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| import os |
| import datasets |
|
|
| logger = datasets.logging.get_logger(__name__) |
|
|
|
|
| """ Toronto emotional speech set (TESS) Dataset""" |
|
|
| _CITATION = """\ |
| @data{SP2/E8H2MF_2020, |
| author = {Pichora-Fuller, M. Kathleen and Dupuis, Kate}, |
| publisher = {Borealis}, |
| title = {{Toronto emotional speech set (TESS)}}, |
| year = {2020}, |
| version = {DRAFT VERSION}, |
| doi = {10.5683/SP2/E8H2MF}, |
| url = {https://doi.org/10.5683/SP2/E8H2MF} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| These stimuli were modeled on the Northwestern University Auditory |
| Test No. 6 (NU-6; Tillman & Carhart, 1966). |
| A set of 200 target words were spoken in the carrier phrase |
| "Say the word _____' by two actresses (aged 26 and 64 years) and |
| recordings were made of the set portraying each of seven emotions |
| (anger, disgust, fear, happiness, pleasant surprise, sadness, and neutral). |
| There are 2800 stimuli in total. Two actresses were recruited from |
| the Toronto area. Both actresses speak English as their first language, |
| are university educated, and have musical training. Audiometric testing |
| indicated that both actresses have thresholds within the normal range. (2010-06-21) |
| """ |
|
|
| _HOMEPAGE = "https://doi.org/10.5683/SP2/E8H2MF" |
| _LICENSE = "CC BY-NC 4.0" |
|
|
| _ROOT_DIR = "tess" |
| _DATA_URL = f"data/{_ROOT_DIR}.zip" |
|
|
| _CLASS_NAMES = [ |
| "neutral", |
| "happy", |
| "sad", |
| "angry", |
| "fear", |
| "disgust", |
| "ps", |
| ] |
|
|
|
|
| class TessDataset(datasets.GeneratorBasedBuilder): |
| """The Tess dataset""" |
|
|
| VERSION = datasets.Version("1.0.0") |
|
|
| def _info(self): |
| sampling_rate = 24_400 |
| features = datasets.Features( |
| { |
| "path": datasets.Value("string"), |
| "audio": datasets.Audio(sampling_rate=sampling_rate), |
| "speaker_id": datasets.Value("string"), |
| "speaker_age": datasets.Value("int8"), |
| "text": datasets.Value("string"), |
| "word": datasets.Value("string"), |
| "label": datasets.ClassLabel(names=_CLASS_NAMES), |
| } |
| ) |
|
|
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| license=_LICENSE, |
| |
| ) |
|
|
| def _split_generators(self, dl_manager): |
|
|
| archive_path = dl_manager.download_and_extract(_DATA_URL) |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={"archive_path": archive_path}, |
| ) |
| ] |
|
|
| def _generate_examples(self, archive_path): |
| "speaker_word_label.wav (audio/wav) num bytes." |
|
|
| filepath = os.path.join(archive_path, _ROOT_DIR, "MANIFEST.TXT") |
|
|
| examples = {} |
| with open(filepath, encoding="utf-8") as f: |
| for row in f: |
| filename = row.split()[0] |
| speakerId, word, label = filename.split(".")[0].split("_") |
| audio_path = os.path.join(archive_path, _ROOT_DIR, filename) |
| examples[audio_path] = { |
| "path": audio_path, |
| "speaker_id": speakerId, |
| "speaker_age": 64 if speakerId == "OAF" else 26, |
| "text": f"Say the word {word}", |
| "word": word, |
| "label": label, |
| } |
|
|
| id_ = 0 |
| for path in list(examples.keys()): |
| with open(path, "rb") as f: |
| audio_bytes = f.read() |
| audio = {"path": path, "bytes": audio_bytes} |
| yield id_, {**examples[path], "audio": audio} |
| id_ += 1 |
|
|
