Upload ./AudioSCAN-XTTSv2-female.py with huggingface_hub

AudioSCAN-XTTSv2-female.py

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+import json
+import os
+
+import datasets
+
+from .configs import CONFIGS_MAP
+
+_BASE_URL = (
+    "https://huggingface.co/datasets/formospeech/AudioSCAN-XTTSv2-female/resolve/main/"
+)
+_AUDIO_URL = _BASE_URL + "audio.tar.gz"
+
+
+class AudioSCANConfig(datasets.BuilderConfig):
+    """BuilderConfig for CommonVoice."""
+
+    def __init__(self, **kwargs):
+        super(AudioSCANConfig, self).__init__(
+            version=datasets.Version("0.1.0", ""), **kwargs
+        )
+
+
+class LibrispeechASR(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 256
+    DEFAULT_CONFIG_NAME = "addprim_jump"
+    BUILDER_CONFIGS = []
+    for config_name in CONFIGS_MAP.keys():
+        BUILDER_CONFIGS.append(
+            AudioSCANConfig(
+                name=config_name,
+                description=None,
+            )
+        )
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="",
+            features=datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "out": datasets.Value("string"),
+                    "in": datasets.Value("string"),
+                }
+            ),
+            license=None,
+            supervised_keys=("audio", "out"),
+            homepage=None,
+            citation=None,
+            task_templates=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(_AUDIO_URL)
+        local_extracted_archive = (
+            dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
+        )
+
+        split_meta_urls = {
+            split_name: _BASE_URL + split_meta_path
+            for split_name, split_meta_path in CONFIGS_MAP[self.config.name].items()
+        }
+
+        split_meta_paths = dl_manager.download_and_extract(split_meta_urls)
+        splits = []
+        for split_name in CONFIGS_MAP[self.config.name].keys():
+            split = datasets.SplitGenerator(
+                name=split_name,
+                gen_kwargs={
+                    "meta_path": split_meta_paths[split_name],
+                    "local_extracted_archive": local_extracted_archive,
+                    "files": dl_manager.iter_archive(archive_path),
+                },
+            )
+            splits.append(split)
+
+        return splits
+
+    def _generate_examples(self, meta_path, local_extracted_archive, files):
+        with open(meta_path, "r") as f:
+            metadata = json.loads("[" + ",".join(f.readlines()) + "]")
+
+            audio_data = {}
+            for path, file in files:
+                filename = os.path.basename(path)
+                audio_data[filename] = file.read()
+            key = 0
+            for item in metadata:
+                path = (
+                    os.path.join(local_extracted_archive, item["audio_path"])
+                    if local_extracted_archive
+                    else item["audio_path"]
+                )
+                yield (
+                    key,
+                    {
+                        "in": item["in"],
+                        "out": item["out"],
+                        "audio": {
+                            "path": path,
+                            "bytes": audio_data[item["audio_path"]],
+                        },
+                    },
+                )
+                key += 1