Create common-voice-13-fa.py

common-voice-13-fa.py

@@ -0,0 +1,136 @@
+import csv
+import os
+
+import datasets
+from tqdm import tqdm
+
+
+_DESCRIPTION = """\
+Persian portion of the common voice 13 dataset, gathered and maintained by Hezar AI.
+"""
+
+_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/"
+
+_BASE_URL = "https://huggingface.co/datasets/hezarai/common-voice-13-fa/resolve/main/"
+
+_AUDIO_URL = _BASE_URL + "audio/{split}.zip"
+
+_TRANSCRIPT_URL = _BASE_URL + "transcripts/{split}.tsv"
+
+
+class CommonVoiceFaConfig(datasets.BuilderConfig):
+    """BuilderConfig for CommonVoice."""
+
+    def __init__(self, **kwargs):
+        super(CommonVoiceFaConfig, self).__init__(**kwargs)
+
+
+class CommonVoice(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    BUILDER_CONFIGS = [
+        CommonVoiceFaConfig(
+            name="commonvoice-13-fa",
+            version="1.0.0",
+            description=_DESCRIPTION,
+        )
+    ]
+
+    def _info(self):
+        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):
+        splits = ("train", "dev", "test")
+        audio_urls = {split: _AUDIO_URL.format(split=split) for split in splits}
+
+        archive_paths = dl_manager.download(audio_urls)
+        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+
+        transcript_urls = {split: _TRANSCRIPT_URL.format(split=split) for split in splits}
+        transcript_paths = dl_manager.download_and_extract(transcript_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(archive_paths.get(split))],
+                        "transcript_path": transcript_paths[split],
+                    },
+                ),
+            )
+
+        return split_generators
+
+    def _generate_examples(self, local_extracted_archive_paths, archives, transcript_path):
+        data_fields = list(self._info().features.keys())
+        metadata = {}
+        with open(transcript_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