Merge pull request #458 from djanibekov/sarawak_malay

Closes #444  | Create dataset loader for Sarawak Malay

requirements.txt

@@ -24,3 +24,5 @@ typing_extensions
 scikit-learn==1.1.2
 pyarrow
 opencv-python>=4.9
+textgrid==1.5
+audiosegment==0.23.0

seacrowd/sea_datasets/sarawak_malay/sarawak_malay.py

@@ -0,0 +1,178 @@
+# 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 audiosegment
+import datasets
+import textgrid
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@INPROCEEDINGS{
+10337314,
+author={Rahim, Mohd Zulhafiz and Juan, Sarah Samson and Mohamad, Fitri Suraya},
+booktitle={2023 International Conference on Asian Language Processing (IALP)},
+title={Improving Speaker Diarization for Low-Resourced Sarawak Malay Language Conversational Speech Corpus},
+year={2023},
+pages={228-233},
+keywords={Training;Oral communication;Data models;Usability;Speech processing;Testing;Speaker diarization;x-vectors;clustering;low-resource;auto-labeling;pseudo-labeling;unsupervised},
+doi={10.1109/IALP61005.2023.10337314}}
+"""
+
+_DATASETNAME = "sarawak_malay"
+
+_DESCRIPTION = """\
+This is a Sarawak Malay conversation data for the purpose of speech technology research. \
+At the moment, this is an experimental data and currently used for investigating \
+speaker diarization. The data was collected by Faculty of Computer Science and \
+Information Technology, Universiti Malaysia Sarawak. The data consists of 38 conversations \
+that have been transcribed using Transcriber (see TextGrid folder), where each file \
+contains two speakers. Each conversation was recorded by different individuals using microphones \
+from mobile devices or laptops thus, different file formats were collected from the data collectors. \
+All data was then standardized to mono, 16000Khz, wav format.
+"""
+
+_HOMEPAGE = "https://github.com/sarahjuan/sarawakmalay"
+
+_LANGUAGES = ["zlm"]
+
+_LICENSE = Licenses.CC0_1_0.value
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://github.com/sarahjuan/sarawakmalay/archive/refs/heads/main.zip",
+}
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION, Tasks.TEXT_TO_SPEECH]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "1.0.0"
+
+
+class SarawakMalayDataset(datasets.GeneratorBasedBuilder):
+    """This is experimental Sarawak Malay conversation data collected by \
+        Universiti Malaysia Sarawak for speech technology research, \
+        specifically speaker diarization. The data includes 38 conversations, \
+        each with two speakers, recorded on various devices and then standardized to mono, \
+        16000Khz, wav format."""
+
+    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=f"{_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=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "text": datasets.Value("string"),
+                    "metadata": {
+                        "malay_text": 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]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "sarawakmalay-main"),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        id_counter = 0
+        filenames = filter(lambda x: x.endswith(".wav"), os.listdir(f"{filepath}/wav"))
+        filenames = map(lambda x: x.replace(".wav", ""), filenames)
+
+        os.makedirs(f"{filepath}/segmented", exist_ok=True)
+        for i, filename in enumerate(filenames):
+            info = textgrid.TextGrid.fromFile(f"{filepath}/TextGrid/{filename}.TextGrid")
+            if len(info) == 3:
+                sarawak_conversation, malay_conversation, speakers = info
+            else:
+                sarawak_conversation, malay_conversation, speakers, _ = info
+
+            audio_file = audiosegment.from_file(f"{filepath}/wav/{filename}.wav").resample(sample_rate_Hz=16000)
+
+            for sarawak_tg, malay_tg, speaker in zip(sarawak_conversation, malay_conversation, speakers):
+                start, end, text = sarawak_tg.minTime, sarawak_tg.maxTime, sarawak_tg.mark
+                malay_text = malay_tg.mark
+                speaker_id = speaker.mark
+
+                start_sec, end_sec = int(start * 1000), int(end * 1000)
+                segment = audio_file[start_sec:end_sec]
+                segement_filename = f"{filepath}/segmented/{filename}-{round(start, 0)}-{round(end, 0)}.wav"
+                segment.export(segement_filename, format="wav")
+
+                if self.config.schema == "source":
+                    yield id_counter, {
+                        "id": id_counter,
+                        "speaker_id": speaker_id,
+                        "path": f"{filepath}/wav/{filename}.wav",
+                        "audio": segement_filename,
+                        "text": text,
+                        "metadata": {
+                            "malay_text": malay_text,
+                        },
+                    }
+
+                elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+                    yield id_counter, {"id": id_counter, "speaker_id": speaker_id, "path": f"{filepath}/wav/{filename}.wav", "audio": segement_filename, "text": text, "metadata": {"speaker_age": None, "speaker_gender": None}}
+
+                id_counter += 1