Initial commit with my dataset

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

_common_voice.py

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+# coding=utf-8
+# Copyright 2022 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.
+""" Common Voice Dataset"""
+
+
+import csv
+import os
+import json
+
+import datasets
+from datasets.utils.py_utils import size_str
+from tqdm import tqdm
+
+
+_CITATION = """\
+@inproceedings{commonvoice:2020,
+  author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
+  title = {Common Voice: A Massively-Multilingual Speech Corpus},
+  booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
+  pages = {4211--4215},
+  year = 2020
+}
+"""
+
+_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
+
+_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
+
+# TODO: change "streaming" to "main" after merge!
+_BASE_URL = "https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0/resolve/main/"
+
+_AUDIO_URL = _BASE_URL + "audio/{lang}/{split}.tar"
+
+_TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}.tsv"
+
+class CommonVoiceConfig(datasets.BuilderConfig):
+    """BuilderConfig for CommonVoice."""
+
+    def __init__(self, name, version, **kwargs):
+        self.language = kwargs.pop("language", None)
+        description = (
+            f"This is a test. "
+        )
+        super(CommonVoiceConfig, self).__init__(
+            name=name,
+            version=datasets.Version(version),
+            description=description,
+            **kwargs,
+        )
+
+
+class CommonVoice(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    BUILDER_CONFIGS = [
+        CommonVoiceConfig(
+            name=lang,
+            version=STATS["version"],
+            language=LANGUAGES[lang],
+            release_date=STATS["date"],
+            num_clips=lang_stats["clips"],
+            num_speakers=lang_stats["users"],
+            validated_hr=float(lang_stats["validHrs"]) if lang_stats["validHrs"] else None,
+            total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None,
+            size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None,
+        )
+        for lang, lang_stats in STATS["locales"].items()
+    ]
+
+    def _info(self):
+        total_languages = len(STATS["locales"])
+        total_valid_hours = STATS["totalValidHrs"]
+        description = (
+            "Common Voice is Mozilla's initiative to help teach machines how real people speak. "
+            f"The dataset currently consists of {total_valid_hours} validated hours of speech "
+            f" in {total_languages} languages, but more voices and languages are always added."
+        )
+        features = datasets.Features(
+            {
+                "client_id": datasets.Value("string"),
+                "path": datasets.Value("string"),
+                "audio": datasets.features.Audio(sampling_rate=48_000),
+                "sentence": datasets.Value("string"),
+                "up_votes": datasets.Value("int64"),
+                "down_votes": datasets.Value("int64"),
+                "age": datasets.Value("string"),
+                "gender": datasets.Value("string"),
+                "accent": datasets.Value("string"),
+                "locale": datasets.Value("string"),
+                "segment": datasets.Value("string"),
+                "variant": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=description,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            version=self.config.version,
+        )
+
+    def _split_generators(self, dl_manager):
+        lang = self.config.name
+        n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL)
+        with open(n_shards_path, encoding="utf-8") as f:
+            n_shards = json.load(f)
+
+        audio_urls = {}
+        splits = ("train", "dev", "test", "other", "invalidated")
+        for split in splits:
+            audio_urls[split] = [
+                _AUDIO_URL.format(lang=lang, split=split, shard_idx=i) for i in range(n_shards[lang][split])
+            ]
+        archive_paths = dl_manager.download(audio_urls)
+        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+
+        meta_urls = {split: _TRANSCRIPT_URL.format(lang=lang, split=split) for split in splits}
+        meta_paths = dl_manager.download_and_extract(meta_urls)
+
+        split_generators = []
+        split_names = {
+            "train": datasets.Split.TRAIN,
+            "dev": datasets.Split.VALIDATION,
+            "test": datasets.Split.TEST,
+        }
+        for split in splits:
+            split_generators.append(
+                datasets.SplitGenerator(
+                    name=split_names.get(split, split),
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
+                        "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
+                        "meta_path": meta_paths[split],
+                    },
+                ),
+            )
+
+        return split_generators
+
+    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
+        data_fields = list(self._info().features.keys())
+        metadata = {}
+        with open(meta_path, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for row in tqdm(reader, desc="Reading metadata..."):
+                if not row["path"].endswith(".mp3"):
+                    row["path"] += ".mp3"
+                # accent -> accents in CV 8.0
+                if "accents" in row:
+                    row["accent"] = row["accents"]
+                    del row["accents"]
+                # if data is incomplete, fill with empty values
+                for field in data_fields:
+                    if field not in row:
+                        row[field] = ""
+                metadata[row["path"]] = row
+
+        for i, audio_archive in enumerate(archives):
+            for path, file in audio_archive:
+                _, filename = os.path.split(path)
+                if filename in metadata:
+                    result = dict(metadata[filename])
+                    # set the audio feature and the path to the extracted file
+                    path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path
+                    result["audio"] = {"path": path, "bytes": file.read()}
+                    result["path"] = path
+                    yield path, result

