_e, encoded_len_e = jasper_encoder( audio_signal=processed_signal_e, length=p_length_e) log_probs_e = jasper_decoder(encoder_output=encoded_e) predictions_e = greedy_decoder(log_probs=log_probs_e) loss_e = ctc_loss( log_probs=log_probs_e, targets=transcript_e, input_length=encoded_len_e, target_length=transcript_len_e, ) # create corresponding eval callback tagname = os.path.basename(args.eval_datasets[i]).split(".")[0] eval_callback = nemo.core.EvaluatorCallback( eval_tensors=[ loss_e, predictions_e, transcript_e, transcript_len_e ], user_iter_callback=partial(process_evaluation_batch, labels=vocab), user_epochs_done_callback=partial(process_evaluation_epoch, tag=tagname), eval_step=args.eval_freq, tb_writer=neural_factory.tb_writer, ) callbacks.append(eval_callback) return callbacks
def test_double_jasper_training(self): with open( os.path.abspath( os.path.join(os.path.dirname(__file__), "../data/jasper_smaller.yaml"))) as file: jasper_model_definition = self.yaml.load(file) dl = nemo_asr.AudioToTextDataLayer( featurizer_config=self.featurizer_config, manifest_filepath=self.manifest_filepath, labels=self.labels, batch_size=4, ) pre_process_params = { 'int_values': False, 'frame_splicing': 1, 'features': 64, 'window_size': 0.02, 'n_fft': 512, 'dither': 1e-05, 'window': 'hann', 'sample_rate': 16000, 'normalize': 'per_feature', 'window_stride': 0.01, } preprocessing = nemo_asr.AudioToMelSpectrogramPreprocessor( **pre_process_params) jasper_encoder1 = nemo_asr.JasperEncoder( feat_in=jasper_model_definition[ 'AudioToMelSpectrogramPreprocessor']['features'], **jasper_model_definition['JasperEncoder'], ) jasper_encoder2 = nemo_asr.JasperEncoder( feat_in=jasper_model_definition[ 'AudioToMelSpectrogramPreprocessor']['features'], **jasper_model_definition['JasperEncoder'], ) mx_max1 = nemo.backends.pytorch.common.SimpleCombiner(mode="max") mx_max2 = nemo.backends.pytorch.common.SimpleCombiner(mode="max") jasper_decoder1 = nemo_asr.JasperDecoderForCTC(feat_in=1024, num_classes=len( self.labels)) jasper_decoder2 = nemo_asr.JasperDecoderForCTC(feat_in=1024, num_classes=len( self.labels)) ctc_loss = nemo_asr.CTCLossNM(num_classes=len(self.labels)) # DAG audio_signal, a_sig_length, transcript, transcript_len = dl() processed_signal, p_length = preprocessing(input_signal=audio_signal, length=a_sig_length) encoded1, encoded_len1 = jasper_encoder1(audio_signal=processed_signal, length=p_length) encoded2, encoded_len2 = jasper_encoder2(audio_signal=processed_signal, length=p_length) log_probs1 = jasper_decoder1(encoder_output=encoded1) log_probs2 = jasper_decoder2(encoder_output=encoded2) log_probs = mx_max1(x1=log_probs1, x2=log_probs2) encoded_len = mx_max2(x1=encoded_len1, x2=encoded_len2) loss = ctc_loss( log_probs=log_probs, targets=transcript, input_length=encoded_len, target_length=transcript_len, ) callback = nemo.core.SimpleLossLoggerCallback( tensors=[loss], print_func=lambda x: logging.info(str(x[0].item()))) # Instantiate an optimizer to perform `train` action neural_factory = nemo.core.NeuralModuleFactory( backend=nemo.core.Backend.PyTorch, local_rank=None, create_tb_writer=False, ) optimizer = neural_factory.get_trainer() optimizer.train( [loss], callbacks=[callback], optimizer="sgd", optimization_params={ "num_epochs": 10, "lr": 0.0003 }, )