Source code for espnet.bin.st_train

#!/usr/bin/env python3
# encoding: utf-8

# Copyright 2019 Kyoto University (Hirofumi Inaguma)
#  Apache 2.0  (

"""End-to-end speech translation model training script."""

import logging
import os
import random
import subprocess
import sys

import configargparse
import numpy as np

from espnet import __version__
from espnet.utils.cli_utils import strtobool

# NOTE: you need this func to generate our sphinx doc
[docs]def get_parser(parser=None, required=True): """Get default arguments.""" if parser is None: parser = configargparse.ArgumentParser( description="Train a speech translation (ST) model on one CPU, " "one or multiple GPUs", config_file_parser_class=configargparse.YAMLConfigFileParser, formatter_class=configargparse.ArgumentDefaultsHelpFormatter, ) # general configuration parser.add("--config", is_config_file=True, help="config file path") parser.add( "--config2", is_config_file=True, help="second config file path that overwrites the settings in `--config`.", ) parser.add( "--config3", is_config_file=True, help="third config file path that overwrites the settings " "in `--config` and `--config2`.", ) parser.add_argument( "--ngpu", default=None, type=int, help="Number of GPUs. If not given, use all visible devices", ) parser.add_argument( "--train-dtype", default="float32", choices=["float16", "float32", "float64", "O0", "O1", "O2", "O3"], help="Data type for training (only pytorch backend). " "O0,O1,.. flags require apex. " "See", ) parser.add_argument( "--backend", default="chainer", type=str, choices=["chainer", "pytorch"], help="Backend library", ) parser.add_argument( "--outdir", type=str, required=required, help="Output directory" ) parser.add_argument("--debugmode", default=1, type=int, help="Debugmode") parser.add_argument("--dict", required=required, help="Dictionary") parser.add_argument("--seed", default=1, type=int, help="Random seed") parser.add_argument("--debugdir", type=str, help="Output directory for debugging") parser.add_argument( "--resume", "-r", default="", nargs="?", help="Resume the training from snapshot", ) parser.add_argument( "--minibatches", "-N", type=int, default="-1", help="Process only N minibatches (for debug)", ) parser.add_argument("--verbose", "-V", default=0, type=int, help="Verbose option") parser.add_argument( "--tensorboard-dir", default=None, type=str, nargs="?", help="Tensorboard log dir path", ) parser.add_argument( "--report-interval-iters", default=100, type=int, help="Report interval iterations", ) parser.add_argument( "--save-interval-iters", default=0, type=int, help="Save snapshot interval iterations", ) # task related parser.add_argument( "--train-json", type=str, default=None, help="Filename of train label data (json)", ) parser.add_argument( "--valid-json", type=str, default=None, help="Filename of validation label data (json)", ) # network architecture parser.add_argument( "--model-module", type=str, default=None, help="model defined module (default: espnet.nets.xxx_backend.e2e_st:E2E)", ) # loss related parser.add_argument( "--ctc_type", default="builtin", type=str, choices=["builtin", "gtnctc", "cudnnctc"], help="Type of CTC implementation to calculate loss.", ) parser.add_argument( "--mtlalpha", default=0.0, type=float, help="Multitask learning coefficient, alpha: \ alpha*ctc_loss + (1-alpha)*att_loss", ) parser.add_argument( "--asr-weight", default=0.0, type=float, help="Multitask learning coefficient for ASR task, weight: " " asr_weight*(alpha*ctc_loss + (1-alpha)*att_loss)" " + (1-asr_weight-mt_weight)*st_loss", ) parser.add_argument( "--mt-weight", default=0.0, type=float, help="Multitask learning coefficient for MT task, weight: \ mt_weight*mt_loss + (1-mt_weight-asr_weight)*st_loss", ) parser.add_argument( "--lsm-weight", default=0.0, type=float, help="Label smoothing weight" ) # recognition options to compute CER/WER parser.add_argument( "--report-cer", default=False, action="store_true", help="Compute CER on development set", ) parser.add_argument( "--report-wer", default=False, action="store_true", help="Compute WER on development set", ) # translations options to compute BLEU parser.add_argument( "--report-bleu", default=True, action="store_true", help="Compute BLEU on development set", ) parser.