tess.py

# coding=utf-8
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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import os
import datasets  # type: ignore

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,
            # task_templates=[datasets.TaskTemplate("audio-classification")],
        )

    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