#!/usr/bin/env python3
# Copyright 2021, Ludwig Kürzinger; Kamo Naoyuki
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Perform CTC segmentation to align utterances within audio files."""
import argparse
import logging
import sys
from pathlib import Path
from typing import List, Optional, TextIO, Union
import numpy as np
import soundfile
import torch
# imports for CTC segmentation
from ctc_segmentation import (
CtcSegmentationParameters,
ctc_segmentation,
determine_utterance_segments,
prepare_text,
prepare_token_list,
)
from typeguard import typechecked
from espnet2.tasks.asr import ASRTask
from espnet2.torch_utils.device_funcs import to_device
from espnet2.utils import config_argparse
from espnet2.utils.types import str2bool, str_or_none
# imports for inference
from espnet.utils.cli_utils import get_commandline_args
[docs]class CTCSegmentationTask:
"""Task object for CTC segmentation.
When formatted with str(·), this object returns
results in a kaldi-style segments file formatting.
The human-readable output can be configured with
the printing options.
Properties:
text: Utterance texts, separated by line. But without the utterance
name at the beginning of the line (as in kaldi-style text).
ground_truth_mat: Ground truth matrix (CTC segmentation).
utt_begin_indices: Utterance separator for the Ground truth matrix.
timings: Time marks of the corresponding chars.
state_list: Estimated alignment of chars/tokens.
segments: Calculated segments as: (start, end, confidence score).
config: CTC Segmentation configuration object.
name: Name of aligned audio file (Optional). If given, name is
considered when generating the text.
utt_ids: The list of utterance names (Optional). This list should
have the same length as the number of utterances.
lpz: CTC posterior log probabilities (Optional).
Properties for printing:
print_confidence_score: Includes the confidence score.
print_utterance_text: Includes utterance text.
"""
text = None
ground_truth_mat = None
utt_begin_indices = None
timings = None
char_probs = None
state_list = None
segments = None
config = None
done = False
# Optional
name = "utt"
utt_ids = None
lpz = None
# Printing
print_confidence_score = True
print_utterance_text = True
def __init__(self, **kwargs):
"""Initialize the module."""
self.set(**kwargs)
[docs] def set(self, **kwargs):
"""Update properties.
Args:
**kwargs: Key-value dict that contains all properties
with their new values. Unknown properties are ignored.
"""
for key in kwargs:
if (
not key.startswith("_")
and hasattr(self, key)
and kwargs[key] is not None
):
setattr(self, key, kwargs[key])
def __str__(self):
"""Return a kaldi-style ``segments`` file (string)."""
output = ""
num_utts = len(self.segments)
if self.utt_ids is None:
utt_names = [f"{self.name}_{i:04}" for i in range(num_utts)]
else:
# ensure correct mapping of segments to utterance ids
assert num_utts == len(self.utt_ids)
utt_names = self.utt_ids
for i, boundary in enumerate(self.segments):
# utterance name and file name
utt_entry = f"{utt_names[i]} {self.name} "
# segment start and end
utt_entry += f"{boundary[0]:.2f} {boundary[1]:.2f}"
# confidence score
if self.print_confidence_score:
utt_entry += f" {boundary[2]:3.4f}"
# utterance ground truth
if self.print_utterance_text:
utt_entry += f" {self.text[i]}"
output += utt_entry + "\n"
return output
[docs]class CTCSegmentation:
"""Align text to audio using CTC segmentation.
Usage:
Initialize with given ASR model and parameters.
If needed, parameters for CTC segmentation can be set with ``set_config(·)``.
Then call the instance as function to align text within an audio file.
Example:
>>> # example file included in the ESPnet repository
>>> import soundfile
>>> speech, fs = soundfile.read("test_utils/ctc_align_test.wav")
>>> # load an ASR model
>>> from espnet_model_zoo.downloader import ModelDownloader
>>> d = ModelDownloader()
>>> wsjmodel = d.download_and_unpack( "kamo-naoyuki/wsj" )
>>> # Apply CTC segmentation
>>> aligner = CTCSegmentation( **wsjmodel )
>>> text=["utt1 THE SALE OF THE HOTELS", "utt2 ON PROPERTY MANAGEMENT"]
>>> aligner.set_config( gratis_blank=True )
>>> segments = aligner( speech, text, fs=fs )
>>> print( segments )
utt1 utt 0.27 1.72 -0.1663 THE SALE OF THE HOTELS
utt2 utt 4.54 6.10 -4.9646 ON PROPERTY MANAGEMENT
On multiprocessing:
To parallelize the computation with multiprocessing, these three steps
can be separated:
(1) ``get_lpz``: obtain the lpz,
(2) ``prepare_segmentation_task``: prepare the task, and
(3) ``get_segments``: perform CTC segmentation.