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses") parser.add_argument("--beam-size", type=int, default=4, help="Beam size") parser.add_argument("--penalty", default=0.0, type=float, help="Incertion penalty") parser.add_argument( "--maxlenratio", default=0.0, type=float, help="""Input length ratio to obtain max output length. If maxlenratio=0.0 (default), it uses a end-detect function to automatically find maximum hypothesis lengths""", ) parser.add_argument( "--minlenratio", default=0.0, type=float, help="Input length ratio to obtain min output length", ) parser.add_argument( "--rnnlm", type=str, default=None, help="RNNLM model file to read" ) parser.add_argument( "--rnnlm-conf", type=str, default=None, help="RNNLM model config file to read" ) parser.add_argument("--lm-weight", default=0.0, type=float, help="RNNLM weight.") parser.add_argument("--sym-space", default="<space>", type=str, help="Space symbol") parser.add_argument("--sym-blank", default="<blank>", type=str, help="Blank symbol") # minibatch related parser.add_argument( "--sortagrad", default=0, type=int, nargs="?", help="How many epochs to use sortagrad for. 0 = deactivated, -1 = all epochs", ) parser.add_argument( "--batch-count", default="auto", choices=BATCH_COUNT_CHOICES, help="How to count batch_size. " "The default (auto) will find how to count by args.", ) parser.add_argument( "--batch-size", "--batch-seqs", "-b", default=0, type=int, help="Maximum seqs in a minibatch (0 to disable)", ) parser.add_argument( "--batch-bins", default=0, type=int, help="Maximum bins in a minibatch (0 to disable)", ) parser.add_argument( "--batch-frames-in", default=0, type=int, help="Maximum input frames in a minibatch (0 to disable)", ) parser.add_argument( "--batch-frames-out", default=0, type=int, help="Maximum output frames in a minibatch (0 to disable)", ) parser.add_argument( "--batch-frames-inout", default=0, type=int, help="Maximum input+output frames in a minibatch (0 to disable)", ) parser.add_argument( "--maxlen-in", "--batch-seq-maxlen-in", default=800, type=int, metavar="ML", help="When --batch-count=seq, batch size is reduced " "if the input sequence length > ML.", ) parser.add_argument( "--maxlen-out", "--batch-seq-maxlen-out", default=150, type=int, metavar="ML", help="When --batch-count=seq, " "batch size is reduced if the output sequence length > ML", ) parser.add_argument( "--n-iter-processes", default=0, type=int, help="Number of processes of iterator", ) parser.add_argument( "--preprocess-conf", type=str, default=None, nargs="?", help="The configuration file for the pre-processing", ) # optimization related parser.add_argument( "--opt", default="adadelta", type=str, choices=["adadelta", "adam", "noam"], help="Optimizer", ) parser.add_argument( "--accum-grad", default=1, type=int, help="Number of gradient accumuration" ) parser.add_argument( "--eps", default=1e-8, type=float, help="Epsilon constant for optimizer" ) parser.add_argument( "--eps-decay", default=0.01, type=float, help="Decaying ratio of epsilon" ) parser.add_argument( "--lr", default=1e-3, type=float, help="Learning rate for optimizer" ) parser.add_argument( "--lr-decay", default=1.0, type=float, help="Decaying ratio of learning rate" ) parser.add_argument( "--weight-decay", default=0.0, type=float, help="Weight decay ratio" ) parser.add_argument( "--criterion", default="acc", type=str, choices=["loss", "acc"], help="Criterion to perform epsilon decay", ) parser.add_argument( "--threshold", default=1e-4, type=float, help="Threshold to stop iteration" ) parser.add_argument( "--epochs", "-e", default=30, type=int, help="Maximum number of epochs" ) parser.add_argument( "--early-stop-criterion", default="validation/main/acc", type=str, nargs="?", help="Value to monitor to trigger an early stopping of the training", ) parser.add_argument( "--patience", default=3, type=int, nargs="?", help="Number of epochs to wait " "without improvement before stopping the training", ) parser.add_argument( "--grad-clip", default=5, type=float, help="Gradient norm threshold to clip" ) parser.add_argument( "--num-save-attention", default=3, type=int, help="Number of samples of attention to be saved", ) parser.add_argument( "--num-save-ctc", default=3, type=int, help="Number of samples of CTC probability to be saved", ) parser.add_argument( "--grad-noise", type=strtobool, default=False, help="The flag to switch to use noise injection to gradients during training", ) # speech translation related parser.add_argument( "--context-residual", default=False, type=strtobool, nargs="?", help="The flag to switch to use context vector residual in the decoder network", ) # finetuning related parser.add_argument( "--enc-init", default=None, type=str, nargs="?", help="Pre-trained