338 lines
11 KiB
Python
338 lines
11 KiB
Python
# Copyright (c) 2019 NVIDIA Corporation
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import argparse
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import copy
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import math
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import os
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from functools import partial
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from ruamel.yaml import YAML
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import nemo
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import nemo.collections.asr as nemo_asr
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import nemo.utils.argparse as nm_argparse
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from nemo.collections.asr.helpers import (
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monitor_asr_train_progress,
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process_evaluation_batch,
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process_evaluation_epoch,
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)
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from nemo.utils.lr_policies import CosineAnnealing
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logging = nemo.logging
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def parse_args():
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parser = argparse.ArgumentParser(
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parents=[nm_argparse.NemoArgParser()],
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description="Jasper",
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conflict_handler="resolve",
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)
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parser.set_defaults(
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checkpoint_dir=None,
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optimizer="novograd",
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batch_size=64,
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eval_batch_size=64,
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lr=0.002,
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amp_opt_level="O1",
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create_tb_writer=True,
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model_config="./train/jasper10x5dr.yaml",
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train_dataset="./train/asr_data/train_manifest.json",
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eval_datasets="./train/asr_data/test_manifest.json",
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work_dir="./train/work",
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num_epochs=50,
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weight_decay=0.005,
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checkpoint_save_freq=1000,
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eval_freq=100,
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load_dir="./train/models/jasper/",
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warmup_steps=3,
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exp_name='jasper-speller'
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)
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# Overwrite default args
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parser.add_argument(
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"--max_steps",
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type=int,
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default=None,
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required=False,
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help="max number of steps to train",
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)
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parser.add_argument(
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"--num_epochs",
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type=int,
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default=None,
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required=False,
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help="number of epochs to train",
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)
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parser.add_argument(
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"--model_config",
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type=str,
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required=False,
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help="model configuration file: model.yaml",
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)
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# Create new args
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parser.add_argument("--exp_name", default="Jasper", type=str)
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parser.add_argument("--beta1", default=0.95, type=float)
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parser.add_argument("--beta2", default=0.25, type=float)
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parser.add_argument("--warmup_steps", default=0, type=int)
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parser.add_argument(
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"--load_dir",
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default=None,
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type=str,
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help="directory with pre-trained checkpoint",
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)
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args = parser.parse_args()
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if args.max_steps is None and args.num_epochs is None:
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raise ValueError("Either max_steps or num_epochs should be provided.")
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return args
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def construct_name(
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name, lr, batch_size, max_steps, num_epochs, wd, optimizer, iter_per_step
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):
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if max_steps is not None:
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return "{0}-lr_{1}-bs_{2}-s_{3}-wd_{4}-opt_{5}-ips_{6}".format(
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name, lr, batch_size, max_steps, wd, optimizer, iter_per_step
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)
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else:
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return "{0}-lr_{1}-bs_{2}-e_{3}-wd_{4}-opt_{5}-ips_{6}".format(
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name, lr, batch_size, num_epochs, wd, optimizer, iter_per_step
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)
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def create_all_dags(args, neural_factory):
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yaml = YAML(typ="safe")
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with open(args.model_config) as f:
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jasper_params = yaml.load(f)
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vocab = jasper_params["labels"]
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sample_rate = jasper_params["sample_rate"]
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# Calculate num_workers for dataloader
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total_cpus = os.cpu_count()
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cpu_per_traindl = max(int(total_cpus / neural_factory.world_size), 1)
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# perturb_config = jasper_params.get('perturb', None)
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train_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"])
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train_dl_params.update(jasper_params["AudioToTextDataLayer"]["train"])
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del train_dl_params["train"]
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del train_dl_params["eval"]
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# del train_dl_params["normalize_transcripts"]
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data_layer = nemo_asr.AudioToTextDataLayer(
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manifest_filepath=args.train_dataset,
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sample_rate=sample_rate,
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labels=vocab,
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batch_size=args.batch_size,
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num_workers=cpu_per_traindl,
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**train_dl_params,
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# normalize_transcripts=False
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)
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N = len(data_layer)
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steps_per_epoch = math.ceil(
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N / (args.batch_size * args.iter_per_step * args.num_gpus)
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)
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logging.info("Have {0} examples to train on.".format(N))
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data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor(
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sample_rate=sample_rate, **jasper_params["AudioToMelSpectrogramPreprocessor"]
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)
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multiply_batch_config = jasper_params.get("MultiplyBatch", None)
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if multiply_batch_config:
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multiply_batch = nemo_asr.MultiplyBatch(**multiply_batch_config)
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spectr_augment_config = jasper_params.get("SpectrogramAugmentation", None)
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if spectr_augment_config:
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data_spectr_augmentation = nemo_asr.SpectrogramAugmentation(
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**spectr_augment_config
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)
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eval_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"])
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eval_dl_params.update(jasper_params["AudioToTextDataLayer"]["eval"])
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del eval_dl_params["train"]
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del eval_dl_params["eval"]
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data_layers_eval = []
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if args.eval_datasets:
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for eval_datasets in args.eval_datasets:
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data_layer_eval = nemo_asr.AudioToTextDataLayer(
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manifest_filepath=eval_datasets,
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sample_rate=sample_rate,
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labels=vocab,
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batch_size=args.eval_batch_size,
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num_workers=cpu_per_traindl,
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**eval_dl_params,
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)
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data_layers_eval.append(data_layer_eval)
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else:
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logging.warning("There were no val datasets passed")
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jasper_encoder = nemo_asr.JasperEncoder(
