updating the repo with the loader script

atc_data_loader.py

@@ -0,0 +1,282 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# SPDX-FileCopyrightText: Copyright © <2022> Idiap Research Institute <[email protected]>
+#
+# SPDX-FileContributor: Juan Zuluaga-Gomez <[email protected]>
+#
+# SPDX-License-Identifier: MIT-License
+
+"""\
+Script for loading air traffic control (ATC) speech datasets for automatic speech recognition (ASR).
+This script has been designed for ATC datasets that are in Kaldi format
+
+Required files: text, wav.scp and segments files
+
+- Databases
+    - Training: 
+        - ATCOSIM, LDC-ATCC or, UWB-ATCC corpora.
+    - Testing:
+        - ATCO2-test-set-1h or 4h, LDC-ATCC or, UWB-ATCC corpora.
+"""
+
+import os
+import re
+
+import datasets
+import numpy as np
+import soundfile as sf
+from datasets.tasks import AutomaticSpeechRecognition
+
+_CITATION = """\
+@article{zuluaga2022atco2,
+  title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications},
+  author={Zuluaga-Gomez, Juan and Vesel{\'y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others},
+  journal={arXiv preprint arXiv:2211.04054},
+  year={2022}
+}    
+@article{zuluaga2022does,
+  title={How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
+  author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and others},
+  journal={2022 IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
+  year={2022}
+}
+@article{zuluagabertraffic,
+  title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications (submitted to @ SLT-2022)},
+  author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others},
+  journal={2022 IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
+  year={2022}
+}
+"""
+
+_DESCRIPTION = """\
+ATC speech DATASET. This DataLoader works with data in Kaldi format.
+    - We use the following files: text, segments and wav.scp 
+            - text --> utt_id transcript
+            - segments --> utt_id recording_id t_begin t_end
+            - wav.scp --> recording_id /path/to/wav/
+The default dataset is from ATCO2 project, a 1-hour sample: https://www.replaywell.com/atco2/download/ATCO2-ASRdataset-v1_beta.tgz
+"""
+
+_DATA_URL = "http://catalog.elra.info/en-us/repository/browse/ELRA-S0484/"
+
+_HOMEPAGE = "https://github.com/idiap/w2v2-air-traffic"
+
+logger = datasets.logging.get_logger(__name__)
+
+# Our models work with audio data at 16kHZ,
+_SAMPLING_RATE = int(16000)
+
+
+class ATCDataASRConfig(datasets.BuilderConfig):
+    """BuilderConfig for air traffic control datasets."""
+
+    def __init__(self, **kwargs):
+        """
+        Args:
+          data_dir: `string`, the path to the folder containing the files required to read: json or wav.scp
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(ATCDataASRConfig, self).__init__(**kwargs)
+
+
+class ATCDataASR(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_WRITER_BATCH_SIZE = 256
+    DEFAULT_CONFIG_NAME = "all"
+    BUILDER_CONFIGS = [
+        # TRAIN, DEV AND TEST DATASETS
+        ATCDataASRConfig(name="train", description="ATC train dataset."),
+        ATCDataASRConfig(name="dev", description="ATC dev dataset."),
+        ATCDataASRConfig(name="test", description="ATC test dataset."),
+        # UNSUPERVISED DATASETS
+        ATCDataASRConfig(name="unsupervised", description="ATC unsupervised dataset."),
+    ]
+
+    # provide some information about the Dataset we just gathered
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "file": datasets.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=_SAMPLING_RATE),
+                    "text": datasets.Value("string"),
+                    "segment_start_time": datasets.Value("float"),
+                    "segment_end_time": datasets.Value("float"),
+                    "duration": datasets.Value("float"),
+                }
+            ),
+            supervised_keys=("audio", "text"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            task_templates=[
+                AutomaticSpeechRecognition(
+                    audio_column="audio", transcription_column="text"
+                )
+            ],
+        )
+
+    def _split_generators(self, dlmanager):
+        """Returns SplitGenerators."""
