def create_all_dags(args, neural_factory): logger = neural_factory.logger 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"] 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.iter_per_step * args.num_gpus)) logger.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"]) 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: neural_factory.logger.info("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), factory=neural_factory) ctc_loss = nemo_asr.CTCLossNM( num_classes=len(vocab)) greedy_decoder = nemo_asr.GreedyCTCDecoder() logger.info('================================') logger.info( f"Number of parameters in encoder: {jasper_encoder.num_weights}") logger.info( f"Number of parameters in decoder: {jasper_decoder.num_weights}") logger.info( f"Total number of parameters in decoder: " f"{jasper_decoder.num_weights + jasper_encoder.num_weights}") logger.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, logger=logger), 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, tag=tagname, logger=logger), eval_step=args.eval_freq, tb_writer=neural_factory.tb_writer) callbacks.append(eval_callback) return loss_t, callbacks, steps_per_epoch
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) vocab = quartz_params['labels'] sample_rate = quartz_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) # 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: nemo.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