add script

psst.py

@@ -0,0 +1,127 @@
+# 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()
+                            }
+                        }