_giga_speech.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 = """\
+# """
+
+# _DESCRIPTION = """\
+# """
+
+# _HOMEPAGE = "https://github.com/SpeechColab/GigaSpeech"
+
+# _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
+
+# _CATEGORIES = (
+#     "People  and  Blogs",
+#     "Business",
+#     "Nonprofits  and  Activism",
+#     "Crime",
+#     "History",
+#     "Pets  and  Animals",
+#     "News and Politics",
+#     "Travel and Events",
+#     "Kids and Family",
+#     "Leisure",
+#     "N/A",
+#     "Comedy",
+#     "News  and  Politics",
+#     "Sports",
+#     "Arts",
+#     "Science  and  Technology",
+#     "Autos  and  Vehicles",
+#     "Science and Technology",
+#     "People and Blogs",
+#     "Music",
+#     "Society and Culture",
+#     "Education",
+#     "Howto  and  Style",
+#     "Film  and  Animation",
+#     "Gaming",
+#     "Entertainment",
+#     "Travel  and  Events",
+#     "Health and Fitness",
+#     "audiobook",
+# )
+
+# _SOURCES = ("audiobook", "podcast", "youtube")
+
+# _SUBSETS = ("xs", "s", "m", "l", "xl")
+
+# _BASE_DATA_URL = "https://huggingface.co/datasets/speechcolab/gigaspeech/resolve/main/data/"
+
+_AUDIO_ARCHIVE_URL = _BASE_DATA_URL + "audio/{subset}_files{is_additional}/{subset}_chunks_{archive_id:04}.tar.gz"
+
+_META_URL = _BASE_DATA_URL + "metadata/{subset}_metadata{is_additional}/{subset}_chunks_{archive_id:04}_metadata.csv"
+
+_N_ARCHIVES_URL = _BASE_DATA_URL + "{subset}_n_archives{is_additional}.txt"
+
+
+class GigaspeechConfig(datasets.BuilderConfig):
+    """BuilderConfig for Gigaspeech."""
+
+    def __init__(self, name, *args, **kwargs):
+        """BuilderConfig for Gigaspeech
+        """
+        super().__init__(name=name, *args, **kwargs)
+        # larger subsets are supersets of smaller subsets,
+        # if we want to download "m", we need to download "xs" and "s" data too.
+        # so if name == "m", self.subsets_to_download will be ("xs", "s", "m")
+        if name not in {"dev", "test"}:
+            self.subsets_to_download = _SUBSETS[:_SUBSETS.index(name) + 1]
+        else:
+            self.subsets_to_download = (name,)
+
+
+class Gigaspeech(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 = [GigaspeechConfig(name=subset) for subset in _SUBSETS + ("dev", "test")]
+
+    DEFAULT_WRITER_BATCH_SIZE = 128
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "segment_id": datasets.Value("string"),
+                "speaker": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "begin_time": datasets.Value("float32"),
+                "end_time": datasets.Value("float32"),
+                "audio_id": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "source": datasets.ClassLabel(names=_SOURCES),
+                "category": datasets.ClassLabel(names=_CATEGORIES),
+                "original_full_path": datasets.Value("string"),  # relative path to full audio in original data dirs
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _is_additional_data(self, name):
+        if name in {"s", "m", "l", "xl"}:
+            return "_additional"
+        return ""
+
+    @property
+    def _splits_to_subsets(self):
+        return {
+            "train": self.config.subsets_to_download,
+            "dev": ["dev"],
+            "test": ["test"]
+        }
+
+    def _read_n_archives(self, n_archives_path):
+        with open(n_archives_path, encoding="utf-8") as f:
+            return int(f.read().strip())
+
+    def _split_generators(self, dl_manager):
+        splits_to_subsets = self._splits_to_subsets
+        if self.config.name in {"dev", "test"}:
+            splits = (self.config.name,)
+        else:
+            splits = ("train", "dev", "test")
+
+        # 1. get number of archives (shards) in each subset
+        n_archives_links = {
+            split: {
+                subset: _N_ARCHIVES_URL.format(subset=subset, is_additional=self._is_additional_data(subset))
+                for subset in splits_to_subsets[split]
+            }
+            for split in splits
+        }
+        n_archives_paths = dl_manager.download_and_extract(n_archives_links)
+        n_archives = {
+            # mapping from a subset to a single number - number of audio archives (shards) in a subset
+            split: {
+                subset: self._read_n_archives(n_archives_paths[split][subset])
+                for subset in splits_to_subsets[split]
+            }
+            for split in splits