Note that the function `get_segments` is a staticmethod and therefore
independent of an already initialized CTCSegmentation object.
References:
CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition
2020, Kürzinger, Winkelbauer, Li, Watzel, Rigoll
https://arxiv.org/abs/2007.09127
More parameters are described in https://github.com/lumaku/ctc-segmentation
"""
fs = 16000
samples_to_frames_ratio = None
time_stamps = "auto"
choices_time_stamps = ["auto", "fixed"]
text_converter = "tokenize"
choices_text_converter = ["tokenize", "classic"]
warned_about_misconfiguration = False
config = CtcSegmentationParameters()
@typechecked
def __init__(
self,
asr_train_config: Union[Path, str],
asr_model_file: Union[Path, str, None] = None,
fs: int = 16000,
ngpu: int = 0,
batch_size: int = 1,
dtype: str = "float32",
kaldi_style_text: bool = True,
text_converter: str = "tokenize",
time_stamps: str = "auto",
**ctc_segmentation_args,
):
"""Initialize the CTCSegmentation module.
Args:
asr_train_config: ASR model config file (yaml).
asr_model_file: ASR model file (pth).
fs: Sample rate of audio file.
ngpu: Number of GPUs. Set 0 for processing on CPU, set to 1 for
processing on GPU. Multi-GPU aligning is currently not
implemented. Default: 0.
batch_size: Currently, only batch size == 1 is implemented.
dtype: Data type used for inference. Set dtype according to
the ASR model.
kaldi_style_text: A kaldi-style text file includes the name of the
utterance at the start of the line. If True, the utterance name
is expected as first word at each line. If False, utterance
names are automatically generated. Set this option according to
your input data. Default: True.
text_converter: How CTC segmentation handles text.
"tokenize": Use ESPnet 2 preprocessing to tokenize the text.
"classic": The text is preprocessed as in ESPnet 1 which takes
token length into account. If the ASR model has longer tokens,
this option may yield better results. Default: "tokenize".
time_stamps: Choose the method how the time stamps are
calculated. While "fixed" and "auto" use both the sample rate,
the ratio of samples to one frame is either automatically
determined for each inference or fixed at a certain ratio that
is initially determined by the module, but can be changed via
the parameter ``samples_to_frames_ratio``. Recommended for
longer audio files: "auto".
**ctc_segmentation_args: Parameters for CTC segmentation.
"""
# Basic settings
if batch_size > 1:
raise NotImplementedError("Batch decoding is not implemented")
device = "cpu"
if ngpu == 1:
device = "cuda"
elif ngpu > 1:
logging.error("Multi-GPU not yet implemented.")
raise NotImplementedError("Only single GPU decoding is supported")
# Prepare ASR model
asr_model, asr_train_args = ASRTask.build_model_from_file(
asr_train_config, asr_model_file, device
)
asr_model.to(dtype=getattr(torch, dtype)).eval()
self.preprocess_fn = ASRTask.build_preprocess_fn(asr_train_args, False)
# Warn for nets with high memory consumption on long audio files
if hasattr(asr_model, "encoder"):
encoder_module = asr_model.encoder.__class__.__module__
else:
encoder_module = "Unknown"
logging.info(f"Encoder module: {encoder_module}")
logging.info(f"CTC module: {asr_model.ctc.__class__.__module__}")
if "rnn" not in encoder_module.lower():
logging.warning("No RNN model detected; memory consumption may be high.")
self.asr_model = asr_model
self.asr_train_args = asr_train_args
self.device = device
self.dtype = dtype
self.ctc = asr_model.ctc
self.kaldi_style_text = kaldi_style_text
self.token_list = asr_model.token_list
# Apply configuration
self.set_config(
fs=fs,
time_stamps=time_stamps,
kaldi_style_text=kaldi_style_text,
text_converter=text_converter,
**ctc_segmentation_args,
)
# last token "<sos/eos>", not needed
self.config.char_list = asr_model.token_list[:-1]
[docs] def set_config(self, **kwargs):
"""Set CTC segmentation parameters.
