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ami-ihm-kaldi-chunked.py

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+# 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)
+        if name not in {"dev", "test"}:
+            self.subsets_to_download = _SUBSETS[: _SUBSETS.index(name) + 1]
+        else:
+            self.subsets_to_download = (name,)
+
+
+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 = [_AUDIO_ARCHIVE_URL.format(subset=self.name, split="train", _id=m) for m in _TRAIN_SAMPLE_IDS]
+        dev_audio_files = [_AUDIO_ARCHIVE_URL.format(subset=self.name, split="dev", _id=m) for m in _VALIDATION_SAMPLE_IDS]
+        eval_audio_files = [_AUDIO_ARCHIVE_URL.format(subset=self.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"))
+
+        import ipdb; ipdb.set_trace()
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"audio": train_audio_archives, "annotation": train_annotation},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"audio": dev_audio_archives, "annotation": dev_annotation},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"audio": eval_audio_archives, "annotation": eval_annotation},
+            ),
+        ]
+
+    def _generate_examples(self, audio, annotation):
+        import ipdb; ipdb.set_trace()
+#        assert len(audio_archives_iterators) == len(meta_paths)
+#        if local_audio_archives_paths:
+#            assert len(audio_archives_iterators) == len(local_audio_archives_paths)
+#
+#        for i, (meta_path, audio_archive_iterator) in enumerate(
+#            zip(meta_paths, audio_archives_iterators)
+#        ):
+#            meta_dict = dict()
+#            with open(meta_path) as csvfile:
+#                meta_csv = csv.DictReader(csvfile)
+#                for line in meta_csv:
+#                    meta_dict[line["sid"]] = line
+#
+#            for audio_path_in_archive, audio_file in audio_archive_iterator:
+                # `audio_path_in_archive` is like "dev_chunks_0000/YOU1000000029_S0000095.wav"
+#                audio_filename = os.path.split(audio_path_in_archive)[1]
+#                audio_id = audio_filename.split(".wav")[0]
+#                audio_meta = meta_dict[audio_id]
+#                audio_meta["segment_id"] = audio_meta.pop("sid")
+#                audio_meta["original_full_path"] = audio_meta.pop("path")
+#                audio_meta["text"] = audio_meta.pop("text_tn")
+#                audio_meta["audio_id"] = audio_meta.pop("aid")
+#                if not audio_meta["category"]:
+#                    audio_meta["category"] = "N/A"
+#
+#                path = (
+#                    os.path.join(local_audio_archives_paths[i], audio_path_in_archive)
+#                    if local_audio_archives_paths
+#                    else audio_path_in_archive
+#                )
+
+#                yield audio_id, {
+#                    "audio": {"path": path, "bytes": audio_file.read()},
+#                    **{
+#                        feature: value
+#                        for feature, value in audio_meta.items()
+#                        if feature in self.info.features
+#                    },
+#                }