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feat_in=jasper_params["AudioToMelSpectrogramPreprocessor"]["features"],
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**jasper_params["JasperEncoder"],
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)
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jasper_decoder = nemo_asr.JasperDecoderForCTC(
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feat_in=jasper_params["JasperEncoder"]["jasper"][-1]["filters"],
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num_classes=len(vocab),
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)
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ctc_loss = nemo_asr.CTCLossNM(num_classes=len(vocab))
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greedy_decoder = nemo_asr.GreedyCTCDecoder()
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logging.info("================================")
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logging.info(f"Number of parameters in encoder: {jasper_encoder.num_weights}")
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logging.info(f"Number of parameters in decoder: {jasper_decoder.num_weights}")
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logging.info(
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f"Total number of parameters in model: "
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f"{jasper_decoder.num_weights + jasper_encoder.num_weights}"
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)
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logging.info("================================")
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# Train DAG
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(audio_signal_t, a_sig_length_t, transcript_t, transcript_len_t) = data_layer()
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processed_signal_t, p_length_t = data_preprocessor(
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input_signal=audio_signal_t, length=a_sig_length_t
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)
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if multiply_batch_config:
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(
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processed_signal_t,
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p_length_t,
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transcript_t,
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transcript_len_t,
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) = multiply_batch(
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in_x=processed_signal_t,
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in_x_len=p_length_t,
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in_y=transcript_t,
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in_y_len=transcript_len_t,
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)
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if spectr_augment_config:
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processed_signal_t = data_spectr_augmentation(input_spec=processed_signal_t)
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encoded_t, encoded_len_t = jasper_encoder(
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audio_signal=processed_signal_t, length=p_length_t
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)
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log_probs_t = jasper_decoder(encoder_output=encoded_t)
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predictions_t = greedy_decoder(log_probs=log_probs_t)
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loss_t = ctc_loss(
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log_probs=log_probs_t,
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targets=transcript_t,
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input_length=encoded_len_t,
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target_length=transcript_len_t,
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)
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# Callbacks needed to print info to console and Tensorboard
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train_callback = nemo.core.SimpleLossLoggerCallback(
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tensors=[loss_t, predictions_t, transcript_t, transcript_len_t],
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print_func=partial(monitor_asr_train_progress, labels=vocab),
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get_tb_values=lambda x: [("loss", x[0])],
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tb_writer=neural_factory.tb_writer,
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)
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chpt_callback = nemo.core.CheckpointCallback(
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folder=neural_factory.checkpoint_dir,
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load_from_folder=args.load_dir,
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step_freq=args.checkpoint_save_freq,
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)
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callbacks = [train_callback, chpt_callback]
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# assemble eval DAGs
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for i, eval_dl in enumerate(data_layers_eval):
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(audio_signal_e, a_sig_length_e, transcript_e, transcript_len_e) = eval_dl()
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processed_signal_e, p_length_e = data_preprocessor(
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input_signal=audio_signal_e, length=a_sig_length_e
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)
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encoded_e, encoded_len_e = jasper_encoder(
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audio_signal=processed_signal_e, length=p_length_e
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)
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log_probs_e = jasper_decoder(encoder_output=encoded_e)
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predictions_e = greedy_decoder(log_probs=log_probs_e)
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loss_e = ctc_loss(
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log_probs=log_probs_e,
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targets=transcript_e,
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input_length=encoded_len_e,
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target_length=transcript_len_e,
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)
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# create corresponding eval callback
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tagname = os.path.basename(args.eval_datasets[i]).split(".")[0]
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eval_callback = nemo.core.EvaluatorCallback(
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eval_tensors=[loss_e, predictions_e, transcript_e, transcript_len_e],
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user_iter_callback=partial(process_evaluation_batch, labels=vocab),
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user_epochs_done_callback=partial(process_evaluation_epoch, tag=tagname),
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eval_step=args.eval_freq,
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tb_writer=neural_factory.tb_writer,
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)
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callbacks.append(eval_callback)
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return loss_t, callbacks, steps_per_epoch
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def main():
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args = parse_args()
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name = construct_name(
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args.exp_name,
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args.lr,
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args.batch_size,
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args.max_steps,
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args.num_epochs,
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args.weight_decay,
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args.optimizer,
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args.iter_per_step,
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)
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log_dir = name
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if args.work_dir:
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log_dir = os.path.join(args.work_dir, name)
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# instantiate Neural Factory with supported backend
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neural_factory = nemo.core.NeuralModuleFactory(
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backend=nemo.core.Backend.PyTorch,
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local_rank=args.local_rank,
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optimization_level=args.amp_opt_level,
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log_dir=log_dir,
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checkpoint_dir=args.checkpoint_dir,
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create_tb_writer=args.create_tb_writer,
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files_to_copy=[args.model_config, __file__],
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cudnn_benchmark=args.cudnn_benchmark,
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tensorboard_dir=args.tensorboard_dir,
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)
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args.num_gpus = neural_factory.world_size
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checkpoint_dir = neural_factory.checkpoint_dir
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if args.local_rank is not None:
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logging.info("Doing ALL GPU")
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# build dags
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train_loss, callbacks, steps_per_epoch = create_all_dags(args, neural_factory)
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# train model
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neural_factory.train(
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tensors_to_optimize=[train_loss],
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callbacks=callbacks,
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lr_policy=CosineAnnealing(
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args.max_steps
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if args.max_steps is not None
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else args.num_epochs * steps_per_epoch,
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warmup_steps=args.warmup_steps,
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),
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optimizer=args.optimizer,
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optimization_params={
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"num_epochs": args.num_epochs,
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"max_steps": args.max_steps,
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"lr": args.lr,
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"betas": (args.beta1, args.beta2),
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"weight_decay": args.weight_decay,
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"grad_norm_clip": None,
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},
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batches_per_step=args.iter_per_step,
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)
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if __name__ == "__main__":
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main()
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