full test

data/vi/train_raw.tsv

@@ -1,4 +1,4 @@
-0-0-1	fake label1
+0-0-0	fake label1
 0-0-1	fake label2
 1-0-0	fake label3
 1-0-1	fake label4

gigaspeech2_metadata.py

@@ -0,0 +1,164 @@
+import os
+from collections import OrderedDict
+from pathlib import Path
+import datasets
+import glob
+import os
+
+from datasets.arrow_dataset import config
+
+
+class Gigaspeech2Config(datasets.BuilderConfig):
+    """BuilderConfig for Yodas."""
+
+    def __init__(self, config_name, version, **kwargs):
+        config_split = config_name.split("-")
+        self.language = config_split[0]
+        print(config_split)
+        self.split = config_split[1]
+        self.index = config_split[2] if len(config_split)>=3 else None
+        self.base_data_path = f"data/{self.language}"
+
+        description = (
+            f"Youtube speech to text dataset in {self.language}."
+        )
+        super(Gigaspeech2Config, self).__init__(
+            name=config_name,
+            version=datasets.Version(version),
+            description=description,
+            **kwargs,
+        )
+
+
+DEFAULT_CONFIG_NAME = "all"
+LANGS = ['id', 'th', 'vi']
+LANGS_SPLITS={
+    "id":2,
+    "th":2,
+    "vi":2
+}
+VERSION = "1.0.1"
+CONFIG_NAME=[]
+for lang in LANGS:
+    CONFIG_NAME.append(f"{lang}-test")
+    CONFIG_NAME.append(f"{lang}-dev")
+    CONFIG_NAME.append(f"{lang}-train_raw")
+    CONFIG_NAME.append(f"{lang}-train_refine")
+    for i in range(LANGS_SPLITS[lang]):
+        CONFIG_NAME.append(f"{lang}-train_raw-{i}")
+        CONFIG_NAME.append(f"{lang}-train_refine-{i}")
+
+class Gigaspeech2(datasets.GeneratorBasedBuilder):
+    """Yodas dataset."""
+
+    BUILDER_CONFIGS = [
+        Gigaspeech2Config(config_name, version=VERSION) for config_name in CONFIG_NAME
+    ]
+
+    VERSION = datasets.Version("1.0.1")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="Gigaspeech2",
+            features=datasets.Features(
+                OrderedDict(
+                    [
+                        # ("id", datasets.Value("string")),
+                        # ("utt_id", datasets.Value("string")),
+                        ("audio", datasets.Audio(sampling_rate=16_000)),
+                        ("text", datasets.Value("string")),
+                    ]
+                )
+            ),
+            supervised_keys=None,
+            homepage="",  # TODO
+            citation="",  # TODO
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO
+        lang = self.config.language
+        split = self.config.split
+        index = self.config.index
+        if index is not None:
+            assert int(index)<LANGS_SPLITS[lang], f"split {self.config.name} not found"
+
+        text_file = dl_manager.download([f"{self.config.base_data_path}/{split}.tsv"])
+        if split == "test":
+            audio_tar_files = dl_manager.download([f"{self.config.base_data_path}/{split}.tar.gz"])
+        elif split == "dev":
+            audio_tar_files = dl_manager.download([f"{self.config.base_data_path}/{split}.tar.gz"])
+        elif index is not None:
+            audio_tar_files = dl_manager.download([f"{self.config.base_data_path}/train/{index}.tar.gz"])
+        else:
+            audio_tar_files = dl_manager.download([f"{self.config.base_data_path}/train/{i}.tar.gz" for i in range(LANGS_SPLITS[lang])])
+
+        if dl_manager.is_streaming:
+            audio_archives = [dl_manager.iter_archive(audio_tar_file) for audio_tar_file in audio_tar_files]
+
+        else:
+            print("extracting audio ...")
+            extracted_audio_archives = dl_manager.extract(audio_tar_files)
+
+            audio_archives = []
+            for extracted_dir in extracted_audio_archives:
+                audio_archives.extend(glob.glob("{}/**/*.wav".format(str(extracted_dir)), recursive=True))
+
+        if split=="test":
+            dataset_split = datasets.Split.TEST
+        elif split=="dev":
+            dataset_split = datasets.Split.VALIDATION
+        else:
+            dataset_split = datasets.Split.TRAIN
+
+        return [
+            datasets.SplitGenerator(
+                name=dataset_split,
+                gen_kwargs={
+                    "is_streaming": dl_manager.is_streaming,
+                    "audio_archives": audio_archives,
+                    'tsv_file': text_file[0],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, is_streaming, audio_archives, tsv_file):
+        """Yields examples."""
+
+        id_ = 0
+
+        with open(tsv_file, 'r') as f:
+            lines = f.read().splitlines()
+            lines = [line.split("\t") for line in lines]
+            id2label={line[0]:line[1] for line in lines}
+
+        if is_streaming:
+            for tar_file in audio_archives:
+                for path, audio_f in tar_file:
+                    path = Path(path)
+                    utt_id = path.stem
+                    result = {
+                        'id': id_,
+                        'utt_id': utt_id,
+                        'audio': {"path": None, "bytes": audio_f.read()},
+                        'text': id2label[utt_id],
+                    }
+
+                    yield id_, result
+                    id_ += 1
+        else:
+            print(id2label)
+            for audio_file in audio_archives:
+                utt_id = Path(audio_file).stem
+                result = {
+                    'id': id_,
+                    'utt_id': utt_id,
+                    'audio': {"path": audio_file, "bytes": open(audio_file, 'rb').read()},
+                    'text': id2label[utt_id]
+                }
+
+                yield id_, result
+                id_ += 1
+
+