+        }
+
+        # 2. prepare sharded archives with audio files
+        audio_archives_urls = {
+            split: {
+                subset: [
+                    _AUDIO_ARCHIVE_URL.format(subset=subset, is_additional=self._is_additional_data(subset),
+                                              archive_id=i)
+                    for i in range(n_archives[split][subset])
+                ]
+                for subset in splits_to_subsets[split]
+            }
+            for split in splits
+        }
+        audio_archives_paths = dl_manager.download(audio_archives_urls)
+        # flatten archives paths from
+        # {"train": {"xs": [path1, path2,], "s": [path3], "m": [path5, path5]}, "dev": {"dev": [path6,...]}, "test": {"test": [...]}}
+        # to {"train": [path1, path2, path3, path4, path5], "dev": [path6, ...], "test": [...]}
+        audio_archives_paths = _flatten_nested_dict(audio_archives_paths)
+        local_audio_archives_paths = dl_manager.extract(audio_archives_paths) if not dl_manager.is_streaming \
+            else None
+
+        # 3. prepare sharded metadata csv files
+        meta_urls = {
+            split: {
+                subset: [
+                    _META_URL.format(subset=subset, is_additional=self._is_additional_data(subset), archive_id=i)
+                    for i in range(n_archives[split][subset])
+                ]
+                for subset in splits_to_subsets[split]
+            }
+            for split in splits
+        }
+        meta_paths = dl_manager.download_and_extract(meta_urls)
+        meta_paths = _flatten_nested_dict(meta_paths)
+
+        if self.config.name not in {"dev", "test"}:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "audio_archives_iterators": [
+                            dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["train"]
+                        ],
+                        "local_audio_archives_paths": local_audio_archives_paths[
+                            "train"] if local_audio_archives_paths else None,
+                        "meta_paths": meta_paths["train"]
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "audio_archives_iterators": [
+                            dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["dev"]
+                        ],
+                        "local_audio_archives_paths": local_audio_archives_paths[
+                            "dev"] if local_audio_archives_paths else None,
+                        "meta_paths": meta_paths["dev"]
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "audio_archives_iterators": [
+                            dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["test"]
+                        ],
+                        "local_audio_archives_paths": local_audio_archives_paths[
+                            "test"] if local_audio_archives_paths else None,
+                        "meta_paths": meta_paths["test"]
+                    },
+                ),
+            ]
+
+        if self.config.name == "dev":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "audio_archives_iterators": [
+                            dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["dev"]
+                        ],
+                        "local_audio_archives_paths": local_audio_archives_paths[
+                            "dev"] if local_audio_archives_paths else None,
+                        "meta_paths": meta_paths["dev"]
+                    },
+                ),
+            ]
+
+        if self.config.name == "test":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "audio_archives_iterators": [
+                            dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["test"]
+                        ],
+                        "local_audio_archives_paths": local_audio_archives_paths[
+                            "test"] if local_audio_archives_paths else None,
+                        "meta_paths": meta_paths["test"]
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, audio_archives_iterators, local_audio_archives_paths, meta_paths):
+        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}
+                }
+
+
+def _flatten_nested_dict(nested_dict):
+    return {
+        key: [inner_list_element for inner_list in value_to_lists.values() for inner_list_element in inner_list]
+        for key, value_to_lists in nested_dict.items()
+    }