+
+        split = self.config.name
+
+        # UNSUPERVISED set (used only for decoding)
+        if "unsupervised" in split:
+            split_name = datasets.Split.TEST
+        elif "test" in split or "dev" in split or "dummy" in split:
+            split_name = datasets.Split.TEST
+        # The last option left is: Train set
+        else:
+            split_name = datasets.Split.TRAIN
+
+        # you need to pass a data directory where the Kaldi folder is stored
+        filepath = self.config.data_dir
+
+        return [
+            datasets.SplitGenerator(
+                name=split_name,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": filepath,
+                    "split": split,
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """You need to pass a path with the kaldi data, the folder should have
+        audio: wav.scp,
+        transcripts: text,
+        timing information: segments
+        """
+
+        logger.info("Generating examples located in: %s", filepath)
+
+        text_file = os.path.join(filepath, "text")
+        wavscp = os.path.join(filepath, "wav.scp")
+        segments = os.path.join(filepath, "segments")
+
+        id_ = ""
+        text_dict, wav_dict = {}, {}
+        segments_dict, utt2wav_id = {}, {}
+
+        line = 0
+        # get the text file
+        with open(text_file) as text_f:
+            for line in text_f:
+                if len(line.split(" ")) > 1:
+                    id_, transcript = line.split(" ", maxsplit=1)
+                    transcript = _remove_special_characters(transcript)
+                    if len(transcript.split(" ")) == 0:
+                        continue
+                    if len(transcript) < 2:
+                        continue
+                    text_dict[id_] = transcript
+                else:  # line is empty
+                    # if unsupervised set, then it's normal. else, continue
+                    if not "test_unsup" in self.config.name:
+                        continue
+                    id_ = line.rstrip().split(" ")[0]
+                    text_dict[id_] = ""
+
+        # get wav.scp and load data into memory
+        with open(wavscp) as text_f:
+            for line in text_f:
+                if line:
+                    if len(line.split()) < 2:
+                        continue
+                    id_, wavpath = line.split(" ", maxsplit=1)
+                    # only selects the part that ends of wav, flac or sph
+                    wavpath = [
+                        x
+                        for x in wavpath.split(" ")
+                        if ".wav" in x or ".WAV" in x or ".flac" in x or ".sph" in x
+                    ][0].rstrip()
+
+                    # make the output
+                    segment, sampling_rate = sf.read(wavpath, dtype=np.int16)
+                    wav_dict[id_] = [wavpath.rstrip(), segment, sampling_rate]
+
+        # get segments dictionary
+        with open(segments) as text_f:
+            for line in text_f:
+                if line:
+                    if len(line.split()) < 4:
+                        continue
+                    id_, wavid_, start, end = line.rstrip().split(" ")
+                    segments_dict[id_] = start.rstrip(), end.rstrip()
+                    utt2wav_id[id_] = wavid_
+
+        for rec_id, text in text_dict.items():
+            if rec_id in utt2wav_id and rec_id in segments_dict:
+
+                # get audio data from memory and the path of the file
+                wavpath, segment, sampling_rate = wav_dict[utt2wav_id[rec_id]]
+                # get timing information
+                seg_start, seg_end = segments_dict[rec_id]
+                seg_start, seg_end = float(seg_start), float(seg_end)
+                duration = round((seg_end - seg_start), 3)
+
+                # get the samples, bytes, already cropping by segment,
+                samples = _extract_audio_segment(
+                    segment, sampling_rate, float(seg_start), float(seg_end)
+                )
+
+                # output data for given dataset
+                example = {
+                    "audio": {
+                        "path": wavpath,
+                        "array": samples,
+                        "sampling_rate": sampling_rate,
+                    },
+                    "id": rec_id,
+                    "file": wavpath,
+                    "text": text,
+                    "segment_start_time": format(float(seg_start), ".3f"),
+                    "segment_end_time": format(float(seg_end), ".3f"),
+                    "duration": format(float(duration), ".3f"),
+                }
+
+                yield rec_id, example
+
+
+def _remove_special_characters(text):
+    """Function to remove some special chars/symbols from the given transcript"""
+
+    text = text.split(" ")
+    # first remove words between [] and <>
+    text = " ".join(
+        [
+            x
+            for x in text
+            if "[" not in x and "]" not in x and "<" not in x and ">" not in x
+        ]
+    )
+
+    # regex with predifined symbols to ignore/remove,
+    chars_to_ignore_regex2 = '[\{\[\]\<\>\/\,\?\.\!\u00AC\;\:"\\%\\\]|[0-9]'
+
+    text = re.sub(chars_to_ignore_regex2, "", text).lower()
+    sentence = text.replace("\u2013", "-")
+    sentence = sentence.replace("\u2014", "-")
+    sentence = sentence.replace("\u2018", "'")
+    sentence = sentence.replace("\u201C", "")
+    sentence = sentence.replace("\u201D", "")
+    sentence = sentence.replace("ñ", "n")
+    sentence = sentence.replace(" - ", " ")
+    sentence = sentence.replace("-", "")
+    sentence = sentence.replace("'", " ")
+    
+    return sentence.lower().rstrip()
+
+
+def _extract_audio_segment(segment, sampling_rate, start_sec, end_sec):
+    """Extracts segment of audio samples (as an ndarray) from the given segment."""
+    # The dataset only contains mono audio.
+    start_sample = int(start_sec * sampling_rate)
+    end_sample = min(int(end_sec * sampling_rate), segment.shape[0])
+    samples = segment[start_sample:end_sample]
+    return samples