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asr_malcsc.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
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+# no bibtex citation
+_CITATION = ""
+_DATASETNAME = "asr_malcsc"
+_DESCRIPTION = """\
+This open-source dataset consists of 5 hours of transcribed Malay
+conversational speech on certain topics, where ten conversations between five
+pairs of speakers were contained.
+"""
+
+_HOMEPAGE = "https://magichub.com/datasets/malay-conversational-speech-corpus/"
+_LANGUAGES = ["zlm"]
+_LICENSE = Licenses.CC_BY_NC_ND_4_0.value
+_LOCAL = False
+_URLS = {
+    _DATASETNAME: "https://magichub.com/df/df.php?file_name=Malay_Conversational_Speech_Corpus.zip",
+}
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ASRMalcscDataset(datasets.GeneratorBasedBuilder):
+    """ASR-Malcsc consists transcribed Malay conversational speech on certain topics"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "sptext"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "channel": datasets.Value("string"),
+                    "uttrans_id": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "topic": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "timestamp": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "speaker_gender": datasets.Value("string"),
+                    "speaker_age": datasets.Value("int64"),
+                    "speaker_region": datasets.Value("string"),
+                    "speaker_device": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.speech_text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        data_paths = {
+            _DATASETNAME: Path(dl_manager.download_and_extract(_URLS[_DATASETNAME])),
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_paths[_DATASETNAME],
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        # read AUDIOINFO file
+        # columns: channel, uttrans_id, speaker_id, topic
+        audioinfo_filepath = os.path.join(filepath, "AUDIOINFO.txt")
+        with open(audioinfo_filepath, "r", encoding="utf-8") as audioinfo_file:
+            audioinfo_data = audioinfo_file.readlines()
+        audioinfo_data = audioinfo_data[1:]  # remove header
+        audioinfo_data = [s.strip("\n").split("\t") for s in audioinfo_data]
+
+        # read SPKINFO file
+        # columns: channel, speaker_id, gender, age, region, device
+        spkinfo_filepath = os.path.join(filepath, "SPKINFO.txt")
+        with open(spkinfo_filepath, "r", encoding="utf-8") as spkinfo_file:
+            spkinfo_data = spkinfo_file.readlines()
+        spkinfo_data = spkinfo_data[1:]  # remove header
+        spkinfo_data = [s.strip("\n").split("\t") for s in spkinfo_data]
+        for i, s in enumerate(spkinfo_data):
+            if s[2] == "M":
+                s[2] = "male"
+            elif s[2] == "F":
+                s[2] = "female"
+            else:
+                s[2] = None
+        # dictionary of metadata of each speaker
+        spkinfo_dict = {s[1]: {"speaker_gender": s[2], "speaker_age": int(s[3]), "speaker_region": s[4], "speaker_device": s[5]} for s in spkinfo_data}
+
+        num_sample = len(audioinfo_data)
+
+        for i in range(num_sample):
+            # wav file
+            wav_path = os.path.join(filepath, "WAV", audioinfo_data[i][1])
+            # transcription file
+            transcription_path = os.path.join(filepath, "TXT", audioinfo_data[i][1].replace("wav", "txt"))
+            with open(transcription_path, "r", encoding="utf-8") as transcription_file:
+                file_i = transcription_file.readlines()
+            # remove redundant speaker info from transcription file
+            file_i = [s.strip("\n").split("\t") for s in file_i]
+            transcription = [s[-1] for s in file_i]
+            timestamp = [s[0] for s in file_i]
+            text = " \n ".join(transcription)
+            timestamp_text = " \n ".join(timestamp)
+
+            if self.config.schema == "source":
+                example = {
+                    "id": audioinfo_data[i][1].strip(".wav"),
+                    "channel": audioinfo_data[i][0],
+                    "uttrans_id": audioinfo_data[i][1],
+                    "speaker_id": audioinfo_data[i][2],
+                    "topic": audioinfo_data[i][3],
+                    "text": text,
+                    "timestamp": timestamp_text,
+                    "path": wav_path,
+                    "audio": wav_path,
+                    "speaker_gender": spkinfo_dict[audioinfo_data[i][2]]["speaker_gender"],
+                    "speaker_age": spkinfo_dict[audioinfo_data[i][2]]["speaker_age"],
+                    "speaker_region": spkinfo_dict[audioinfo_data[i][2]]["speaker_region"],
+                    "speaker_device": spkinfo_dict[audioinfo_data[i][2]]["speaker_device"],
+                }
+            elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+                example = {
+                    "id": audioinfo_data[i][1].strip(".wav"),
+                    "speaker_id": audioinfo_data[i][2],
+                    "path": wav_path,
+                    "audio": wav_path,
+                    "text": text,
+                    "metadata": {"speaker_age": spkinfo_dict[audioinfo_data[i][2]]["speaker_age"], "speaker_gender": spkinfo_dict[audioinfo_data[i][2]]["speaker_gender"]},
+                }
+
+            yield i, example