Upload 5 files

MELD_Audio.py

@@ -0,0 +1,146 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import datasets
+import pandas as pd
+from datasets import ClassLabel
+import os
+
+"""The Audio, Speech, and Vision Processing Lab - Emotional Sound Database (ASVP - ESD)"""
+
+_CITATION = """\
+@article{poria2018meld,
+  title={Meld: A multimodal multi-party dataset for emotion recognition in conversations},
+  author={Poria, Soujanya and Hazarika, Devamanyu and Majumder, Navonil and Naik, Gautam and Cambria, Erik and Mihalcea, Rada},
+  journal={arXiv preprint arXiv:1810.02508},
+  year={2018}
+}
+@article{chen2018emotionlines,
+  title={Emotionlines: An emotion corpus of multi-party conversations},
+  author={Chen, Sheng-Yeh and Hsu, Chao-Chun and Kuo, Chuan-Chun and Ku, Lun-Wei and others},
+  journal={arXiv preprint arXiv:1802.08379},
+  year={2018}
+}
+"""
+
+_DESCRIPTION = """\
+Multimodal EmotionLines Dataset (MELD) has been created by enhancing and extending EmotionLines dataset. 
+MELD contains the same dialogue instances available in EmotionLines, but it also encompasses audio and 
+visual modality along with text. MELD has more than 1400 dialogues and 13000 utterances from Friends TV series. 
+Multiple speakers participated in the dialogues. Each utterance in a dialogue has been labeled by any of these 
+seven emotions -- Anger, Disgust, Sadness, Joy, Neutral, Surprise and Fear. MELD also has sentiment (positive, 
+negative and neutral) annotation for each utterance.
+This dataset is modified from https://huggingface.co/datasets/zrr1999/MELD_Text_Audio.
+The audio is extracted from MELD mp4 files while the audio only has one channel with sample rate 16khz.
+"""
+
+_LICENSE = "gpl-3.0"
+
+
+class MELD_Audio(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("0.0.1")
+    BUILDER_CONFIGS = [  # noqa: RUF012
+        datasets.BuilderConfig(name="MELD_Audio", version=VERSION, description="MELD audio"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16000),
+                    "emotion": ClassLabel(names=["neutral", "joy", "sadness", "anger", "fear", "disgust", "surprise"]),
+                    "sentiment": ClassLabel(names=["neutral", "positive", "negative"]),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        metadata_dir: dict[str, str] = dl_manager.download_and_extract(
+            {"train": "train_sent_emo.csv", "validation": "dev_sent_emo.csv", "test": "test_sent_emo.csv"}
+        )  # type: ignore  # noqa: PGH003
+        data_path: dict[str, str] = dl_manager.download(
+            {
+                "audios_train": "archive/train.tar.gz",
+                "audios_validation": "archive/dev.tar.gz",
+                "audios_test": "archive/test.tar.gz",
+            }
+        )  # type: ignore  # noqa: PGH003
+        path_to_clips = "MELD_Audio"
+        local_extracted_archive: dict[str, str] = (
+            dl_manager.extract(data_path)
+            if not dl_manager.is_streaming
+            else {
+                "audios_train": None,
+                "audios_validation": None,
+                "audios_test": None,
+            }
+        )  # type: ignore  # noqa: PGH003
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,  # type: ignore  # noqa: PGH003
+                gen_kwargs={
+                    "filepath": metadata_dir["train"],
+                    "split": "train",
+                    "local_extracted_archive": local_extracted_archive["audios_train"],
+                    "audio_files": dl_manager.iter_archive(data_path["audios_train"]),
+                    "path_to_clips": path_to_clips,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,  # type: ignore  # noqa: PGH003
+                gen_kwargs={
+                    "filepath": metadata_dir["validation"],
+                    "split": "validation",
+                    "local_extracted_archive": local_extracted_archive["audios_validation"],
+                    "audio_files": dl_manager.iter_archive(data_path["audios_validation"]),
+                    "path_to_clips": path_to_clips,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,  # type: ignore  # noqa: PGH003
+                gen_kwargs={
+                    "filepath": metadata_dir["test"],
+                    "split": "test",
+                    "local_extracted_archive": local_extracted_archive["audios_test"],
+                    "audio_files": dl_manager.iter_archive(data_path["audios_test"]),
+                    "path_to_clips": path_to_clips,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split, local_extracted_archive, audio_files, path_to_clips):
+        """Yields examples."""
+        metadata_df = pd.read_csv(filepath, sep=",", index_col=0, header=0)
+        metadata = {}
+        for _, row in metadata_df.iterrows():
+            id_ = f"dia{row['Dialogue_ID']}_utt{row['Utterance_ID']}"
+            audio_path = f"{split}/{id_}.flac"
+            metadata[audio_path] = row
+
+        id_ = 0
+        for path, f in audio_files:
+            if path in metadata:
+                row = metadata[path]
+                path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
+                audio = {"path": path, bytes: f.read()}
+                yield (
+                    id_,
+                    {
+                        "text": row["Utterance"],
+                        "path": path,
+                        "audio": audio,
+                        "emotion": row["Emotion"],
+                        "sentiment": row["Sentiment"],
+                    },
+                )
+                id_ += 1

README.md

@@ -1,5 +1,43 @@
 ---
-license: gpl-3.0
-language:
-- en
----
+dataset_info:
+  config_name: MELD_Text
+  features:
+  - name: text
+    dtype: string
+  - name: path
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: emotion
+    dtype:
+      class_label:
+        names:
+          '0': neutral
+          '1': joy
+          '2': sadness
+          '3': anger
+          '4': fear
+          '5': disgust
+          '6': surprise
+  - name: sentiment
+    dtype:
+      class_label:
+        names:
+          '0': neutral
+          '1': positive
+          '2': negative
+  splits:
+  - name: train
+    num_bytes: 3629722
+    num_examples: 9988
+  - name: validation
+    num_bytes: 411341
+    num_examples: 1108
+  - name: test
+    num_bytes: 945283
+    num_examples: 2610
+  download_size: 7840135137
+  dataset_size: 4986346
+---