Hui Zhang
3 years ago
parent
190f4cc4bc
commit
a4e27da64b
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"""End-to-end speech recognition model decoding script."""
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import configargparse
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import logging
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import os
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import random
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import sys
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import numpy as np
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from distutils.util import strtobool
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from deepspeech.training.cli import default_argument_parser
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# NOTE: you need this func to generate our sphinx doc
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def get_parser():
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"""Get default arguments."""
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parser = configargparse.ArgumentParser(
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description="Transcribe text from speech using "
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"a speech recognition model on one CPU or GPU",
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config_file_parser_class=configargparse.YAMLConfigFileParser,
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formatter_class=configargparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add(
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'--model-name',
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type=str,
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default='u2_kaldi',
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help='model name, e.g: deepspeech2, u2, u2_kaldi, u2_st')
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# general configuration
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parser.add("--config", is_config_file=True, help="Config file path")
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parser.add(
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"--config2",
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is_config_file=True,
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help="Second config file path that overwrites the settings in `--config`",
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)
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parser.add(
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"--config3",
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is_config_file=True,
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help="Third config file path that overwrites the settings "
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"in `--config` and `--config2`",
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)
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parser.add_argument("--ngpu", type=int, default=0, help="Number of GPUs")
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parser.add_argument(
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"--dtype",
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choices=("float16", "float32", "float64"),
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default="float32",
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help="Float precision (only available in --api v2)",
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)
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parser.add_argument("--debugmode", type=int, default=1, help="Debugmode")
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parser.add_argument("--seed", type=int, default=1, help="Random seed")
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parser.add_argument("--verbose", "-V", type=int, default=2, help="Verbose option")
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parser.add_argument(
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"--batchsize",
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type=int,
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default=1,
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help="Batch size for beam search (0: means no batch processing)",
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)
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parser.add_argument(
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"--preprocess-conf",
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type=str,
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default=None,
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help="The configuration file for the pre-processing",
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)
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parser.add_argument(
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"--api",
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default="v2",
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choices=["v2"],
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help="Beam search APIs "
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"v2: Experimental API. It supports any models that implements ScorerInterface.",
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)
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# task related
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parser.add_argument(
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"--recog-json", type=str, help="Filename of recognition data (json)"
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)
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parser.add_argument(
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"--result-label",
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type=str,
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required=True,
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help="Filename of result label data (json)",
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)
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# model (parameter) related
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parser.add_argument(
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"--model", type=str, required=True, help="Model file parameters to read"
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)
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parser.add_argument(
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"--model-conf", type=str, default=None, help="Model config file"
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)
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parser.add_argument(
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"--num-spkrs",
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type=int,
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default=1,
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choices=[1, 2],
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help="Number of speakers in the speech",
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)
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parser.add_argument(
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"--num-encs", default=1, type=int, help="Number of encoders in the model."
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)
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# search related
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parser.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses")
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parser.add_argument("--beam-size", type=int, default=1, help="Beam size")
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parser.add_argument("--penalty", type=float, default=0.0, help="Incertion penalty")
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parser.add_argument(
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"--maxlenratio",
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type=float,
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default=0.0,
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help="""Input length ratio to obtain max output length.
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If maxlenratio=0.0 (default), it uses a end-detect function
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to automatically find maximum hypothesis lengths.
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If maxlenratio<0.0, its absolute value is interpreted
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as a constant max output length""",
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)
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parser.add_argument(
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"--minlenratio",
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type=float,
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default=0.0,
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help="Input length ratio to obtain min output length",
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)
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parser.add_argument(
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"--ctc-weight", type=float, default=0.0, help="CTC weight in joint decoding"
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)
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parser.add_argument(
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"--weights-ctc-dec",
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type=float,
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action="append",
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help="ctc weight assigned to each encoder during decoding."
