Adding build script

.python-version

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+bigos-hf

pl-asr-bigos-v2.py

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+# Copyright 2020 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""Build script for Polish ASR-BIGOS dataset"""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+print("Running script")
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{FedCSIS20231609,
+	author={Michał Junczyk},
+	pages={585–590},
+	title={BIGOS - Benchmark Intended Grouping of Open Speech Corpora for Polish Automatic Speech Recognition},
+	booktitle={Proceedings of the 18th Conference on Computer Science and Intelligence Systems},
+	year={2023},
+	editor={Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik Ślęzak},
+	publisher={IEEE},
+	doi={10.15439/2023F1609},
+	url={http://dx.doi.org/10.15439/2023F1609},
+	volume={35},
+	series={Annals of Computer Science and Information Systems}
+}
+"""
+
+_DESCRIPTION = """\
+BIGOS (Benchmark Intended Grouping of Open Speech) dataset goal is to simplify access to the openly available Polish speech corpora and
+enable systematic benchmarking of open and commercial Polish ASR systems.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/michaljunczyk/pl-asr-bigos"
+
+_LICENSE = "CC-BY-SA-4.0"
+
+_BIGOS_SUBSETS = ["pjatk-clarin_mobile-15", "pjatk-clarin_studio-15", "fair-mls-20", "mailabs-corpus_librivox-19", "mozilla-common_voice_15-23", "pwr-azon_read-20", "pwr-azon_spont-20", "pwr-maleset-unk", "pwr-shortwords-unk", "pwr-viu-unk", "google-fleurs-22", "polyai-minds14-21"]
+_ALL_CONFIGS = []
+
+for subset in _BIGOS_SUBSETS:
+    _ALL_CONFIGS.append(subset)
+
+_ALL_CONFIGS.append("all")
+
+_BASE_PATH = "data/{subset}/"
+_DATA_URL = _BASE_PATH + "{split}.tar.gz"
+_META_URL = _BASE_PATH + "{split}.tsv"
+
+class BigosConfig(datasets.BuilderConfig):
+
+    def __init__(
+        self, name, description, citation, homepage
+    ):
+        super(BigosConfig, self).__init__(
+            name=self.name,
+            version=datasets.Version("2.0.0", ""),
+            description=self.description,
+        )
+        self.name = name
+        self.description = description
+        self.citation = citation
+        self.homepage = homepage
+
+
+def _build_config(name):
+    return BigosConfig(
+        name=name,
+        description=_DESCRIPTION,
+        citation=_CITATION,
+        homepage=_HOMEPAGE,
+    )
+
+class Bigos(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_WRITER_BATCH_SIZE = 2
+    #in case the issue persits, investigatae the following:
+    #https://github.com/huggingface/datasets/issues/4057
+    BUILDER_CONFIGS = [_build_config(name) for name in _ALL_CONFIGS]
+
+    def _info(self):
+        task_templates = None
+        features = datasets.Features(
+            {
+                "audioname": datasets.Value("string"),
+                "split": datasets.Value("string"),
+                "dataset": datasets.Value("string"),
+                "speaker_id": datasets.Value("string"),
+                "ref_orig": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "samplingrate_orig": datasets.Value("int32"),
+                "sampling_rate": datasets.Value("int32"),
+                "audiopath_bigos": datasets.Value("string"),
+                #"ref_spoken": datasets.Value("string"),
+                #"ref_written": datasets.Value("string"),
+                #"hyp_whisper_cloud": datasets.Value("string"),
+                #"hyp_google_default": datasets.Value("string"),
+                #"hyp_azure_default": datasets.Value("string"),
+                #"hyp_whisper_tiny": datasets.Value("string"),
+                #"hyp_whisper_base": datasets.Value("string"),
+                #"hyp_whisper_small": datasets.Value("string"),
+                #"hyp_whisper_medium": datasets.Value("string"),
+                #"hyp_whisper_large": datasets.Value("string")
+                #"gender": datasets.ClassLabel(names=["male", "female", "other"]),
+                #"speaker_id": datasets.Value("int32"),
+                #"raw_transcription": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=self.config.description + "\n" + _DESCRIPTION,
+            features=features,
+            supervised_keys=("audio", "ref_orig"),
+            homepage=self.config.homepage,
+            citation=self.config.citation + "\n" + _CITATION,
+            task_templates=task_templates,
+        )
+
+    def _split_generators(self, dl_manager):
+        splits = ["test", "train", "validation"]
+
+        if self.config.name == "all":
+            data_urls = {split: [_DATA_URL.format(subset=subset,split=split) for subset in _BIGOS_SUBSETS] for split in splits}
+            meta_urls = {split: [_META_URL.format(subset=subset,split=split) for subset in _BIGOS_SUBSETS] for split in splits}
+        else:
+            data_urls = {split: [_DATA_URL.format(subset=self.config.name, split=split)] for split in splits}
+            meta_urls = {split: [_META_URL.format(subset=self.config.name, split=split)] for split in splits}
+
+        archive_paths = dl_manager.download(data_urls)
+        local_extracted_archives = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+        archive_iters = {split: [dl_manager.iter_archive(path) for path in paths] for split, paths in archive_paths.items()}
+
+        meta_paths = dl_manager.download(meta_urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "local_extracted_archives": local_extracted_archives.get("test", [None] * len(meta_paths.get("test"))),
+                    "archive_iters": archive_iters.get("test"),
+                    "text_paths": meta_paths.get("test")
+                },
+            ),
+    
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "local_extracted_archives": local_extracted_archives.get("train", [None] * len(meta_paths.get("train"))),
+                    "archive_iters": archive_iters.get("train"),
+                    "text_paths": meta_paths.get("train")
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "local_extracted_archives": local_extracted_archives.get("validation", [None] * len(meta_paths.get("validation"))),
+                    "archive_iters": archive_iters.get("validation"),
+                    "text_paths": meta_paths.get("validation")
+                },
+            ),
+        ]
+
+    def _get_data(self, lines, subset_id):
+        data = {}
+        for line in lines:
+            # parse TSV
+            if isinstance(line, bytes):
+                line = line.decode("utf-8")
+            (
+                _id,
+                split,
+                dataset,
+                speaker_id,
+                sampling_rate_orig,
+                sampling_rate,
+                ref_orig,
+                audio_path_bigos,
+            ) = line.strip().split("\t")
+
+
+            data[audio_path_bigos] = {
+                "audioname": _id,
+                "split": split,
+                "dataset": dataset,
+                "speaker_id": speaker_id,
+                "samplingrate_orig": sampling_rate_orig,
+                "sampling_rate": sampling_rate,
+                "ref_orig": ref_orig,
+                "audiopath_bigos": audio_path_bigos,
+                #"age": int(age),
+            }
+
+        return data
+
+    def _generate_examples(self, local_extracted_archives, archive_iters, text_paths):
+        assert len(local_extracted_archives) == len(archive_iters) == len(text_paths)
+        key = 0
+
+        print("Generating  examples")
+        if self.config.name == "all":
+            subsets = _BIGOS_SUBSETS
+        else:
+            subsets = [self.config.name]
+
+        for archive, text_path, local_extracted_path, subset_id in zip(archive_iters, text_paths, local_extracted_archives, subsets):
+            with open(text_path, encoding="utf-8") as f:
+                lines = f.readlines()
+                data = self._get_data(lines, subset_id)
+
+            for audio_path, audio_file in archive:
+                #print("audio_path: ", audio_path)
+
+                audio_filename = audio_path.split("/")[-1]
+                #if audio_filename not in data.keys():
+                #    continue
+                
+                #print("audio_filename: ", audio_filename)
+                result = data[audio_filename]
+                #print("result: ", result)
+                extracted_audio_path = (
+                    os.path.join(local_extracted_path, audio_filename)
+                    if local_extracted_path is not None
+                    else None
+                )
+                #result["path"] = extracted_audio_path
+                result["audio"] = {"path": audio_path, "bytes": audio_file.read()}
+                yield key, result
+                key += 1