Parameters for timing:
time_stamps: Select method how CTC index duration is estimated, and
thus how the time stamps are calculated.
fs: Sample rate.
samples_to_frames_ratio: If you want to directly determine the
ratio of samples to CTC frames, set this parameter, and
set ``time_stamps`` to "fixed".
Note: If you want to calculate the time stamps as in
ESPnet 1, set this parameter to:
``subsampling_factor * frame_duration / 1000``.
Parameters for text preparation:
set_blank: Index of blank in token list. Default: 0.
replace_spaces_with_blanks: Inserts blanks between words, which is
useful for handling long pauses between words. Only used in
``text_converter="classic"`` preprocessing mode. Default: False.
kaldi_style_text: Determines whether the utterance name is expected
as fist word of the utterance. Set at module initialization.
text_converter: How CTC segmentation handles text.
Set at module initialization.
Parameters for alignment:
min_window_size: Minimum number of frames considered for a single
utterance. The current default value of 8000 corresponds to
roughly 4 minutes (depending on ASR model) and should be OK in
most cases. If your utterances are further apart, increase
this value, or decrease it for smaller audio files.
max_window_size: Maximum window size. It should not be necessary
to change this value.
gratis_blank: If True, the transition cost of blank is set to zero.
Useful for long preambles or if there are large unrelated segments
between utterances. Default: False.
Parameters for calculation of confidence score:
scoring_length: Block length to calculate confidence score. The
default value of 30 should be OK in most cases.
"""
# Parameters for timing
if "time_stamps" in kwargs:
if kwargs["time_stamps"] not in self.choices_time_stamps:
raise NotImplementedError(
f"Parameter ´time_stamps´ has to be one of "
f"{list(self.choices_time_stamps)}",
)
self.time_stamps = kwargs["time_stamps"]
if "fs" in kwargs:
self.fs = float(kwargs["fs"])
if "samples_to_frames_ratio" in kwargs:
self.samples_to_frames_ratio = float(kwargs["samples_to_frames_ratio"])
# Parameters for text preparation
if "set_blank" in kwargs:
assert isinstance(kwargs["set_blank"], int)
self.config.blank = kwargs["set_blank"]
if "replace_spaces_with_blanks" in kwargs:
self.config.replace_spaces_with_blanks = bool(
kwargs["replace_spaces_with_blanks"]
)
if "kaldi_style_text" in kwargs:
assert isinstance(kwargs["kaldi_style_text"], bool)
self.kaldi_style_text = kwargs["kaldi_style_text"]
if "text_converter" in kwargs:
if kwargs["text_converter"] not in self.choices_text_converter:
raise NotImplementedError(
f"Parameter ´text_converter´ has to be one of "
f"{list(self.choices_text_converter)}",
)
self.text_converter = kwargs["text_converter"]
# Parameters for alignment
if "min_window_size" in kwargs:
assert isinstance(kwargs["min_window_size"], int)
self.config.min_window_size = kwargs["min_window_size"]
if "max_window_size" in kwargs:
assert isinstance(kwargs["max_window_size"], int)
self.config.max_window_size = kwargs["max_window_size"]
if "gratis_blank" in kwargs:
self.config.blank_transition_cost_zero = bool(kwargs["gratis_blank"])
if (
self.config.blank_transition_cost_zero
and self.config.replace_spaces_with_blanks
and not self.warned_about_misconfiguration
):
logging.error(
"Blanks are inserted between words, and also the transition cost of"
" blank is zero. This configuration may lead to misalignments!"
)
self.warned_about_misconfiguration = True
# Parameter for calculation of confidence score
if "scoring_length" in kwargs:
assert isinstance(kwargs["scoring_length"], int)
self.config.score_min_mean_over_L = kwargs["scoring_length"]
[docs] def get_timing_config(self, speech_len=None, lpz_len=None):
"""Obtain parameters to determine time stamps."""
timing_cfg = {
"index_duration": self.config.index_duration,
}
# As the parameter ctc_index_duration vetoes the other
if self.time_stamps == "fixed":
# Initialize the value, if not yet available
if self.samples_to_frames_ratio is None:
ratio = self.estimate_samples_to_frames_ratio()
self.samples_to_frames_ratio = ratio
index_duration = self.samples_to_frames_ratio / self.fs
else:
assert self.time_stamps == "auto"
samples_to_frames_ratio = speech_len / lpz_len
index_duration = samples_to_frames_ratio / self.fs
timing_cfg["index_duration"] = index_duration
return timing_cfg
[docs] def estimate_samples_to_frames_ratio(self, speech_len=215040):
"""Determine the ratio of encoded frames to sample points.
