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# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
# Copyright 2022 Jim O'Regan
#
# 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.
# Lint as: python3
from email.mime import audio
from pathlib import Path
import os
import csv
import datasets
from datasets.tasks import AutomaticSpeechRecognition
_DESCRIPTION = """
The PSST Challenge focuses on a technically-challenging and clinically
important task—high-accuracy automatic phoneme recognition of disordered
speech, in a diagnostic context—which has applications in many different
areas relating to speech and language disorders.
"""
class PSSTDataset(datasets.GeneratorBasedBuilder):
"""PSST Dataset"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="psst"),
]
# utterance_id session test prompt transcript correctness aq_index duration_frames filename
def _info(self):
features = datasets.Features(
{
"utterance_id": datasets.Value("string"),
"session": datasets.Value("string"),
"test": datasets.Value("string"),
"prompt": datasets.Value("string"),
"transcript": datasets.Value("string"),
"phonemes": datasets.Sequence(datasets.Value("string")),
"correctness": datasets.Value("bool"),
"aq_index": datasets.Value("float"),
"duration_frames": datasets.Value("uint64"),
"audio": datasets.Audio(sampling_rate=16_000)
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage="https://psst.study/",
task_templates=[
AutomaticSpeechRecognition(audio_file_path_column="filename", transcription_column="transcript")
],
)
def _split_generators(self, dl_manager):
if hasattr(dl_manager, 'manual_dir') and dl_manager.manual_dir is not None:
data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
else:
raise Exception("No path to data specified")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"split": "train",
"data_dir": data_dir
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"split": "valid",
"data_dir": data_dir
},
),
]
# utterance_id session test prompt transcript correctness aq_index duration_frames filename
def _generate_examples(
self, split, data_dir
):
"""Yields examples as (key, example) tuples. """
data_path = Path(data_dir)
split_path = data_path / split
if not split_path.exists():
raise Exception(f"{split} directory not found ({split_path})")
utterances = split_path / "utterances.tsv"
if not utterances.exists():
raise Exception(f"utterances.tsv not found in {split} directory ({split_path})")
with open(utterances) as tsvfile:
data = csv.DictReader(tsvfile, delimiter='\t')
for row in data:
audiopath = split_path / row["filename"]
if audiopath.exists():
with open(audiopath, "rb") as audiofile:
yield row["utterance_id"], {
"utterance_id": row["utterance_id"],
"session": row["session"],
"test": row["test"],
"prompt": row["prompt"],
"transcript": row["transcript"],
"phonemes": row["transcript"].strip().split(" "),
"correctness": (row["correctness"] == "True"),
"aq_index": float(row["aq_index"]),
"duration_frames": int(row["duration_frames"]),
"audio": {
"path": str(audiopath),
"bytes": audiofile.read()
}
}
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