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"[in multi-encoder mode only]",
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)
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parser.add_argument(
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"--ctc-window-margin",
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type=int,
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default=0,
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help="""Use CTC window with margin parameter to accelerate
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CTC/attention decoding especially on GPU. Smaller magin
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makes decoding faster, but may increase search errors.
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If margin=0 (default), this function is disabled""",
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)
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# transducer related
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parser.add_argument(
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"--search-type",
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type=str,
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default="default",
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choices=["default", "nsc", "tsd", "alsd", "maes"],
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help="""Type of beam search implementation to use during inference.
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Can be either: default beam search ("default"),
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N-Step Constrained beam search ("nsc"), Time-Synchronous Decoding ("tsd"),
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Alignment-Length Synchronous Decoding ("alsd") or
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modified Adaptive Expansion Search ("maes").""",
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)
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parser.add_argument(
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"--nstep",
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type=int,
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default=1,
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help="""Number of expansion steps allowed in NSC beam search or mAES
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(nstep > 0 for NSC and nstep > 1 for mAES).""",
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)
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parser.add_argument(
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"--prefix-alpha",
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type=int,
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default=2,
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help="Length prefix difference allowed in NSC beam search or mAES.",
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)
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parser.add_argument(
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"--max-sym-exp",
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type=int,
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default=2,
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help="Number of symbol expansions allowed in TSD.",
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)
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parser.add_argument(
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"--u-max",
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type=int,
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default=400,
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help="Length prefix difference allowed in ALSD.",
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)
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parser.add_argument(
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"--expansion-gamma",
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type=float,
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default=2.3,
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help="Allowed logp difference for prune-by-value method in mAES.",
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)
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parser.add_argument(
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"--expansion-beta",
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type=int,
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default=2,
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help="""Number of additional candidates for expanded hypotheses
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selection in mAES.""",
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)
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parser.add_argument(
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"--score-norm",
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type=strtobool,
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nargs="?",
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default=True,
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help="Normalize final hypotheses' score by length",
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)
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parser.add_argument(
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"--softmax-temperature",
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type=float,
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default=1.0,
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help="Penalization term for softmax function.",
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)
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# rnnlm related
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parser.add_argument(
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"--rnnlm", type=str, default=None, help="RNNLM model file to read"
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)
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parser.add_argument(
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"--rnnlm-conf", type=str, default=None, help="RNNLM model config file to read"
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)
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parser.add_argument(
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"--word-rnnlm", type=str, default=None, help="Word RNNLM model file to read"
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)
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parser.add_argument(
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"--word-rnnlm-conf",
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type=str,
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default=None,
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help="Word RNNLM model config file to read",
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)
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parser.add_argument("--word-dict", type=str, default=None, help="Word list to read")
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parser.add_argument("--lm-weight", type=float, default=0.1, help="RNNLM weight")
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# ngram related
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parser.add_argument(
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"--ngram-model", type=str, default=None, help="ngram model file to read"
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)
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parser.add_argument("--ngram-weight", type=float, default=0.1, help="ngram weight")
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parser.add_argument(
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"--ngram-scorer",
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type=str,
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default="part",
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choices=("full", "part"),
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help="""if the ngram is set as a part scorer, similar with CTC scorer,
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ngram scorer only scores topK hypethesis.
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if the ngram is set as full scorer, ngram scorer scores all hypthesis
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the decoding speed of part scorer is musch faster than full one""",
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)
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# streaming related
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parser.add_argument(
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"--streaming-mode",
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type=str,
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default=None,
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choices=["window", "segment"],
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help="""Use streaming recognizer for inference.
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`--batchsize` must be set to 0 to enable this mode""",
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)
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parser.add_argument("--streaming-window", type=int, default=10, help="Window size")
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parser.add_argument(
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"--streaming-min-blank-dur",
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type=int,
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default=10,
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help="Minimum blank duration threshold",
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)
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parser.add_argument(
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"--streaming-onset-margin", type=int, default=1, help="Onset margin"
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)
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parser.add_argument(
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"--streaming-offset-margin", type=int, default=1, help="Offset margin"
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)
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# non-autoregressive related
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# Mask CTC related. See https://arxiv.org/abs/2005.08700 for the detail.
