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gowajee.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.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA,
+                                      Licenses, Tasks)
+
+_CITATION = """
+@techreport{gowajee,
+    title = {{Gowajee Corpus}},
+    author = {Ekapol Chuangsuwanich and Atiwong Suchato and Korrawe Karunratanakul and Burin Naowarat and Chompakorn CChaichot
+and Penpicha Sangsa-nga and Thunyathon Anutarases and Nitchakran Chaipojjana and Yuatyong Chaichana},
+    year = {2020},
+    institution = {Chulalongkorn University, Faculty of Engineering, Computer Engineering Department},
+    month = {12},
+    Date-Added = {2023-07-30},
+    url = {https://github.com/ekapolc/gowajee_corpus}
+    note = {Version 0.9.3}
+}
+"""
+
+_DATASETNAME = "gowajee"
+
+_DESCRIPTION = """
+The Gowajee corpus was collected in the Automatic Speech Recognition class offered at
+Chulalongkorn University as a homework assignment. Each group was asked to come up with an
+example smart home application.
+"""
+
+_HOMEPAGE = "https://github.com/ekapolc/gowajee_corpus"
+
+_LANGUAGES = ["tha"]
+
+_LICENSE = Licenses.MIT.value
+
+_LOCAL = False
+
+_URL = "https://drive.google.com/file/d/1soriRMMuZI5w5RZOjAnbpocBZxT6i1-l/view"  # ~1.5GB
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_TO_TEXT_TRANSLATION]
+_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}"  # sptext
+
+_SOURCE_VERSION = "0.9.3"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class GowajeeDataset(datasets.GeneratorBasedBuilder):
+    """Automatic Speech Recognition dataset on smart home application where the wakeword is "Gowajee"."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    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_SCHEMA}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=_SEACROWD_SCHEMA,
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "transcription": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]]  # 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."""
+        # check if gdown is installed
+        try:
+            import gdown
+        except ImportError as err:
+            raise ImportError("Please install `gdown` to enable downloading data from google drive.") from err
+
+        # download data from gdrive
+        output_dir = Path.cwd() / "data" / "gowajee"
+        output_dir.mkdir(parents=True, exist_ok=True)
+        output_file = output_dir / "gowajee_v0-9-3.zip"
+        if not output_file.exists():
+            gdown.download(_URL, str(output_file), fuzzy=True)
+        else:
+            print(f"File already downloaded: {str(output_file)}")
+
+        # extract data
+        data_dir = Path(dl_manager.extract(output_file)) / "v0.9.2"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        text_file = data_dir / split / "text"
+        utt2spk_file = data_dir / split / "utt2spk"
+        wav_scp_file = data_dir / split / "wav.scp"
+
+        # load the data
+        with open(text_file, "r", encoding="utf-8") as f:
+            text_lines = f.readlines()
+            text_lines = [line.strip().split(" ", 1) for line in text_lines]
+        with open(utt2spk_file, "r", encoding="utf-8") as f:
+            utt2spk_lines = f.readlines()
+            utt2spk_lines = [line.strip().split(" ") for line in utt2spk_lines]
+        with open(wav_scp_file, "r", encoding="utf-8") as f:
+            wav_scp_lines = f.readlines()
+            wav_scp_lines = [line.strip().split(" ", 1) for line in wav_scp_lines]
+
+        assert len(text_lines) == len(utt2spk_lines) == len(wav_scp_lines), f"Length of text_lines: {len(text_lines)}, utt2spk_lines: {len(utt2spk_lines)}, wav_scp_lines: {len(wav_scp_lines)}"
+
+        text_df = pd.DataFrame(text_lines, columns=["utt_id", "text"])
+        utt2spk_df = pd.DataFrame(utt2spk_lines, columns=["utt_id", "speaker"])
+        wav_df = pd.DataFrame(wav_scp_lines, columns=["utt_id", "wav_path"])
+        merged_df = pd.merge(text_df, utt2spk_df, on="utt_id")
+        merged_df = pd.merge(merged_df, wav_df, on="utt_id")
+
+        for _, row in merged_df.iterrows():
+            wav_file = data_dir / row["wav_path"]
+
+            if self.config.schema == "source":
+                yield row["utt_id"], {
+                    "audio": str(wav_file),
+                    "transcription": row["text"],
+                    "speaker_id": row["speaker"],
+                }
+            elif self.config.schema == _SEACROWD_SCHEMA:
+                yield row["utt_id"], {
+                    "id": row["utt_id"],
+                    "path": str(wav_file),
+                    "audio": str(wav_file),
+                    "text": row["text"],
+                    "speaker_id": row["speaker"],
+                    "metadata": None,
+                }