audio/urmi (christian)/dev.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fbf86dafd01656196af2e98c8fc9638ca96fa0cb1ddf6e8a4bc0b140649da30b
+size 30720

audio/urmi (christian)/test.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2ecd175686cde03f3be0491431a1bdea6cf6b44ee32b1c52a672b4087cd81ab
+size 30720

audio/urmi (christian)/train.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32185858f3d78d8c37f041d54ea1e0ea67740f14b99af8900173a2a56ac34a03
+size 40960

build.py

@@ -87,6 +87,8 @@ def save_data(subsets):
                         'path': audio_file_name,
                     })
 
+                    break
+
                 pbar.set_description(f"Saving audios ({dialect}/{split})")
                 audio_tar_path = f"{audio_dir_path}.tar"
                 with tarfile.open(audio_tar_path, 'w') as tar:

main.ipynb

@@ -7,6 +7,33 @@
     "# Creating the NENA Speech Dataset"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Repo card metadata block was not found. Setting CardData to empty.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<nena_speech_1_0.NENASpeech at 0x15ae1c0d0>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from nena_speech_1_0 import NENASpeech\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -16,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,27 +185,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m/Users/matthew/Documents/nenadb/dataloader/main.ipynb Cell 10\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/matthew/Documents/nenadb/dataloader/main.ipynb#X10sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m save_data(subsets)\n",
-      "\u001b[1;32m/Users/matthew/Documents/nenadb/dataloader/main.ipynb Cell 10\u001b[0m line \u001b[0;36m2\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matthew/Documents/nenadb/dataloader/main.ipynb#X10sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m     f\u001b[39m.\u001b[39mwrite(response\u001b[39m.\u001b[39mcontent)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matthew/Documents/nenadb/dataloader/main.ipynb#X10sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m     f\u001b[39m.\u001b[39mflush()\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/matthew/Documents/nenadb/dataloader/main.ipynb#X10sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m     audio \u001b[39m=\u001b[39m AudioSegment\u001b[39m.\u001b[39;49mfrom_file(f\u001b[39m.\u001b[39;49mname)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matthew/Documents/nenadb/dataloader/main.ipynb#X10sZmlsZQ%3D%3D?line=26'>27</a>\u001b[0m audio \u001b[39m=\u001b[39m audio\u001b[39m.\u001b[39mset_frame_rate(\u001b[39m48000\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matthew/Documents/nenadb/dataloader/main.ipynb#X10sZmlsZQ%3D%3D?line=27'>28</a>\u001b[0m audio_file_name \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mnena_speech_\u001b[39m\u001b[39m{\u001b[39;00mexample\u001b[39m.\u001b[39mid\u001b[39m}\u001b[39;00m\u001b[39m.mp3\u001b[39m\u001b[39m\"\u001b[39m\n",
-      "File \u001b[0;32m~/Documents/nenadb/dataloader/venv/lib/python3.11/site-packages/pydub/audio_segment.py:728\u001b[0m, in \u001b[0;36mAudioSegment.from_file\u001b[0;34m(cls, file, format, codec, parameters, start_second, duration, **kwargs)\u001b[0m\n\u001b[1;32m    726\u001b[0m     info \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    727\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 728\u001b[0m     info \u001b[39m=\u001b[39m mediainfo_json(orig_file, read_ahead_limit\u001b[39m=\u001b[39;49mread_ahead_limit)\n\u001b[1;32m    729\u001b[0m \u001b[39mif\u001b[39;00m info:\n\u001b[1;32m    730\u001b[0m     audio_streams \u001b[39m=\u001b[39m [x \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m info[\u001b[39m'\u001b[39m\u001b[39mstreams\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m    731\u001b[0m                      \u001b[39mif\u001b[39;00m x[\u001b[39m'\u001b[39m\u001b[39mcodec_type\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39maudio\u001b[39m\u001b[39m'\u001b[39m]\n",
