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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 = {'ne-NP': 'Nepali'}

male-female.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
+
+from .languages import LANGUAGES
+from .release_stats import STATS
+
+
+_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_11_0/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 CommonVoice."""
+
+    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"Common Voice speech to text 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 = [from datasets import load_dataset, DatasetDict
+
+common_voice = DatasetDict()
+
+common_voice["train"] = load_dataset("../dataset", "ne-NP", split="train")
+common_voice["test"] = load_dataset("../dataset", "ne-NP", split="test")
+
+print(common_voice)
+
+        CommonVoiceConfig(
+            name=lang,
+            version=0.01
+        )
+        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."
+        )from datasets import load_dataset, DatasetDict
+
+common_voice = DatasetDict()
+
+common_voice["train"] = load_dataset("../dataset", "ne-NP", split="train")
+common_voice["test"] = load_dataset("../dataset", "ne-NP", split="test")
+
+print(common_voice)
+
+        features = datasets.Features(
+            {
+
+                "audio_id": datasets.Value("string"),from datasets import load_dataset, DatasetDict
+
+common_voice = DatasetDict()
+
+common_voice["train"] = load_dataset("../dataset", "ne-NP", split="train")
+common_voice["test"] = load_dataset("../dataset", "ne-NP", split="test")
+
+print(common_voice)
+
+                "audio": datasets.features.Audio(sampling_rate=48_000),
+                "sentence": datasets.Value("string"),
+                
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=description,
+            features=features,
+            supervised_keys=None,
+            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

n_shards.json

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+{
+    "ne-NP": {
+        "train": 1,
+        "test": 1
+    }
+}