This method helps to determine the time a single encoded frame occupies.
As the sample rate already gave the number of samples, only the ratio
of samples per encoded CTC frame are needed. This function estimates them by
doing one inference, which is only needed once.
Args:
speech_len: Length of randomly generated speech vector for single
inference. Default: 215040.
Returns:
samples_to_frames_ratio: Estimated ratio.
"""
random_input = torch.rand(speech_len)
lpz = self.get_lpz(random_input)
lpz_len = lpz.shape[0]
# Most frontends (DefaultFrontend, SlidingWindow) discard trailing data
lpz_len = lpz_len + 1
samples_to_frames_ratio = speech_len // lpz_len
return samples_to_frames_ratio
[docs] @torch.no_grad()
def get_lpz(self, speech: Union[torch.Tensor, np.ndarray]):
"""Obtain CTC posterior log probabilities for given speech data.
Args:
speech: Speech audio input.
Returns:
lpz: Numpy vector with CTC log posterior probabilities.
"""
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
# data: (Nsamples,) -> (1, Nsamples)
speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
# lengths: (1,)
lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
batch = {"speech": speech, "speech_lengths": lengths}
batch = to_device(batch, device=self.device)
# Encode input
enc, _ = self.asr_model.encode(**batch)
assert len(enc) == 1, len(enc)
# Apply ctc layer to obtain log character probabilities
lpz = self.ctc.log_softmax(enc).detach()
# Shape should be ( <time steps>, <classes> )
lpz = lpz.squeeze(0).cpu().numpy()
return lpz
def _split_text(self, text):
"""Convert text to list and extract utterance IDs."""
utt_ids = None
# Handle multiline strings
if isinstance(text, str):
text = text.splitlines()
# Remove empty lines
text = list(filter(len, text))
# Handle kaldi-style text format
if self.kaldi_style_text:
utt_ids_and_text = [utt.split(" ", 1) for utt in text]
# remove utterances with empty text
utt_ids_and_text = filter(lambda ui: len(ui) == 2, utt_ids_and_text)
utt_ids_and_text = list(utt_ids_and_text)
utt_ids = [utt[0] for utt in utt_ids_and_text]
text = [utt[1] for utt in utt_ids_and_text]
return utt_ids, text
[docs] def prepare_segmentation_task(self, text, lpz, name=None, speech_len=None):
"""Preprocess text, and gather text and lpz into a task object.
Text is pre-processed and tokenized depending on configuration.
If ``speech_len`` is given, the timing configuration is updated.
Text, lpz, and configuration is collected in a CTCSegmentationTask
object. The resulting object can be serialized and passed in a
multiprocessing computation.
A minimal amount of text processing is done, i.e., splitting the
utterances in ``text`` into a list and applying ``text_cleaner``.
It is recommended that you normalize the text beforehand, e.g.,
change numbers into their spoken equivalent word, remove special
characters, and convert UTF-8 characters to chars corresponding to
your ASR model dictionary.
The text is tokenized based on the ``text_converter`` setting:
The "tokenize" method is more efficient and the easiest for models
based on latin or cyrillic script that only contain the main chars,
["a", "b", ...] or for Japanese or Chinese ASR models with ~3000
short Kanji / Hanzi tokens.
The "classic" method improves the the accuracy of the alignments
for models that contain longer tokens, but with a greater complexity
for computation. The function scans for partial tokens which may
improve time resolution.
For example, the word "▁really" will be broken down into
``['▁', '▁r', '▁re', '▁real', '▁really']``. The alignment will be
based on the most probable activation sequence given by the network.
Args:
text: List or multiline-string with utterance ground truths.
lpz: Log CTC posterior probabilities obtained from the CTC-network;
numpy array shaped as ( <time steps>, <classes> ).
name: Audio file name. Choose a unique name, or the original audio
file name, to distinguish multiple audio files. Default: None.
speech_len: Number of sample points. If given, the timing
configuration is automatically derived from length of fs, length
of speech and length of lpz. If None is given, make sure the
timing parameters are correct, see time_stamps for reference!