-      "File \u001b[0;32m~/Documents/nenadb/dataloader/venv/lib/python3.11/site-packages/pydub/utils.py:275\u001b[0m, in \u001b[0;36mmediainfo_json\u001b[0;34m(filepath, read_ahead_limit)\u001b[0m\n\u001b[1;32m    273\u001b[0m command \u001b[39m=\u001b[39m [prober, \u001b[39m'\u001b[39m\u001b[39m-of\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mjson\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m+\u001b[39m command_args\n\u001b[1;32m    274\u001b[0m res \u001b[39m=\u001b[39m Popen(command, stdin\u001b[39m=\u001b[39mstdin_parameter, stdout\u001b[39m=\u001b[39mPIPE, stderr\u001b[39m=\u001b[39mPIPE)\n\u001b[0;32m--> 275\u001b[0m output, stderr \u001b[39m=\u001b[39m res\u001b[39m.\u001b[39;49mcommunicate(\u001b[39minput\u001b[39;49m\u001b[39m=\u001b[39;49mstdin_data)\n\u001b[1;32m    276\u001b[0m output \u001b[39m=\u001b[39m output\u001b[39m.\u001b[39mdecode(\u001b[39m\"\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m    277\u001b[0m stderr \u001b[39m=\u001b[39m stderr\u001b[39m.\u001b[39mdecode(\u001b[39m\"\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m'\u001b[39m)\n",
-      "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.5/Frameworks/Python.framework/Versions/3.11/lib/python3.11/subprocess.py:1209\u001b[0m, in \u001b[0;36mPopen.communicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m   1206\u001b[0m     endtime \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1208\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1209\u001b[0m     stdout, stderr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_communicate(\u001b[39minput\u001b[39;49m, endtime, timeout)\n\u001b[1;32m   1210\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1211\u001b[0m     \u001b[39m# https://bugs.python.org/issue25942\u001b[39;00m\n\u001b[1;32m   1212\u001b[0m     \u001b[39m# See the detailed comment in .wait().\u001b[39;00m\n\u001b[1;32m   1213\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.5/Frameworks/Python.framework/Versions/3.11/lib/python3.11/subprocess.py:2108\u001b[0m, in \u001b[0;36mPopen._communicate\u001b[0;34m(self, input, endtime, orig_timeout)\u001b[0m\n\u001b[1;32m   2101\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_timeout(endtime, orig_timeout,\n\u001b[1;32m   2102\u001b[0m                         stdout, stderr,\n\u001b[1;32m   2103\u001b[0m                         skip_check_and_raise\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m   2104\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(  \u001b[39m# Impossible :)\u001b[39;00m\n\u001b[1;32m   2105\u001b[0m         \u001b[39m'\u001b[39m\u001b[39m_check_timeout(..., skip_check_and_raise=True) \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m   2106\u001b[0m         \u001b[39m'\u001b[39m\u001b[39mfailed to raise TimeoutExpired.\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m-> 2108\u001b[0m ready \u001b[39m=\u001b[39m selector\u001b[39m.\u001b[39;49mselect(timeout)\n\u001b[1;32m   2109\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_timeout(endtime, orig_timeout, stdout, stderr)\n\u001b[1;32m   2111\u001b[0m \u001b[39m# XXX Rewrite these to use non-blocking I/O on the file\u001b[39;00m\n\u001b[1;32m   2112\u001b[0m \u001b[39m# objects; they are no longer using C stdio!\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.5/Frameworks/Python.framework/Versions/3.11/lib/python3.11/selectors.py:415\u001b[0m, in \u001b[0;36m_PollLikeSelector.select\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    413\u001b[0m ready \u001b[39m=\u001b[39m []\n\u001b[1;32m    414\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 415\u001b[0m     fd_event_list \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_selector\u001b[39m.\u001b[39mpoll(timeout)\n\u001b[1;32m    416\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mInterruptedError\u001b[39;00m:\n\u001b[1;32m    417\u001b[0m     \u001b[39mreturn\u001b[39;00m ready\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "save_data(subsets)"
    ]

