Delete loading script auxiliary file

ami-ihm-kaldi-chunked.py

@@ -1,382 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
-labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
-and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
-and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
-sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
-for speech recognition training, and to filter out segments with low-quality transcription. For system training,
-GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
-For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage,
-and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand,
-are re-processed by professional human transcribers to ensure high transcription quality.
-"""
-
-import csv
-import os
-
-import datasets
-
-_CITATION = """\
-@article{DBLP:journals/corr/abs-2106-06909,
-  author    = {Guoguo Chen and
-               Shuzhou Chai and
-               Guanbo Wang and
-               Jiayu Du and
-               Wei{-}Qiang Zhang and
-               Chao Weng and
-               Dan Su and
-               Daniel Povey and
-               Jan Trmal and
-               Junbo Zhang and
-               Mingjie Jin and
-               Sanjeev Khudanpur and
-               Shinji Watanabe and
-               Shuaijiang Zhao and
-               Wei Zou and
-               Xiangang Li and
-               Xuchen Yao and
-               Yongqing Wang and
-               Yujun Wang and
-               Zhao You and
-               Zhiyong Yan},
-  title     = {GigaSpeech: An Evolving, Multi-domain {ASR} Corpus with 10, 000 Hours
-               of Transcribed Audio},
-  journal   = {CoRR},
-  volume    = {abs/2106.06909},
-  year      = {2021},
-  url       = {https://arxiv.org/abs/2106.06909},
-  eprinttype = {arXiv},
-  eprint    = {2106.06909},
-  timestamp = {Wed, 29 Dec 2021 14:29:26 +0100},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-06909.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-"""
-
-_DESCRIPTION = """\
-GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
-labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
-and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
-and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
-sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
-for speech recognition training, and to filter out segments with low-quality transcription. For system training,
-GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
-For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage,
-and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand,
-are re-processed by professional human transcribers to ensure high transcription quality.
-"""
-
-_HOMEPAGE = "https://groups.inf.ed.ac.uk/ami/corpus/"
-
-_LICENSE = "CC BY 4.0"
-
-_TRAIN_SAMPLE_IDS = [
-    "EN2001a",
-    "EN2001b",
-    "EN2001d",
-    "EN2001e",
-    "EN2003a",
-    "EN2004a",
-    "EN2005a",
-    "EN2006a",
-    "EN2006b",
-    "EN2009b",
-    "EN2009c",
-    "EN2009d",
-    "ES2002a",
-    "ES2002b",
-    "ES2002c",
-    "ES2002d",
-    "ES2003a",
-    "ES2003b",
-    "ES2003c",
-    "ES2003d",
-    "ES2005a",
-    "ES2005b",
-    "ES2005c",
-    "ES2005d",
-    "ES2006a",
-    "ES2006b",
-    "ES2006c",
-    "ES2006d",
-    "ES2007a",
-    "ES2007b",
-    "ES2007c",
-    "ES2007d",
-    "ES2008a",
-    "ES2008b",
-    "ES2008c",
-    "ES2008d",
-    "ES2009a",
-    "ES2009b",
-    "ES2009c",
-    "ES2009d",
-    "ES2010a",
-    "ES2010b",
-    "ES2010c",
-    "ES2010d",
-    "ES2012a",
-    "ES2012b",
-    "ES2012c",
-    "ES2012d",
-    "ES2013a",
-    "ES2013b",
-    "ES2013c",
-    "ES2013d",
-    "ES2014a",
-    "ES2014b",
-    "ES2014c",
-    "ES2014d",
-    "ES2015a",
-    "ES2015b",
-    "ES2015c",
-    "ES2015d",
-    "ES2016a",
-    "ES2016b",
-    "ES2016c",
-    "ES2016d",
-    "IB4005",
-    "IN1001",
-    "IN1002",
-    "IN1005",
-    "IN1007",
-    "IN1008",
-    "IN1009",
-    "IN1012",
-    "IN1013",
-    "IN1014",
-    "IN1016",
-    "IS1000a",
-    "IS1000b",
-    "IS1000c",
-    "IS1000d",
-    "IS1001a",
-    "IS1001b",
-    "IS1001c",
-    "IS1001d",
-    "IS1002b",
-    "IS1002c",
-    "IS1002d",
-    "IS1003a",
-    "IS1003b",
-    "IS1003c",
-    "IS1003d",
-    "IS1004a",
-    "IS1004b",
-    "IS1004c",
-    "IS1004d",
-    "IS1005a",
-    "IS1005b",
-    "IS1005c",
-    "IS1006a",
-    "IS1006b",
-    "IS1006c",
-    "IS1006d",
-    "IS1007a",
-    "IS1007b",
-    "IS1007c",
-    "IS1007d",
-    "TS3005a",
-    "TS3005b",
-    "TS3005c",
-    "TS3005d",
-    "TS3006a",
-    "TS3006b",
-    "TS3006c",
-    "TS3006d",
-    "TS3007a",
-    "TS3007b",
-    "TS3007c",
-    "TS3007d",
-    "TS3008a",
-    "TS3008b",
-    "TS3008c",
-    "TS3008d",
-    "TS3009a",
-    "TS3009b",
-    "TS3009c",
-    "TS3009d",
-    "TS3010a",
-    "TS3010b",
-    "TS3010c",
-    "TS3010d",
-    "TS3011a",
-    "TS3011b",
-    "TS3011c",
-    "TS3011d",
-    "TS3012a",
-    "TS3012b",
-    "TS3012c",
-    "TS3012d",
-]
-
-_VALIDATION_SAMPLE_IDS = [
-    "ES2011a",
