add script

eatd_corpus.py

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+from pathlib import Path
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition, AudioClassification
+import os
+
+
+_DESCRIPTION = """
+An Emotional Audio-Textual Corpus
+
+The EATD-Corpus is a dataset that consists of audio and text files of 162 volunteers who received counseling.
+
+Training set contains data from 83 volunteers (19 depressed and 64 non-depressed).
+
+Validation set contains data from 79 volunteers (11 depressed and 68 non-depressed).
+"""
+
+
+_URL = "https://github.com/speechandlanguageprocessing/ICASSP2022-Depression"
+
+
+_CITE = """
+@INPROCEEDINGS{9746569,
+  author={Shen, Ying and Yang, Huiyu and Lin, Lin},
+  booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
+  title={Automatic Depression Detection: an Emotional Audio-Textual Corpus and A Gru/Bilstm-Based Model}, 
+  year={2022},
+  pages={6247-6251},
+  doi={10.1109/ICASSP43922.2022.9746569}
+}
+"""
+
+
+class EATDDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="speech", version=VERSION, description="Data for speech recognition"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio_raw": datasets.Audio(sampling_rate=16_000),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "raw_sds": datasets.Value("uint8"),
+                "sds_score": datasets.Value("float"),
+                "label": datasets.ClassLabel(names=["neutral", "negative", "positive"])
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITE,
+            task_templates=[
+                AutomaticSpeechRecognition(audio_column="audio", transcription_column="text"),
+                AudioClassification(audio_column="audio", label_column="label")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        if hasattr(dl_manager, 'manual_dir') and dl_manager.manual_dir is not None:
+            data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "split": "train",
+                        "data_dir": data_dir,
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "split": "valid",
+                        "data_dir": data_dir,
+                    },
+                ),
+            ]
+
+    def _generate_examples(
+        self, split, data_dir
+    ):
+        basepath = Path(data_dir)
+        prefix = "v" if split == "valid" else "t"
+        for dir in basepath.glob(f"{prefix}_*"):
+            base_id = dir.name
+            with open(str(dir / "label.txt")) as labelf:
+                label = labelf.read().strip()
+                if label.endswith(".0"):
+                    raw_sds = int(label[:-2])
+                else:
+                    raw_sds = int(label)
+            with open(str(dir / "new_label.txt")) as labelf:
+                new_label = labelf.read().strip()
+                sds_score = float(new_label)
+            for polarity in ["neutral", "negative", "positive"]:
+                raw_audio = dir / f"{polarity}.wav"
+                proc_audio = dir / f"{polarity}_out.wav"
+                text_file = dir / f"{polarity}.txt"
+                with open(raw_audio, "rb") as rawf, open(proc_audio, "rb") as procf, open(text_file, "r") as textf:
+                    text = textf.read().strip()
+                    sid = f"{base_id}_{polarity}"
+                    yield sid, {
+                        "audio_raw": {
+                            "bytes": rawf.read(),
+                            "path": str(raw_audio),
+                        },
+                        "audio": {
+                            "bytes": procf.read(),
+                            "path": str(proc_audio),
+                        },
+                        "text": text,
+                        "id": sid,
+                        "raw_sds": raw_sds,
+                        "sds_score": sds_score,
+                        "label": polarity
+                    }