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parser.add_argument(
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"--maskctc-n-iterations",
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type=int,
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default=10,
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help="Number of decoding iterations."
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"For Mask CTC, set 0 to predict 1 mask/iter.",
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)
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parser.add_argument(
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"--maskctc-probability-threshold",
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type=float,
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default=0.999,
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help="Threshold probability for CTC output",
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)
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# quantize model related
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parser.add_argument(
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"--quantize-config",
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nargs="*",
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help="Quantize config list. E.g.: --quantize-config=[Linear,LSTM,GRU]",
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)
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parser.add_argument(
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"--quantize-dtype", type=str, default="qint8", help="Dtype dynamic quantize"
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)
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parser.add_argument(
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"--quantize-asr-model",
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type=bool,
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default=False,
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help="Quantize asr model",
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)
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parser.add_argument(
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"--quantize-lm-model",
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type=bool,
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default=False,
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help="Quantize lm model",
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)
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return parser
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def main(args):
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"""Run the main decoding function."""
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parser = get_parser()
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parser.add_argument(
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"--output", metavar="CKPT_DIR", help="path to save checkpoint.")
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parser.add_argument(
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"--checkpoint_path", type=str, help="path to load checkpoint")
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parser.add_argument(
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"--dict-path", type=str, help="path to load checkpoint")
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# parser = default_argument_parser(parser)
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args = parser.parse_args(args)
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if args.ngpu == 0 and args.dtype == "float16":
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raise ValueError(f"--dtype {args.dtype} does not support the CPU backend.")
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# logging info
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if args.verbose == 1:
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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elif args.verbose == 2:
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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else:
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logging.basicConfig(
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level=logging.WARN,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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logging.warning("Skip DEBUG/INFO messages")
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logging.info(args)
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# check CUDA_VISIBLE_DEVICES
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if args.ngpu > 0:
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cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
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if cvd is None:
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logging.warning("CUDA_VISIBLE_DEVICES is not set.")
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elif args.ngpu != len(cvd.split(",")):
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logging.error("#gpus is not matched with CUDA_VISIBLE_DEVICES.")
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sys.exit(1)
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# TODO(mn5k): support of multiple GPUs
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if args.ngpu > 1:
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logging.error("The program only supports ngpu=1.")
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sys.exit(1)
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# display PYTHONPATH
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logging.info("python path = " + os.environ.get("PYTHONPATH", "(None)"))
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# seed setting
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random.seed(args.seed)
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np.random.seed(args.seed)
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logging.info("set random seed = %d" % args.seed)
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# validate rnn options
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if args.rnnlm is not None and args.word_rnnlm is not None:
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logging.error(
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"It seems that both --rnnlm and --word-rnnlm are specified. "
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"Please use either option."
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)
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sys.exit(1)
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# recog
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if args.num_spkrs == 1:
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if args.num_encs == 1:
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# Experimental API that supports custom LMs
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if args.api == "v2":
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from deepspeech.decoders.recog import recog_v2
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recog_v2(args)
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else:
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raise ValueError("Only support --api v2")
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else:
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if args.api == "v2":
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raise NotImplementedError(
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f"--num-encs {args.num_encs} > 1 is not supported in --api v2"
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)
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elif args.num_spkrs == 2:
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raise ValueError("asr_mix not supported.")
