Upload commonvoice_120.py with huggingface_hub

commonvoice_120.py

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+# coding=utf-8
+import csv
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_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
+}
+"""
+
+_DATASETNAME = "commonvoice_120"
+
+_DESCRIPTION = """\
+The Common Mozilla Voice dataset consists of a unique MP3 and corresponding text file.
+Many of the 26119 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines.
+The dataset currently consists of 17127 validated hours in 104 languages, but more voices and languages are always added.
+
+Before using this dataloader, please accept the acknowledgement at https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0 and use huggingface-cli login for authentication
+"""
+
+_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
+
+_LANGUAGES = ["cnh", "ind", "tha", "vie"]
+_LANG_TO_CVLANG = {"cnh": "cnh", "ind": "id", "tha": "th", "vie": "vi"}
+
+_AGE_TO_INT = {"": None, "teens": 10, "twenties": 20, "thirties": 30, "fourties": 40, "fifties": 50, "sixties": 60, "seventies": 70, "eighties": 80}
+
+_LICENSE = Licenses.CC0_1_0.value
+
+# Note: the dataset is gated in HuggingFace. It's public after providing access token
+_LOCAL = False
+
+_COMMONVOICE_URL_TEMPLATE = "https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0/resolve/main/"
+_URLS = {"audio": _COMMONVOICE_URL_TEMPLATE + "audio/{lang}/{split}/{lang}_{split}_{shard_idx}.tar", "transcript": _COMMONVOICE_URL_TEMPLATE + "transcript/{lang}/{split}.tsv", "n_shards": _COMMONVOICE_URL_TEMPLATE + "n_shards.json"}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION, Tasks.TEXT_TO_SPEECH]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class Commonvoice120(datasets.GeneratorBasedBuilder):
+    """This is the dataloader for CommonVoice 12.0 Mozilla"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = (
+        *[
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{lang}{'_' if lang else ''}source",
+                version=datasets.Version(_SOURCE_VERSION),
+                description=f"{_DATASETNAME} source schema for {lang}",
+                schema="source",
+                subset_id=f"{_DATASETNAME}{'_' if lang else ''}{lang}",
+            )
+            for lang in ["", *_LANGUAGES]
+        ],
+        *[
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{lang}{'_' if lang else ''}seacrowd_sptext",
+                version=datasets.Version(_SEACROWD_VERSION),
+                description=f"{_DATASETNAME} SEACrowd schema for {lang}",
+                schema="seacrowd_sptext",
+                subset_id=f"{_DATASETNAME}{'_' if lang else ''}{lang}",
+            )
+            for lang in ["", *_LANGUAGES]
+        ],
+    )
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            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"),
+                }
+            )
+        elif self.config.schema == "seacrowd_sptext":
+            features = schemas.speech_text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        lang_code = self.config.subset_id.split("_")[-1]
+        languages = [_LANG_TO_CVLANG.get(lang, lang) for lang in (_LANGUAGES if lang_code == "120" else [lang_code])]
+        n_shards_path = dl_manager.download_and_extract(_URLS["n_shards"])
+        with open(n_shards_path, encoding="utf-8") as f:
+            n_shards = json.load(f)
+
+        audio_urls = {}
+        meta_urls = {}
+        splits = ("train", "dev", "test")
+        for split in splits:
+            audio_urls[split] = [_URLS["audio"].format(lang=lang, split=split, shard_idx=i) for lang in languages for i in range(n_shards[lang][split])]
+            meta_urls[split] = [_URLS["transcript"].format(lang=lang, split=split) for lang in languages]
+        archive_paths = dl_manager.download(audio_urls)
+        local_extracted_archive_paths = dl_manager.extract(archive_paths)
+        meta_paths = dl_manager.download_and_extract(meta_urls)
+
+        split_names = {
+            "train": datasets.Split.TRAIN,
+            "dev": datasets.Split.VALIDATION,
+            "test": datasets.Split.TEST,
+        }
+        return [
+            datasets.SplitGenerator(
+                name=split_names.get(split, split),
+                gen_kwargs={
+                    "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
+                    "audio_archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
+                    "meta_paths": meta_paths[split],
+                    "split": "train",
+                },
+            )
+            for split in splits
+        ]
+
+    def _generate_examples(self, local_extracted_archive_paths: [Path], audio_archives: [Path], meta_paths: [Path], split: str) -> Tuple[int, Dict]:
+        data_fields = list(self._info().features.keys())
+        metadata = {}
+        for meta_path in meta_paths:
+            with open(meta_path, encoding="utf-8") as f:
+                reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for row in reader:
+                    if not row["path"].endswith(".mp3"):
+                        row["path"] += ".mp3"
+                    if "accents" in row:
+                        row["accent"] = row["accents"]
+                        del row["accents"]
+                    for field in data_fields:
+                        if field not in row:
+                            row[field] = ""
+                    metadata[row["path"]] = row
+
+        if self.config.schema == "source":
+            for i, audio_archive in enumerate(audio_archives):
+                for path, file in audio_archive:
+                    _, filename = os.path.split(path)
+                    if filename in metadata:
+                        src_result = dict(metadata[filename])
+                        path = os.path.join(local_extracted_archive_paths[i], path)
+                        result = {
+                            "client_id": src_result["client_id"],
+                            "path": path,
+                            "audio": {"path": path, "bytes": file.read()},
+                            "sentence": src_result["sentence"],
+                            "up_votes": src_result["up_votes"],
+                            "down_votes": src_result["down_votes"],
+                            "age": src_result["age"],
+                            "gender": src_result["gender"],
+                            "accent": src_result["accent"],
+                            "locale": src_result["locale"],
+                            "segment": src_result["segment"],
+                        }
+                        yield path, result
+
+        elif self.config.schema == "seacrowd_sptext":
+            for i, audio_archive in enumerate(audio_archives):
+                for path, file in audio_archive:
+                    _, filename = os.path.split(path)
+                    if filename in metadata:
+                        src_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)
+                        result = {
+                            "id": src_result["path"].replace(".mp3", ""),
+                            "path": path,
+                            "audio": {"path": path, "bytes": file.read()},
+                            "text": src_result["sentence"],
+                            "speaker_id": src_result["client_id"],
+                            "metadata": {
+                                "speaker_age": _AGE_TO_INT[src_result["age"]],
+                                "speaker_gender": src_result["gender"],
+                            },
+                        }
+                        yield path, result