def __init__(self): self.neural_factory = nemo.core.NeuralModuleFactory( placement=nemo.core.DeviceType.GPU, cudnn_benchmark=True) self.asr_model = nemo_asr.models.ASRConvCTCModel.from_pretrained( model_info="QuartzNet15x5-En-Base.nemo") # Set this to True to enable beam search decoder self.ENABLE_NGRAM = True # This is only necessary if ENABLE_NGRAM = True. Otherwise, set to empty string self.LM_PATH = "WSJ_lm.binary" self.greedy_decoder = nemo_asr.GreedyCTCDecoder() self.labels = self.asr_model.vocabulary self.beam_search_with_lm = nemo_asr.BeamSearchDecoderWithLM( vocab=self.labels, beam_width=64, alpha=2.0, beta=1.5, lm_path=self.LM_PATH, num_cpus=max(os.cpu_count(), 1), )
def __init__(self, model_yaml, encoder_checkpoint, decoder_checkpoint, language_model=None): super(JasperASR, self).__init__() # Read model YAML yaml = YAML(typ="safe") with open(model_yaml) as f: jasper_model_definition = yaml.load(f) self.neural_factory = nemo.core.NeuralModuleFactory( placement=nemo.core.DeviceType.GPU, backend=nemo.core.Backend.PyTorch) self.labels = jasper_model_definition["labels"] self.data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor() self.jasper_encoder = nemo_asr.JasperEncoder( jasper=jasper_model_definition["JasperEncoder"]["jasper"], activation=jasper_model_definition["JasperEncoder"]["activation"], feat_in=jasper_model_definition[ "AudioToMelSpectrogramPreprocessor"]["features"], ) self.jasper_encoder.restore_from(encoder_checkpoint, local_rank=0) self.jasper_decoder = nemo_asr.JasperDecoderForCTC(feat_in=1024, num_classes=len( self.labels)) self.jasper_decoder.restore_from(decoder_checkpoint, local_rank=0) self.greedy_decoder = nemo_asr.GreedyCTCDecoder() self.beam_search_with_lm = None if language_model: self.beam_search_with_lm = nemo_asr.BeamSearchDecoderWithLM( vocab=self.labels, beam_width=64, alpha=2.0, beta=1.0, lm_path=language_model, num_cpus=max(os.cpu_count(), 1), )
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 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 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 main(): parser = argparse.ArgumentParser(description='Jasper') parser.add_argument("--local_rank", default=None, type=int) parser.add_argument("--batch_size", default=32, type=int) parser.add_argument("--model_config", type=str, required=True) parser.add_argument("--eval_datasets", type=str, required=True) parser.add_argument("--load_dir", type=str, required=True) parser.add_argument("--vocab_file", type=str, required=True) parser.add_argument("--save_logprob", default=None, type=str) parser.add_argument("--lm_path", default=None, type=str) parser.add_argument("--beam_width", default=50, type=int) parser.add_argument("--alpha", default=2.0, type=float) parser.add_argument("--beta", default=1.0, type=float) parser.add_argument("--cutoff_prob", default=0.99, type=float) parser.add_argument("--cutoff_top_n", default=40, type=int) args = parser.parse_args() batch_size = args.batch_size load_dir = args.load_dir if args.local_rank is not None: if args.lm_path: raise NotImplementedError( "Beam search decoder with LM does not currently support evaluation on multi-gpu." ) device = nemo.core.DeviceType.AllGpu else: device = nemo.core.DeviceType.GPU # Instantiate Neural Factory with supported backend neural_factory = nemo.core.NeuralModuleFactory( backend=nemo.core.Backend.PyTorch, local_rank=args.local_rank, optimization_level=nemo.core.Optimization.mxprO1, placement=device, ) if args.local_rank is not None: logging.info('Doing ALL GPU') 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'] eval_datasets = args.eval_datasets 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_layer = nemo_asr.AudioToTextDataLayer( manifest_filepath=eval_datasets, sample_rate=sample_rate, labels=vocab, batch_size=batch_size, **eval_dl_params, ) n = len(data_layer) logging.info('Evaluating {0} examples'.format(n)) data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor( sample_rate=sample_rate, **jasper_params["AudioToMelSpectrogramPreprocessor"], ) 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), ) greedy_decoder = nemo_asr.GreedyCTCDecoder() if args.lm_path: beam_width = args.beam_width alpha = args.alpha beta = args.beta cutoff_prob = args.cutoff_prob cutoff_top_n = args.cutoff_top_n beam_search_with_lm = nemo_asr.BeamSearchDecoderWithLM( vocab=vocab, beam_width=beam_width, alpha=alpha, beta=beta, cutoff_prob=cutoff_prob, cutoff_top_n=cutoff_top_n, lm_path=args.lm_path, num_cpus=max(os.cpu_count(), 1), ) 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('================================') ( audio_signal_e1, a_sig_length_e1, transcript_e1, transcript_len_e1, ) = data_layer() processed_signal_e1, p_length_e1 = data_preprocessor( input_signal=audio_signal_e1, length=a_sig_length_e1) encoded_e1, encoded_len_e1 = jasper_encoder( audio_signal=processed_signal_e1, length=p_length_e1) log_probs_e1 = jasper_decoder(encoder_output=encoded_e1) predictions_e1 = greedy_decoder(log_probs=log_probs_e1) eval_tensors = [ log_probs_e1, predictions_e1, transcript_e1, transcript_len_e1, encoded_len_e1, ] if args.lm_path: beam_predictions_e1 = beam_search_with_lm( log_probs=log_probs_e1, log_probs_length=encoded_len_e1) eval_tensors.append(beam_predictions_e1) evaluated_tensors = neural_factory.infer( tensors=eval_tensors, checkpoint_dir=load_dir, ) greedy_hypotheses = post_process_predictions(evaluated_tensors[1], vocab) references = post_process_transcripts(evaluated_tensors[2], evaluated_tensors[3], vocab) cer = word_error_rate(hypotheses=greedy_hypotheses, references=references, use_cer=True) logging.info("Greedy CER {:.2f}%".format(cer * 100)) if args.lm_path: beam_hypotheses = [] # Over mini-batch for i in evaluated_tensors[-1]: # Over samples for j in i: beam_hypotheses.append(j[0][1]) cer = word_error_rate(hypotheses=beam_hypotheses, references=references, use_cer=True) logging.info("Beam CER {:.2f}".format(cer * 100)) if args.save_logprob: # Convert logits to list of numpy arrays logprob = [] for i, batch in enumerate(evaluated_tensors[0]): for j in range(batch.shape[0]): logprob.append( batch[j][:evaluated_tensors[4][i][j], :].cpu().numpy()) with open(args.save_logprob, 'wb') as f: pickle.dump(logprob, f, protocol=pickle.HIGHEST_PROTOCOL)
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'", ) parser.add_argument("--dataset", type=str, required=True, help="path to evaluation data") parser.add_argument("--eval_batch_size", type=int, default=1, help="batch size to use for evaluation") parser.add_argument("--wer_target", type=float, default=None, help="used by test") parser.add_argument("--wer_tolerance", type=float, default=1.0, help="used by test") parser.add_argument("--trim_silence", default=True, type=bool, help="trim audio from silence or not") parser.add_argument( "--normalize_text", default=True, type=bool, help="Normalize transcripts or not. 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() # Instantiate the model which we'll train logging.info(f"Speech2Text: Will fine-tune from {args.asr_model}") asr_model = nemo_asr.models.ASRConvCTCModel.from_pretrained( model_info=args.asr_model) asr_model.eval() logging.info("\n\n") logging.info(f"Evaluation using {type(asr_model)} model.") logging.info(f"Evaluation using alphabet {asr_model.vocabulary}.") logging.info(f"The model has {asr_model.num_weights} weights.\n\n") eval_data_layer = nemo_asr.AudioToTextDataLayer( manifest_filepath=args.dataset, labels=asr_model.vocabulary, batch_size=args.eval_batch_size, trim_silence=args.trim_silence, shuffle=False, normalize_transcripts=args.normalize_text, ) greedy_decoder = nemo_asr.GreedyCTCDecoder() 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) predictions = greedy_decoder(log_probs=log_probs) # inference eval_tensors = [ log_probs, predictions, transcript, transcript_len, encoded_len ] evaluated_tensors = nf.infer(tensors=eval_tensors) greedy_hypotheses = post_process_predictions(evaluated_tensors[1], asr_model.vocabulary) references = post_process_transcripts(evaluated_tensors[2], evaluated_tensors[3], asr_model.vocabulary) if args.asr_model.strip().endswith('-Zh'): val = word_error_rate(hypotheses=greedy_hypotheses, references=references, use_cer=True) metric = 'CER' else: val = word_error_rate(hypotheses=greedy_hypotheses, references=references, use_cer=False) metric = 'WER' logging.info(f"Greedy {metric} = {val}") if args.wer_target is not None: if args.wer_target * args.wer_tolerance < wer: raise ValueError( f"Resulting WER {wer} is higher than the target {args.wer_target}" )
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 loss = ctc_loss(
def main(): parser = argparse.ArgumentParser(description='Jasper') # model params parser.add_argument("--model_config", type=str, required=True) parser.add_argument("--eval_datasets", type=str, required=True) parser.add_argument("--load_dir", type=str, required=True) # run params parser.add_argument("--local_rank", default=None, type=int) parser.add_argument("--batch_size", default=64, type=int) parser.add_argument("--amp_opt_level", default="O1", type=str) # store results parser.add_argument("--save_logprob", default=None, type=str) # lm inference parameters parser.add_argument("--lm_path", default=None, type=str) parser.add_argument('--alpha', default=2.0, type=float, help='value of LM weight', required=False) parser.add_argument( '--alpha_max', type=float, help='maximum