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if __name__ == "__main__":
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main(sys.argv[1:])
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@ -0,0 +1,7 @@
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batchsize: 0
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beam-size: 60
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ctc-weight: 0.4
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lm-weight: 0.0
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maxlenratio: 0.0
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minlenratio: 0.0
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penalty: 0.0
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@ -0,0 +1,7 @@
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batchsize: 0
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beam-size: 60
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ctc-weight: 0.4
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lm-weight: 0.6
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maxlenratio: 0.0
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minlenratio: 0.0
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penalty: 0.0
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@ -0,0 +1,7 @@
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batchsize: 0
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beam-size: 60
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ctc-weight: 0.4
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lm-weight: 0.0
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maxlenratio: 0.0
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minlenratio: 0.0
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penalty: 0.0
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@ -0,0 +1,103 @@
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#!/bin/bash
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set -e
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expdir=exp
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datadir=data
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nj=32
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decode_config=conf/decode/decode.yaml
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lang_model=rnnlm.model.best
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lmexpdir=exp/train_rnnlm_pytorch_lm_transformer_cosine_batchsize32_lr1e-4_layer16_unigram5000_ngpu4/
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lmtag='nolm'
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recog_set="test-clean test-other dev-clean dev-other"
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recog_set="test-clean"
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# bpemode (unigram or bpe)
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nbpe=5000
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bpemode=unigram
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bpeprefix="data/bpe_${bpemode}_${nbpe}"
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bpemodel=${bpeprefix}.model
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if [ $# != 3 ];then
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echo "usage: ${0} config_path dict_path ckpt_path_prefix"
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exit -1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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config_path=$1
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dict=$2
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ckpt_prefix=$3
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ckpt_dir=$(dirname `dirname ${ckpt_prefix}`)
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echo "ckpt dir: ${ckpt_dir}"
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ckpt_tag=$(basename ${ckpt_prefix})
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echo "ckpt tag: ${ckpt_tag}"
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chunk_mode=false
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if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
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chunk_mode=true
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fi
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echo "chunk mode: ${chunk_mode}"
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echo "decode conf: ${decode_config}"
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# download language model
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#bash local/download_lm_en.sh
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#if [ $? -ne 0 ]; then
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# exit 1
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#fi
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pids=() # initialize pids
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for dmethd in join_ctc; do
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(
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echo "${dmethd} decoding"
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for rtask in ${recog_set}; do
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(
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echo "${rtask} dataset"
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decode_dir=${ckpt_dir}/decode/decode_${rtask/-/_}_${dmethd}_$(basename ${config_path%.*})_${lmtag}_${ckpt_tag}
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feat_recog_dir=${datadir}
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mkdir -p ${decode_dir}
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mkdir -p ${feat_recog_dir}
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# split data
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split_json.sh manifest.${rtask} ${nj}
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#### use CPU for decoding
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ngpu=0
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# set batchsize 0 to disable batch decoding
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${decode_cmd} JOB=1:${nj} ${decode_dir}/log/decode.JOB.log \
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python3 -u ${BIN_DIR}/recog.py \
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--api v2 \
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--config ${decode_config} \
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--ngpu ${ngpu} \
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--batchsize 0 \
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--checkpoint_path ${ckpt_prefix} \
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--dict-path ${dict} \
|
||||
--recog-json ${feat_recog_dir}/split${nj}/JOB/manifest.${rtask} \
|
||||
--result-label ${decode_dir}/data.JOB.json \
|
||||
--model-conf ${config_path} \
|
||||
--model ${ckpt_prefix}.pdparams
|
||||
|
||||
#--rnnlm ${lmexpdir}/${lang_model} \
|
||||
|
||||
score_sclite.sh --bpe ${nbpe} --bpemodel ${bpemodel} --wer false ${decode_dir} ${dict}
|
||||
|
||||
) &
|
||||
pids+=($!) # store background pids
|
||||
i=0; for pid in "${pids[@]}"; do wait ${pid} || ((++i)); done
|
||||
[ ${i} -gt 0 ] && echo "$0: ${i} background jobs are failed." || true
|
||||
done
|
||||
)
|
||||
done
|
||||
|
||||
echo "Finished"
|
||||
|
||||
exit 0
|
||||
Loading…
Reference in new issue