nena_speech_1_0.py

@@ -1,11 +1,143 @@
 """ NENA Speech Dataset"""
 
 
+import csv
+import os
+import json
+
+import datasets
+from datasets.utils.py_utils import size_str
+from tqdm import tqdm
+
+
+# _CITATION = """\
+# """
+
+# _HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
+
+# _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
+
+# TODO: change this
+_BASE_URL = "./"
+
+_AUDIO_URL = _BASE_URL + "audio/{dialect}/{split}.tar"
+
+_TRANSCRIPT_URL = _BASE_URL + "transcript/{dialect}/{split}.tsv"
+
 import datasets
 
 class NENASpeechConfig(datasets.BuilderConfig):
     """BuilderConfig for NENASpeech."""
-    pass
+    def __init__(self, name, version, **kwargs):
+        self.language = kwargs.pop("language", None)
+        description = (
+            f"This is a test. "
+        )
+        super(NENASpeechConfig, self).__init__(
+            name=name,
+            version=datasets.Version(version),
+            description=description,
+            **kwargs,
+        )
 
 class NENASpeech(datasets.GeneratorBasedBuilder):
-    pass
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    BUILDER_CONFIGS = [
+        NENASpeechConfig(
+            name='urmi (christian)',
+            version='1.0.0',
+            language='assyrian',
+        )
+        # for lang, lang_stats in STATS["locales"].items()
+    ]
+
+    def _info(self):
+        # total_languages = len(STATS["locales"])
+        # total_valid_hours = STATS["totalValidHrs"]
+        description = (
+            "description from _info"
+            # "Common Voice is Mozilla's initiative to help teach machines how real people speak. "
+            # f"The dataset currently consists of {total_valid_hours} validated hours of speech "
+            # f" in {total_languages} languages, but more voices and languages are always added."
+        )
+        features = datasets.Features(
+            {
+                "age": datasets.Value("string"),
+                "transcription": datasets.Value("string"),
+                "translation": datasets.Value("string"),
+                "path": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=description,
+            # citation=_CITATION,
+            # homepage=_HOMEPAGE,
+            # license=_LICENSE,
+            features=features,
+            supervised_keys=None,
+            version=self.config.version,
+        )
+
+    def _split_generators(self, dl_manager):
+        dialect = self.config.name
+
+        audio_urls = {}
+        splits = ("train", "dev", "test", "other", "invalidated")
+        for split in splits:
+            audio_urls[split] = _AUDIO_URL.format(dialect=dialect, split=split)
+        archive_paths = dl_manager.download(audio_urls)
+        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+
+        meta_urls = {split: _TRANSCRIPT_URL.format(dialect=dialect, split=split) for split in splits}
+        meta_paths = dl_manager.download_and_extract(meta_urls)
+
+        split_generators = []
+        split_names = {
+            "train": datasets.Split.TRAIN,
+            "dev": datasets.Split.VALIDATION,
+            "test": datasets.Split.TEST,
+        }
+        for split in splits:
+            split_generators.append(
+                datasets.SplitGenerator(
+                    name=split_names.get(split, split),
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
+                        "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
+                        "meta_path": meta_paths[split],
+                    },
+                ),
+            )
+
+        return split_generators
+
+    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
+        data_fields = list(self._info().features.keys())
+        metadata = {}
+        with open(meta_path, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for row in tqdm(reader, desc="Reading metadata..."):
+                if not row["path"].endswith(".mp3"):
+                    row["path"] += ".mp3"
+                # accent -> accents in CV 8.0
+                if "accents" in row:
+                    row["accent"] = row["accents"]
+                    del row["accents"]
+                # if data is incomplete, fill with empty values
+                for field in data_fields:
+                    if field not in row:
+                        row[field] = ""
+                metadata[row["path"]] = row
+
+        for i, audio_archive in enumerate(archives):
+            for path, file in audio_archive:
+                _, filename = os.path.split(path)
+                if filename in metadata:
+                    result = dict(metadata[filename])
+                    # set the audio feature and the path to the extracted file
+                    path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path
+                    result["audio"] = {"path": path, "bytes": file.read()}
+                    result["path"] = path
+                    yield path, result

