Source code for espnet2.bin.asr_align

#!/usr/bin/env python3
# Copyright 2021, Ludwig Kürzinger; Kamo Naoyuki
#  Apache 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 (
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"{}_{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]} {} " # 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 ="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 More parameters are described in """ 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 ), 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""Encoder module: {encoder_module}")"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":, "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 != "" name = audio.stem speech, fs = # load text file transcripts = # 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()