Default: None.
Returns:
task: CTCSegmentationTask object that can be passed to
``get_segments()`` in order to obtain alignments.
"""
config = self.config
# Update timing parameters, if needed
if speech_len is not None:
lpz_len = lpz.shape[0]
timing_cfg = self.get_timing_config(speech_len, lpz_len)
config.set(**timing_cfg)
# `text` is needed in the form of a list.
utt_ids, text = self._split_text(text)
# Obtain utterance & label sequence from text
if self.text_converter == "tokenize":
# list of str --tokenize--> list of np.array
token_list = [
self.preprocess_fn("<dummy>", {"text": utt})["text"] for utt in text
]
# filter out any instances of the <unk> token
unk = config.char_list.index("<unk>")
token_list = [utt[utt != unk] for utt in token_list]
ground_truth_mat, utt_begin_indices = prepare_token_list(config, token_list)
else:
assert self.text_converter == "classic"
text = [self.preprocess_fn.text_cleaner(utt) for utt in text]
token_list = [
"".join(self.preprocess_fn.tokenizer.text2tokens(utt)) for utt in text
]
token_list = [utt.replace("<unk>", "") for utt in token_list]
ground_truth_mat, utt_begin_indices = prepare_text(config, token_list)
task = CTCSegmentationTask(
config=config,
name=name,
text=text,
ground_truth_mat=ground_truth_mat,
utt_begin_indices=utt_begin_indices,
utt_ids=utt_ids,
lpz=lpz,
)
return task
[docs] @staticmethod
@typechecked
def get_segments(task: CTCSegmentationTask):
"""Obtain segments for given utterance texts and CTC log posteriors.
Args:
task: CTCSegmentationTask object that contains ground truth and
CTC posterior probabilities.
Returns:
result: Dictionary with alignments. Combine this with the task
object to obtain a human-readable segments representation.
"""
assert task.config is not None
config = task.config
lpz = task.lpz
ground_truth_mat = task.ground_truth_mat
utt_begin_indices = task.utt_begin_indices
text = task.text
# Align using CTC segmentation
timings, char_probs, state_list = ctc_segmentation(
config, lpz, ground_truth_mat
)
# Obtain list of utterances with time intervals and confidence score
segments = determine_utterance_segments(
config, utt_begin_indices, char_probs, timings, text
)
# Store results
result = {
"name": task.name,
"timings": timings,
"char_probs": char_probs,
"state_list": state_list,
"segments": segments,
"done": True,
}
return result
@typechecked
def __call__(
self,
speech: Union[torch.Tensor, np.ndarray],
text: Union[List[str], str],
fs: Optional[int] = None,
name: Optional[str] = None,
) -> CTCSegmentationTask:
"""Align utterances.
Args:
speech: Audio file.
text: List or multiline-string with utterance ground truths.
fs: Sample rate in Hz. Optional, as this can be given when
the module is initialized.
name: Name of the file. Utterance names are derived from it.
Returns:
CTCSegmentationTask object with segments.
"""
if fs is not None:
self.set_config(fs=fs)
# Get log CTC posterior probabilities
lpz = self.get_lpz(speech)
# Conflate text & lpz & config as a segmentation task object
task = self.prepare_segmentation_task(text, lpz, name, speech.shape[0])
# Apply CTC segmentation
segments = self.get_segments(task)
task.set(**segments)
return task
[docs]@typechecked
def ctc_align(
log_level: Union[int, str],
asr_train_config: str,
asr_model_file: str,
audio: Path,
text: TextIO,
output: TextIO,
print_utt_text: bool = True,
print_utt_score: bool = True,
**kwargs,
):
"""Provide the scripting interface to align text to audio."""
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
# Ignore configuration values that are set to None (from parser).
kwargs = {k: v for (k, v) in kwargs.items() if v is not None}
# Prepare CTC segmentation module
model = {
"asr_train_config": asr_train_config,
"asr_model_file": asr_model_file,
}
aligner = CTCSegmentation(**model, **kwargs)
# load audio file
assert audio.name != ""
name = audio.stem
speech, fs = soundfile.read(str(audio))
# load text file
transcripts = text.read()
# perform inference and CTC segmentation
segments = aligner(speech=speech, text=transcripts, fs=fs, name=name)
# Write to "segments" file or stdout
segments.print_utterance_text = print_utt_text
segments.print_confidence_score = print_utt_score
segments_str = str(segments)
output.write(segments_str)
[docs]def get_parser():
"""Obtain an argument-parser for the script interface."""
