# 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, }