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epadb.py

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+import json
+import datasets
+
+_CITATION = """\
+@article{vidal2019epadb,
+  title={EpaDB: a database for development of pronunciation assessment systems},
+  author={Vidal, Jazmin and Ferrer, Luciana and Brambilla, Leonardo},
+  journal={Proc. Interspeech},
+  pages={589--593},
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+EPADB contains curated pronunciation assessment data collected from Spanish-speaking learners of English.
+"""
+
+class Epadb(datasets.GeneratorBasedBuilder):
+    """EPADB dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "utt_id": datasets.Value("string"),
+                "speaker_id": datasets.Value("string"),
+                "sentence_id": datasets.Value("string"),
+                "phone_ids": datasets.Sequence(datasets.Value("string")),
+                "ref_phonemic_1": datasets.Sequence(datasets.Value("string")),
+                "annot_1": datasets.Sequence(datasets.Value("string")),
+                "lab_phonemic_1": datasets.Sequence(datasets.Value("string")),
+                "error_type": datasets.Sequence(datasets.Value("string")),
+                "start_mfa": datasets.Sequence(datasets.Value("float")),
+                "end_mfa": datasets.Sequence(datasets.Value("float")),
+                "global_1": datasets.Value("float"),
+                "level_1": datasets.Value("string"),
+                "gender": datasets.Value("string"),
+                "duration": datasets.Value("float"),
+                "sample_rate": datasets.Value("int32"),
+                "audio": datasets.Audio(sampling_rate=16000),
+                "transcription": datasets.Value("string"),
+            }),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download("train.json")
+        test_path = dl_manager.download("test.json")
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": train_path, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": test_path, "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for idx, example in enumerate(data):
+                # The audio field contains the relative path
+                # datasets library will automatically download it
+                yield idx, example