init

README.md

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-license: cc-by-nc-4.0
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data/tess.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:752556de8c109b1d27d163e2a00b0ec8b8186600fd233ab8bd369d003d3cb218
+size 224036453

tess.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+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",
+    "calm",
+    "happy",
+    "sad",
+    "angry",
+    "fearful",
+    "disgust",
+    "surprised",
+]
+
+
+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"),
+                "text": 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,
+                    "speakerId": speakerId,
+                    "word": word,
+                    "class": 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