-    "ES2011c",
-    "IB4001",
-    "IB4003",
-    "IB4010",
-    "IS1008a",
-    "IS1008c",
-    "TS3004a",
-    "TS3004c",
-    "ES2011b",
-    "ES2011d",
-    "IB4002",
-    "IB4004",
-    "IB4011",
-    "IS1008b",
-    "IS1008d",
-    "TS3004b",
-    "TS3004d",
-]
-
-_EVAL_SAMPLE_IDS = [
-    "EN2002a",
-    "EN2002b",
-    "EN2002c",
-    "EN2002d",
-    "ES2004a",
-    "ES2004b",
-    "ES2004c",
-    "ES2004d",
-    "IS1009a",
-    "IS1009b",
-    "IS1009c",
-    "IS1009d",
-    "TS3003a",
-    "TS3003b",
-    "TS3003c",
-    "TS3003d",
-]
-
-_SUBSETS = ("ihm",)
-
-_BASE_DATA_URL = "https://huggingface.co/datasets/patrickvonplaten/ami-ihm-kaldi-chunked/resolve/main/"
-
-_AUDIO_ARCHIVE_URL = _BASE_DATA_URL + "audio/{subset}/{split}/{_id}.tar.gz"
-
-_ANNOTATIONS_ARCHIVE_URL = _BASE_DATA_URL + "annotations/{split}/text"
-
-logger = datasets.utils.logging.get_logger(__name__)
-
-
-class AMIConfig(datasets.BuilderConfig):
-    """BuilderConfig for AMI."""
-
-    def __init__(self, name, *args, **kwargs):
-        """BuilderConfig for AMI"""
-        super().__init__(name=name, *args, **kwargs)
-
-
-class AMI(datasets.GeneratorBasedBuilder):
-    """
-    GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
-    labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
-    and unsupervised training (this implementation contains only labelled data for now).
-    Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
-    and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
-    sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
-    for speech recognition training, and to filter out segments with low-quality transcription. For system training,
-    GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
-    For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage,
-    and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand,
-    are re-processed by professional human transcribers to ensure high transcription quality.
-    """
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        AMIConfig(name=subset) for subset in _SUBSETS
-    ]
-
-    DEFAULT_WRITER_BATCH_SIZE = 128
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "segment_id": datasets.Value("string"),
-                "audio_id": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "audio": datasets.Audio(sampling_rate=16_000),
-                "begin_time": datasets.Value("float32"),
-                "end_time": datasets.Value("float32"),
-                "microphone_id": datasets.Value("string"),
-                "speaker_id": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        train_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="train", _id=m) for m in _TRAIN_SAMPLE_IDS}
-        dev_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="dev", _id=m) for m in _VALIDATION_SAMPLE_IDS}
-        eval_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="eval", _id=m) for m in _EVAL_SAMPLE_IDS}
-
-        train_audio_archives = dl_manager.download_and_extract(train_audio_files)
-        dev_audio_archives = dl_manager.download_and_extract(dev_audio_files)
-        eval_audio_archives = dl_manager.download_and_extract(eval_audio_files)
-
-        train_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="train"))
-        dev_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="dev"))
-        eval_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="eval"))
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"audio": train_audio_archives, "annotation": train_annotation, "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"audio": dev_audio_archives, "annotation": dev_annotation, "split": "dev"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"audio": eval_audio_archives, "annotation": eval_annotation, "split": "eval"},
-            ),
-        ]
-
-    def _generate_examples(self, audio, annotation, split):
-        # open annotation file
-        with open(annotation, "r", encoding="utf-8") as f:
-            transcriptions = {}
-            for line in f.readlines():
-                line_items = line.strip().split()
-                _id = line_items[0]
-                text = " ".join(line_items[1:])
-                _, segment_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_")
-
-                transcriptions[_id] = {
-                    "audio_id": _id,
-                    "segment_id": segment_id,
-                    "text": text,
-                    "begin_time": int(begin_time) / 100,
-                    "end_time": int(end_time) / 100,
-                    "microphone_id": microphone_id,
-                    "speaker_id": speaker_id,
-                }
-
-        for _audio_id, (transcription_id, result) in enumerate(transcriptions.items()):
-            folder_id = result["segment_id"]
-            file_name = "_".join([split, transcription_id.lower()]) + ".wav"
-            audio_file = os.path.join(audio[folder_id], folder_id, file_name)
-            result["audio"] = audio_file
-            yield _audio_id, result