parser = config_argparse.ArgumentParser(
description="ASR Decoding",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
# Note(kamo): Use '_' instead of '-' as separator.
# '-' is confusing if written in yaml.
parser.add_argument(
"--log_level",
type=lambda x: x.upper(),
default="INFO",
choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
help="The verbose level of logging",
)
parser.add_argument(
"--ngpu",
type=int,
default=0,
help="The number of gpus. 0 indicates CPU mode",
)
parser.add_argument(
"--dtype",
default="float32",
choices=["float16", "float32", "float64"],
help="Data type",
)
group = parser.add_argument_group("Model configuration related")
group.add_argument("--asr_train_config", type=str, required=True)
group.add_argument("--asr_model_file", type=str, required=True)
group = parser.add_argument_group("Text converter related")
group.add_argument(
"--token_type",
type=str_or_none,
default=None,
choices=["char", "bpe", None],
help="The token type for ASR model. "
"If not given, refers from the training args",
)
group.add_argument(
"--bpemodel",
type=str_or_none,
default=None,
help="The model path of sentencepiece. "
"If not given, refers from the training args",
)
group = parser.add_argument_group("CTC segmentation related")
group.add_argument(
"--fs",
type=int,
default=16000,
help="Sampling Frequency."
" The sampling frequency (in Hz) is needed to correctly determine the"
" starting and ending time of aligned segments.",
)
group.add_argument(
"--min_window_size",
type=int,
default=None,
help="Minimum window size considered for utterance.",
)
group.add_argument(
"--max_window_size",
type=int,
default=None,
help="Maximum window size considered for utterance.",
)
group.add_argument(
"--set_blank",
type=int,
default=None,
help="Index of model dictionary for blank token.",
)
group.add_argument(
"--gratis_blank",
type=str2bool,
default=False,
help="Set the transition cost of the blank token to zero. Audio sections"
" labeled with blank tokens can then be skipped without penalty. Useful"
" if there are unrelated audio segments between utterances.",
)
group.add_argument(
"--replace_spaces_with_blanks",
type=str2bool,
default=False,
help="Fill blanks in between words to better model pauses between words."
" This option is only active for `--text_converter classic`."
" Segments can be misaligned if this option is combined with"
" --gratis-blank.",
)
group.add_argument(
"--scoring_length",
type=int,
default=None,
help="Changes partitioning length L for calculation of the confidence score.",
)
group.add_argument(
"--time_stamps",
type=str,
default=CTCSegmentation.time_stamps,
choices=CTCSegmentation.choices_time_stamps,
help="Select method how CTC index duration is estimated, and"
" thus how the time stamps are calculated.",
)
group.add_argument(
"--text_converter",
type=str,
default=CTCSegmentation.text_converter,
choices=CTCSegmentation.choices_text_converter,
help="How CTC segmentation handles text.",
)
group = parser.add_argument_group("Input/output arguments")
group.add_argument(
"--kaldi_style_text",
type=str2bool,
default=True,
help="Assume that the input text file is kaldi-style formatted, i.e., the"
" utterance name is at the beginning of each line.",
)
group.add_argument(
"--print_utt_text",
type=str2bool,
default=True,
help="Include the utterance text in the segments output.",
)
group.add_argument(
"--print_utt_score",
type=str2bool,
default=True,
help="Include the confidence score in the segments output.",
)
group.add_argument(
"-a",
"--audio",
type=Path,
required=True,
help="Input audio file.",
)
group.add_argument(
"-t",
"--text",
type=argparse.FileType("r"),
required=True,
help="Input text file."
" Each line contains the ground truth of a single utterance."
" Kaldi-style text files include the name of the utterance as"
" the first word in the line.",
)
group.add_argument(
"-o",
"--output",
type=argparse.FileType("w"),
default="-",
help="Output in the form of a `segments` file."
" If not given, output is written to stdout.",
)
return parser
[docs]def main(cmd=None):
"""Parse arguments and start the alignment in ctc_align(·)."""
print(get_commandline_args(), file=sys.stderr)
parser = get_parser()
args = parser.parse_args(cmd)
kwargs = vars(args)
kwargs.pop("config", None)
ctc_align(**kwargs)
if __name__ == "__main__":
main()