| from pathlib import Path | |
| import datasets | |
| import pandas as pd | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {speech-emotion-recognition-dataset}, | |
| author = {TrainingDataPro}, | |
| year = {2023} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The audio dataset consists of a collection of texts spoken with four distinct | |
| emotions. These texts are spoken in English and represent four different | |
| emotional states: **euphoria, joy, sadness and surprise**. | |
| Each audio clip captures the tone, intonation, and nuances of speech as | |
| individuals convey their emotions through their voice. | |
| The dataset includes a diverse range of speakers, ensuring variability in age, | |
| gender, and cultural backgrounds*, allowing for a more comprehensive | |
| representation of the emotional spectrum. | |
| The dataset is labeled and organized based on the emotion expressed in each | |
| audio sample, making it a valuable resource for emotion recognition and | |
| analysis. Researchers and developers can utilize this dataset to train and | |
| evaluate machine learning models and algorithms, aiming to accurately | |
| recognize and classify emotions in speech. | |
| """ | |
| _NAME = 'speech-emotion-recognition-dataset' | |
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" | |
| _LICENSE = "cc-by-nc-nd-4.0" | |
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" | |
| class SpeechEmotionRecognitionDataset(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| return datasets.DatasetInfo(description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| 'set_id': datasets.Value('string'), | |
| 'euphoric': datasets.Audio(), | |
| 'joyfully': datasets.Audio(), | |
| 'sad': datasets.Audio(), | |
| 'surprised': datasets.Audio(), | |
| 'text': datasets.Value('string'), | |
| 'gender': datasets.Value('string'), | |
| 'age': datasets.Value('int8'), | |
| 'country': datasets.Value('string') | |
| }), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| license=_LICENSE) | |
| def _split_generators(self, dl_manager): | |
| audio = dl_manager.download_and_extract(f"{_DATA}audio.zip") | |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") | |
| audio = dl_manager.iter_files(audio) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "audio": audio, | |
| 'annotations': annotations | |
| }), | |
| ] | |
| def _generate_examples(self, audio, annotations): | |
| annotations_df = pd.read_csv(annotations, sep=';') | |
| audio = list(audio) | |
| audio = [audio[i:i + 4] for i in range(0, len(audio), 4)] | |
| for idx, set in enumerate(audio): | |
| for audio_file in set: | |
| if 'euphoric' in audio_file: | |
| euphoric = audio_file | |
| elif 'joyfully' in audio_file: | |
| joyfully = audio_file | |
| elif 'sad' in audio_file: | |
| sad = audio_file | |
| elif 'surprised' in audio_file: | |
| surprised = audio_file | |
| set_id = Path(set[0]).parent.name | |
| yield idx, { | |
| 'set_id': | |
| set_id, | |
| 'euphoric': | |
| euphoric, | |
| 'joyfully': | |
| joyfully, | |
| 'sad': | |
| sad, | |
| 'surprised': | |
| surprised, | |
| 'text': | |
| annotations_df.loc[annotations_df['set_id'] == set_id] | |
| ['text'].values[0], | |
| 'gender': | |
| annotations_df.loc[annotations_df['set_id'] == set_id] | |
| ['gender'].values[0], | |
| 'age': | |
| annotations_df.loc[annotations_df['set_id'] == set_id] | |
| ['age'].values[0], | |
| 'country': | |
| annotations_df.loc[annotations_df['set_id'] == set_id] | |
| ['country'].values[0] | |
| } | |