#!/usr/bin/env python3
# encoding: utf-8
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""End-to-end speech recognition model decoding script."""
import logging
import os
import random
import sys
import configargparse
import numpy as np
from espnet.utils.cli_utils import strtobool
# NOTE: you need this func to generate our sphinx doc
[docs]def get_parser():
"""Get default arguments."""
parser = configargparse.ArgumentParser(
description="Transcribe text from speech using "
"a speech recognition model on one CPU or GPU",
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", type=int, default=0, help="Number of GPUs")
parser.add_argument(
"--dtype",
choices=("float16", "float32", "float64"),
default="float32",
help="Float precision (only available in --api v2)",
)
parser.add_argument(
"--backend",
type=str,
default="chainer",
choices=["chainer", "pytorch"],
help="Backend library",
)
parser.add_argument("--debugmode", type=int, default=1, help="Debugmode")
parser.add_argument("--seed", type=int, default=1, help="Random seed")
parser.add_argument("--verbose", "-V", type=int, default=1, help="Verbose option")
parser.add_argument(
"--batchsize",
type=int,
default=1,
help="Batch size for beam search (0: means no batch processing)",
)
parser.add_argument(
"--preprocess-conf",
type=str,
default=None,
help="The configuration file for the pre-processing",
)
parser.add_argument(
"--api",
default="v1",
choices=["v1", "v2"],
help="Beam search APIs "
"v1: Default API. It only supports the ASRInterface.recognize method "
"and DefaultRNNLM. "
"v2: Experimental API. It supports any models that implements ScorerInterface.",
)
# task related
parser.add_argument(
"--recog-json", type=str, help="Filename of recognition data (json)"
)
parser.add_argument(
"--result-label",
type=str,
required=True,
help="Filename of result label data (json)",
)
# model (parameter) related
parser.add_argument(
"--model", type=str, required=True, help="Model file parameters to read"
)
parser.add_argument(
"--model-conf", type=str, default=None, help="Model config file"
)
parser.add_argument(
"--num-spkrs",
type=int,
default=1,
choices=[1, 2],
help="Number of speakers in the speech",
)
parser.add_argument(
"--num-encs", default=1, type=int, help="Number of encoders in the model."
)
# search related
parser.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses")
parser.add_argument("--beam-size", type=int, default=1, help="Beam size")
parser.add_argument("--penalty", type=float, default=0.0, help="Incertion penalty")
parser.add_argument(
"--maxlenratio",
type=float,
default=0.0,
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.
If maxlenratio<0.0, its absolute value is interpreted
as a constant max output length""",
)
parser.add_argument(
"--minlenratio",
type=float,
default=0.0,
help="Input length ratio to obtain min output length",
)
parser.add_argument(
"--ctc-weight", type=float, default=0.0, help="CTC weight in joint decoding"
)
parser.add_argument(
"--weights-ctc-dec",
type=float,
action="append",
help="ctc weight assigned to each encoder during decoding."
"[in multi-encoder mode only]",
)
parser.add_argument(
"--ctc-window-margin",
type=int,
default=0,
help="""Use CTC window with margin parameter to accelerate
CTC/attention decoding especially on GPU. Smaller magin
makes decoding faster, but may increase search errors.
If margin=0 (default), this function is disabled""",
)
# transducer related
parser.add_argument(
"--search-type",
type=str,
default="default",
choices=["default", "nsc", "tsd", "alsd", "maes"],
help="""Type of beam search implementation to use during inference.
Can be either: default beam search ("default"),
N-Step Constrained beam search ("nsc"), Time-Synchronous Decoding ("tsd"),
Alignment-Length Synchronous Decoding ("alsd") or
modified Adaptive Expansion Search ("maes").""",
)
parser.add_argument(
"--nstep",
type=int,
default=1,
help="""Number of expansion steps allowed in NSC beam search or mAES
(nstep > 0 for NSC and nstep > 1 for mAES).""",
)
parser.add_argument(
"--prefix-alpha",
type=int,
default=2,
help="Length prefix difference allowed in NSC beam search or mAES.",
)
parser.add_argument(
"--max-sym-exp",
type=int,
default=2,
help="Number of symbol expansions allowed in TSD.",
)
parser.add_argument(
"--u-max",
type=int,
default=400,
help="Length prefix difference allowed in ALSD.",
)
parser.add_argument(
"--expansion-gamma",
type=float,
default=2.3,
help="Allowed logp difference for prune-by-value method in mAES.",
)
parser.add_argument(
"--expansion-beta",
type=int,
default=2,
help="""Number of additional candidates for expanded hypotheses
selection in mAES.""",
)
parser.add_argument(
"--score-norm",
type=strtobool,
nargs="?",
default=True,
help="Normalize final hypotheses' score by length",
)
parser.add_argument(
"--softmax-temperature",
type=float,
default=1.0,
help="Penalization term for softmax function.",
)
# rnnlm related
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(
"--word-rnnlm", type=str, default=None, help="Word RNNLM model file to read"
)
parser.add_argument(
"--word-rnnlm-conf",
type=str,
default=None,
help="Word RNNLM model config file to read",
)
parser.add_argument("--word-dict", type=str, default=None, help="Word list to read")
parser.add_argument("--lm-weight", type=float, default=0.1, help="RNNLM weight")
# ngram related
parser.add_argument(
"--ngram-model", type=str, default=None, help="ngram model file to read"
)
parser.add_argument("--ngram-weight", type=float, default=0.1, help="ngram weight")
parser.add_argument(
"--ngram-scorer",
type=str,
default="part",
choices=("full", "part"),
help="""if the ngram is set as a part scorer, similar with CTC scorer,
ngram scorer only scores topK hypethesis.