test.py

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+import datasets
+from datasets import load_dataset
+
+# test reading all subsets for "test" split
+_BIGOS_SUBSETS = ["pjatk-clarin_mobile-15", "pjatk-clarin_studio-15", "fair-mls-20", "mailabs-corpus_librivox-23", "mozilla-common_voice_15-19", "pwr-azon_read-20", "pwr-azon_spont-20", "pwr-maleset-unk", "pwr-shortwords-unk", "pwr-viu-unk", "google-fleurs-22", "polyai-minds14-21"]
+
+splits=["test", "validation", "train"]
+# Refer to documentation for the descriptions of splits and subsets
+
+print("Testing dataset all subsets")
+
+dataset_name = "pl-asr-bigos-v2"
+hf_account = "amu-cai"
+
+hf_db_name="/".join([hf_account,dataset_name])
+print(hf_db_name, hf_db_name)
+
+print("Testing dataset: ${dataset_name} from account ${hf_account} for all subsets")
+
+print("Checking build script locally - test split")
+dataset_local = load_dataset(f"{dataset_name}.py", "all", split="test")
+
+print("Checking build script locally - test split")
+dataset_local = load_dataset(f"{dataset_name}.py", "all", split="validation")
+
+print("Checking build script on huggingface.co")
+dataset_hf = load_dataset(hf_db_name, "all")  
+
+"""
+for split in splits:    
+    print("Checking split: ", split)
+    print(dataset[split][0])
+    #TODO - rename to include date of test set creation in order to check if adding new split removes the previous one
+    if split == "test":
+        assert len(dataset["test"]) == 1900
+        _BIGOS_SUBSETS = ["clarin-pjatk-mobile-15", "clarin-pjatk-studio-15", "fair-mls-20", "mailabs-19", "mozilla-common-voice-19", "pwr-azon-read-20", "pwr-azon-spont-20", "pwr-maleset-unk", "pwr-shortwords-unk", "pwr-viu-unk"]
+
+        print("Testing specific subsets")
+        for subset in _BIGOS_SUBSETS:
+            dataset = load_dataset('michaljunczyk/pl-asr-bigos', subset)
+            print("subset: ", subset)
+
+            for split in splits:
+                print("Checking split: ", split)
+                print(dataset[split][0])
+                if split == "test":
+                    if subset == "pwr-azon-spont-20":
+                        assert len(dataset["test"]) == 100
+                    else:
+                        assert len(dataset["test"]) == 200
+                    print(dataset)
+    
+    # TODO - add more tests for other splits
+"""