ASR model to initialize encoder.", ) parser.add_argument( "--enc-init-mods", default="enc.enc.", type=lambda s: [str(mod) for mod in s.split(",") if s != ""], help="List of encoder modules to initialize, separated by a comma.", ) parser.add_argument( "--dec-init", default=None, type=str, nargs="?", help="Pre-trained ASR, MT or LM model to initialize decoder.", ) parser.add_argument( "--dec-init-mods", default="att., dec.", type=lambda s: [str(mod) for mod in s.split(",") if s != ""], help="List of decoder modules to initialize, separated by a comma.", ) # multilingual related parser.add_argument( "--multilingual", default=False, type=strtobool, help="Prepend target language ID to the source sentence. " " Both source/target language IDs must be prepend in the pre-processing stage.", ) parser.add_argument( "--replace-sos", default=False, type=strtobool, help="Replace <sos> in the decoder with a target language ID \ (the first token in the target sequence)", ) # Feature transform: Normalization parser.add_argument( "--stats-file", type=str, default=None, help="The stats file for the feature normalization", ) parser.add_argument( "--apply-uttmvn", type=strtobool, default=True, help="Apply utterance level mean " "variance normalization.", ) parser.add_argument("--uttmvn-norm-means", type=strtobool, default=True, help="") parser.add_argument("--uttmvn-norm-vars", type=strtobool, default=False, help="") # Feature transform: Fbank parser.add_argument( "--fbank-fs", type=int, default=16000, help="The sample frequency used for " "the mel-fbank creation.", ) parser.add_argument( "--n-mels", type=int, default=80, help="The number of mel-frequency bins." ) parser.add_argument("--fbank-fmin", type=float, default=0.0, help="") parser.add_argument("--fbank-fmax", type=float, default=None, help="") return parser
[docs]def main(cmd_args): """Run the main training function.""" parser = get_parser() args, _ = parser.parse_known_args(cmd_args) if args.backend == "chainer" and args.train_dtype != "float32": raise NotImplementedError( f"chainer backend does not support --train-dtype {args.train_dtype}." "Use --dtype float32." ) if args.ngpu == 0 and args.train_dtype in ("O0", "O1", "O2", "O3", "float16"): raise ValueError( f"--train-dtype {args.train_dtype} does not support the CPU backend." ) from espnet.utils.dynamic_import import dynamic_import if args.model_module is None: model_module = "espnet.nets." + args.backend + "_backend.e2e_st:E2E" else: model_module = args.model_module model_class = dynamic_import(model_module) model_class.add_arguments(parser) args = parser.parse_args(cmd_args) args.model_module = model_module if "chainer_backend" in args.model_module: args.backend = "chainer" if "pytorch_backend" in args.model_module: args.backend = "pytorch" # add version info in args args.version = __version__ # logging info if args.verbose > 0: logging.basicConfig( level=logging.INFO, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) else: logging.basicConfig( level=logging.WARN, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) logging.warning("Skip DEBUG/INFO messages") # If --ngpu is not given, # 1. if CUDA_VISIBLE_DEVICES is set, all visible devices # 2. if nvidia-smi exists, use all devices # 3. else ngpu=0 if args.ngpu is None: cvd = os.environ.get("CUDA_VISIBLE_DEVICES") if cvd is not None: ngpu = len(cvd.split(",")) else: logging.warning("CUDA_VISIBLE_DEVICES is not set.") try: p = ["nvidia-smi", "-L"], stdout=subprocess.PIPE, stderr=subprocess.PIPE ) except (subprocess.CalledProcessError, FileNotFoundError): ngpu = 0 else: ngpu = len(p.stderr.decode().split("\n")) - 1 args.ngpu = ngpu else: if args.ngpu != 1: logging.debug( "There are some bugs with multi-GPU processing in PyTorch 1.2+" + " (see" ) ngpu = args.ngpu"ngpu: {ngpu}") # display PYTHONPATH"python path = " + os.environ.get("PYTHONPATH", "(None)")) # set random seed"random seed = %d" % args.seed) random.seed(args.seed) np.random.seed(args.seed) # load dictionary for debug log if args.dict is not None: with open(args.dict, "rb") as f: dictionary = f.readlines() char_list = [entry.decode("utf-8").split(" ")[0] for entry in dictionary] char_list.insert(0, "<blank>") char_list.append("<eos>") args.char_list = char_list else: args.char_list = None # train"backend = " + args.backend) if args.backend == "pytorch": from import train train(args) else: raise ValueError("Only pytorch are supported.")
if __name__ == "__main__": main(sys.argv[1:])