if the ngram is set as full scorer, ngram scorer scores all hypthesis
the decoding speed of part scorer is musch faster than full one""",
)
# streaming related
parser.add_argument(
"--streaming-mode",
type=str,
default=None,
choices=["window", "segment"],
help="""Use streaming recognizer for inference.
`--batchsize` must be set to 0 to enable this mode""",
)
parser.add_argument("--streaming-window", type=int, default=10, help="Window size")
parser.add_argument(
"--streaming-min-blank-dur",
type=int,
default=10,
help="Minimum blank duration threshold",
)
parser.add_argument(
"--streaming-onset-margin", type=int, default=1, help="Onset margin"
)
parser.add_argument(
"--streaming-offset-margin", type=int, default=1, help="Offset margin"
)
# non-autoregressive related
# Mask CTC related. See https://arxiv.org/abs/2005.08700 for the detail.
parser.add_argument(
"--maskctc-n-iterations",
type=int,
default=10,
help="Number of decoding iterations."
"For Mask CTC, set 0 to predict 1 mask/iter.",
)
parser.add_argument(
"--maskctc-probability-threshold",
type=float,
default=0.999,
help="Threshold probability for CTC output",
)
# quantize model related
parser.add_argument(
"--quantize-config",
nargs="*",
help="""Config for dynamic quantization provided as a list of modules,
separated by a comma. E.g.: --quantize-config=[Linear,LSTM,GRU].
Each specified module should be an attribute of 'torch.nn', e.g.:
torch.nn.Linear, torch.nn.LSTM, torch.nn.GRU, ...""",
)
parser.add_argument(
"--quantize-dtype",
type=str,
default="qint8",
choices=["float16", "qint8"],
help="Dtype for dynamic quantization.",
)
parser.add_argument(
"--quantize-asr-model",
type=bool,
default=False,
help="Apply dynamic quantization to ASR model.",
)
parser.add_argument(
"--quantize-lm-model",
type=bool,
default=False,
help="Apply dynamic quantization to LM.",
)
return parser
[docs]def main(args):
"""Run the main decoding function."""
parser = get_parser()
args = parser.parse_args(args)
if args.ngpu == 0 and args.dtype == "float16":
raise ValueError(f"--dtype {args.dtype} does not support the CPU backend.")
# logging info
if args.verbose == 1:
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
elif args.verbose == 2:
logging.basicConfig(
level=logging.DEBUG,
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")
# check CUDA_VISIBLE_DEVICES
if args.ngpu > 0:
cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
if cvd is None:
logging.warning("CUDA_VISIBLE_DEVICES is not set.")
elif args.ngpu != len(cvd.split(",")):
logging.error("#gpus is not matched with CUDA_VISIBLE_DEVICES.")
sys.exit(1)
# TODO(mn5k): support of multiple GPUs
if args.ngpu > 1:
logging.error("The program only supports ngpu=1.")
sys.exit(1)
# display PYTHONPATH
logging.info("python path = " + os.environ.get("PYTHONPATH", "(None)"))
# seed setting
random.seed(args.seed)
np.random.seed(args.seed)
logging.info("set random seed = %d" % args.seed)
# validate rnn options
if args.rnnlm is not None and args.word_rnnlm is not None:
logging.error(
"It seems that both --rnnlm and --word-rnnlm are specified. "
"Please use either option."
)
sys.exit(1)
# recog
logging.info("backend = " + args.backend)
if args.num_spkrs == 1:
if args.backend == "chainer":
from espnet.asr.chainer_backend.asr import recog
recog(args)
elif args.backend == "pytorch":
if args.num_encs == 1:
# Experimental API that supports custom LMs
if args.api == "v2":
from espnet.asr.pytorch_backend.recog import recog_v2
recog_v2(args)
else:
from espnet.asr.pytorch_backend.asr import recog
if args.dtype != "float32":
raise NotImplementedError(
f"`--dtype {args.dtype}` is only available with `--api v2`"
)
recog(args)
else:
if args.api == "v2":
raise NotImplementedError(
f"--num-encs {args.num_encs} > 1 is not supported in --api v2"
)
else:
from espnet.asr.pytorch_backend.asr import recog
recog(args)
else:
raise ValueError("Only chainer and pytorch are supported.")
elif args.num_spkrs == 2:
if args.backend == "pytorch":
from espnet.asr.pytorch_backend.asr_mix import recog
recog(args)
else:
raise ValueError("Only pytorch is supported.")
if __name__ == "__main__":
main(sys.argv[1:])