release_stats.py

@@ -0,0 +1,50 @@
+STATS = {
+    "bundleURLTemplate": "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-13.0-2023-03-09/{locale}.tar.gz",
+    "locales": {
+        "de": {
+            "duration": 4821107393,
+            "buckets": {
+                "dev": 16143,
+                "invalidated": 50705,
+                "other": 6381,
+                "reported": 9131,
+                "test": 16143,
+                "train": 540437,
+                "validated": 868264,
+            },
+            "reportedSentences": 9100,
+            "clips": 925350,
+            "splits": {
+                "accent": {"": 1},
+                "age": {
+                    "twenties": 0.18,
+                    "fourties": 0.17,
+                    "": 0.32,
+                    "thirties": 0.16,
+                    "teens": 0.03,
+                    "sixties": 0.02,
+                    "fifties": 0.11,
+                    "seventies": 0,
+                    "eighties": 0,
+                    "nineties": 0,
+                },
+                "gender": {"male": 0.59, "": 0.32, "female": 0.08, "other": 0.01},
+            },
+            "users": 17867,
+            "size": 33828262029,
+            "checksum": "71664fadd4189922f3c814889f640111e925fb511b290242e10e7a768bd7b1bb",
+            "avgDurationSecs": 5.21,
+            "validDurationSecs": 4523687.242,
+            "totalHrs": 1339.19,
+            "validHrs": 1256.57,
+        },
+    },
+    "totalDuration": 97709611853,
+    "totalValidDurationSecs": 63681475,
+    "totalHrs": 27141,
+    "totalValidHrs": 17689,
+    "version": "13.0.0",
+    "date": "2022-03-15",
+    "name": "Common Voice Corpus 13",
+    "multilingual": True,
+}

transcript/urmi (christian)/dev.tsv

@@ -0,0 +1,2 @@
+age	transcription	translation	path
+70's	ʾa-mùt ⁺xábrələ?ˈ	What is this all about?’	nena_speech_is5yh1hcxg6p3gd.mp3

transcript/urmi (christian)/test.tsv

@@ -0,0 +1,2 @@
+age	transcription	translation	path
+70's	hì,ˈ ʾàyya꞊da ʾátxa.	Yes, that too is such.	nena_speech_c12wj7acuhfzube.mp3

transcript/urmi (christian)/train.tsv

@@ -0,0 +1,2 @@
+age	transcription	translation	path
+70's	ʾət-k̭àšə,ˈ	of priests.	nena_speech_6rcr536rfodtmog.mp3