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count_n_shards.py

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+from pathlib import Path
+import json
+
+
+splits = ["train", "test"]
+
+if __name__ == "__main__":
+    n_files = {}
+    lang_dirs = [d for d in Path("audio").iterdir() if d.is_dir()]
+    for lang_dir in lang_dirs:
+        lang = lang_dir.name
+        n_files[lang] = {}
+        for split in splits:
+            split_dir = lang_dir / split
+            if split_dir.exists():
+                n_files_per_split = len(list(split_dir.glob("*.tar")))
+            else:
+                n_files_per_split = 0
+            n_files[lang][split] = n_files_per_split
+
+    with open("n_shards.json", "w") as f:
+        json.dump(dict(sorted(n_files.items(), key=lambda x: x[0])), f, ensure_ascii=False, indent=4)

languages.py

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+LANGUAGES = {'de': 'German', 'en': 'English', 'zh-CN': 'Chinese (China)'}

n_shards.json

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+{
+    "de": {
+        "train": 1,
+        "test": 1
+    },
+    "en": {
+        "train": 1,
+        "test": 1
+    },
+    "zh-CN": {
+        "train": 1,
+        "test": 1
+    }
+}

release_stats.py

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+STATS = {'bundleURLTemplate': 'vca-1.0-2024-07-25/vca-1.0-2024-07-25-{locale}.tar.gz', 'date': '2024-07-25', 'name': 'Voice Command Audio 1.0', 'multilingual': True, 'locales': {'de': {'buckets': {'test': 0, 'train': 0}, 'reportedSentences':0, 'duration': 0, 'clips': 0, 'splits': {'accent': {'': 1}, 'age': {'thirties': 1}, 'gender': {'male': 1}}, 'users': 1, 'size': 0, 'checksum': '94a0c7aeb0d18a280380e5a568d21251ed421f093bc164c9f67d8b28dfbecaaf', 'avgDurationSecs': 0, 'validDurationSecs': 0, 'totalHrs': 0, 'validHrs': 0}, 'en': { 'buckets': {'test': 0, 'train': 0}, 'reportedSentences': 0, 'duration': 0,'clips': 0, 'splits': {'accent': {'': 1}, 'age': {'thirties': 1}, 'gender': {'male': 1}}, 'users': 1, 'size': 0, 'checksum': '0efd86ca6b40641b55d1411b7d3b1f1ab8626de4b207504953706df201d198a5', 'avgDurationSecs': 0, 'validDurationSecs': 0, 'totalHrs': 0, 'validHrs': 0}, 'zh-CN': {'buckets': {'test': 0, 'train': 0}, 'reportedSentences': 0, 'duration': 0, 'clips': 0, 'splits': {'accent': {'': 1}, 'age': {'thirties': 1}, 'gender': {'male': 1}}, 'users': 1, 'size': 0, 'checksum': 'd1168d8cf2fd2654fa74056f3b7a0ade9ccb46777efb52d27ccf4d3b3a1841c5', 'avgDurationSecs': 0, 'validDurationSecs': 0, 'totalHrs': 0, 'validHrs': 0}}, 'totalDuration': 0, 'totalValidDurationSecs': 0, 'totalHrs': 0, 'totalValidHrs': 0, 'version': '1.0'}

voice_command_audio_1_0.py

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+# coding=utf-8
+# Copyright 2024 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the MIT License (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://mit-license.org/
+#
+# 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.
+""" Voice Command Audio Dataset"""
+
+
+import csv
+import os
+import json
+
+import datasets
+from datasets.utils.py_utils import size_str
+from tqdm import tqdm
+
+from .languages import LANGUAGES
+from .release_stats import STATS
+
+
+_CITATION = """\
+@inproceedings{none,
+  author = {shaodw_wxh},
+  title = {Voice Command: A General Purpose Speech Reconginition Gaming Interface},
+  booktitle = {},
+  pages = {1-10},
+  year = 2024
+}
+"""
+
+_HOMEPAGE = "https://www.shadow_wxh.org/"
+
+_LICENSE = "https://mit-license.org/"
+
+# TODO: change "streaming" to "main" after merge!
+_BASE_URL = "https://huggingface.co/datasets/shadow-wxh/VoiceCommandAudio/resolve/main/"
+
+_AUDIO_URL = _BASE_URL + "audio/{lang}/{split}/{lang}_{split}_{shard_idx}.tar"
+
+_TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}.tsv"
+
+_N_SHARDS_URL = _BASE_URL + "n_shards.json"
+
+
+class CommonVoiceConfig(datasets.BuilderConfig):
+    """BuilderConfig for Voice Command Audio."""
+
+    def __init__(self, name, version, **kwargs):
+        self.language = kwargs.pop("language", None)
+        self.release_date = kwargs.pop("release_date", None)
+        self.num_clips = kwargs.pop("num_clips", None)
+        self.num_speakers = kwargs.pop("num_speakers", None)
+        self.validated_hr = kwargs.pop("validated_hr", None)
+        self.total_hr = kwargs.pop("total_hr", None)
+        self.size_bytes = kwargs.pop("size_bytes", None)
+        self.size_human = size_str(self.size_bytes)
+        description = (
+            f"Voice Command Audio dataset in {self.language} released on {self.release_date}. "
+            f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data "
+            f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. "
+            f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}."
+        )
+        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 = (
+            "Voice Command Audio is a dataset to help fine tune Voice Command a geral purpose speech recongnition gaming interface. "
+            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=16_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"),
+            }
+        )
+
+        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", "test")
+        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,
+            "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(".wav"):
+                    row["path"] += ".wav"
+                # 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