value of LM weight (for a grid search in \'eval\' mode)', required=False, ) parser.add_argument('--alpha_step', type=float, help='step for LM weight\'s tuning in \'eval\' mode', required=False, default=0.1) parser.add_argument('--beta', default=1.5, type=float, help='value of word count weight', required=False) parser.add_argument( '--beta_max', type=float, help='maximum value of word count weight (for a grid search in \ \'eval\' mode', required=False, ) parser.add_argument( '--beta_step', type=float, help='step for word count weight\'s tuning in \'eval\' mode', required=False, default=0.1, ) parser.add_argument("--beam_width", default=128, type=int) args = parser.parse_args() batch_size = args.batch_size load_dir = args.load_dir if args.local_rank is not None: if args.lm_path: raise NotImplementedError( "Beam search decoder with LM does not currently support evaluation on multi-gpu." ) device = nemo.core.DeviceType.AllGpu else: device = nemo.core.DeviceType.GPU # Instantiate Neural Factory with supported backend neural_factory = nemo.core.NeuralModuleFactory( backend=nemo.core.Backend.PyTorch, local_rank=args.local_rank, optimization_level=args.amp_opt_level, placement=device, ) if args.local_rank is not None: logging.info('Doing ALL GPU') 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'] eval_datasets = args.eval_datasets 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 = nemo_asr.AudioToTextDataLayer( manifest_filepath=eval_datasets, sample_rate=sample_rate, labels=vocab, batch_size=batch_size, **eval_dl_params, ) N = len(data_layer) logging.info('Evaluating {0} examples'.format(N)) data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor( sample_rate=sample_rate, **jasper_params["AudioToMelSpectrogramPreprocessor"]) 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)) 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('================================') # Define inference DAG audio_signal_e1, a_sig_length_e1, transcript_e1, transcript_len_e1 = data_layer( ) processed_signal_e1, p_length_e1 = data_preprocessor( input_signal=audio_signal_e1, length=a_sig_length_e1) encoded_e1, encoded_len_e1 = jasper_encoder( audio_signal=processed_signal_e1, length=p_length_e1) log_probs_e1 = jasper_decoder(encoder_output=encoded_e1) predictions_e1 = greedy_decoder(log_probs=log_probs_e1) eval_tensors = [ log_probs_e1, predictions_e1, transcript_e1, transcript_len_e1, encoded_len_e1 ] # inference evaluated_tensors = neural_factory.infer(tensors=eval_tensors, checkpoint_dir=load_dir) greedy_hypotheses = post_process_predictions(evaluated_tensors[1], vocab) references = post_process_transcripts(evaluated_tensors[2], evaluated_tensors[3], vocab) wer = word_error_rate(hypotheses=greedy_hypotheses, references=references) logging.info("Greedy WER {:.2f}%".format(wer * 100)) # Convert logits to list of numpy arrays logprob = [] for i, batch in enumerate(evaluated_tensors[0]): for j in range(batch.shape[0]): logprob.append( batch[j][:evaluated_tensors[4][i][j], :].cpu().numpy()) if args.save_logprob: with open(args.save_logprob, 'wb') as f: pickle.dump(logprob, f, protocol=pickle.HIGHEST_PROTOCOL) # language model if args.lm_path: if args.alpha_max is None: args.alpha_max = args.alpha # include alpha_max in tuning range args.alpha_max += args.alpha_step / 10.0 if args.beta_max is None: args.beta_max = args.beta # include beta_max in tuning range args.beta_max += args.beta_step / 10.0 beam_wers = [] logprobexp = [np.exp(p) for p in logprob] for alpha in np.arange(args.alpha, args.alpha_max, args.alpha_step): for beta in np.arange(args.beta, args.beta_max, args.beta_step): logging.info('================================') logging.info(f'Infering with (alpha, beta): ({alpha}, {beta})') beam_search_with_lm = nemo_asr.BeamSearchDecoderWithLM( vocab=vocab, beam_width=args.beam_width, alpha=alpha, beta=beta, lm_path=args.lm_path, num_cpus=max(os.cpu_count(), 1), input_tensor=False, ) beam_predictions = beam_search_with_lm(log_probs=logprobexp, log_probs_length=None, force_pt=True) beam_predictions = [b[0][1] for b in beam_predictions[0]] lm_wer = word_error_rate(hypotheses=beam_predictions, references=references) logging.info("Beam WER {:.2f}%".format(lm_wer * 100)) beam_wers.append(((alpha, beta), lm_wer * 100)) logging.info('Beam WER for (alpha, beta)') logging.info('================================') logging.info('\n' + '\n'.join([str(e) for e in beam_wers])) logging.info('================================') best_beam_wer = min(beam_wers, key=lambda x: x[1]) logging.info('Best (alpha, beta): ' f'{best_beam_wer[0]}, ' f'WER: {best_beam_wer[1]:.2f}%')
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)