fix

MOCKS-test.py

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+# coding=utf-8
+# Copyright 2023 The HuggingFace Datasets Authors.
+#
+# Licensed under the Creative Commons version 4.0 and Mozilla Public License version 2.0,
+# (the "Licenses"); you may not use this file except in compliance with the Licenses.
+# You may obtain a copies of the Licenses at
+#
+#     https://creativecommons.org/licenses/by/4.0/
+#     and https://www.mozilla.org/en-US/MPL/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 Licenses for the specific language governing permissions and
+# limitations under the Licenses.
+
+# Lint as: python3
+
+import csv
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{pudo23_interspeech,
+  author={Mikołaj Pudo and Mateusz Wosik and Adam Cieślak and Justyna Krzywdziak and Bożena Łukasiak and Artur Janicki},
+  title={{MOCKS} 1.0: Multilingual Open Custom Keyword Spotting Testset},
+  year={2023},
+  booktitle={Proc. Interspeech 2023},
+}
+"""
+
+_DESCRIPTION = """\
+Multilingual Open Custom Keyword Spotting Testset (MOCKS) is a comprehensive 
+audio testset for evaluation and benchmarking Open-Vocabulary Keyword Spotting (OV-KWS) models.
+"""
+
+
+#_BASE_URL = "https://huggingface.co/datasets/voiceintelligenceresearch/MOCKS/tree/main"
+_BASE_URL = "https://huggingface.co/datasets/mikolaj-p/MOCKS-test/tree/main"
+_DL_URLS_TEMPLATE = {
+    "data": "%s/%s/test/%s/data.tar.gz",
+    "transcription" : "%s/%s/test/data_%s_transcription.tsv",
+    "positive" : "%s/%s/test/%s/all.pair.positive.tsv",
+    "similar" : "%s/%s/test/%s/all.pair.similar.tsv",
+    "different" : "%s/%s/test/%s/all.pair.different.tsv",
+    "positive_subset" : "%s/%s/test/%s/subset.pair.positive.tsv",
+    "similar_subset" : "%s/%s/test/%s/subset.pair.similar.tsv",
+    "different_subset" : "%s/%s/test/%s/subset.pair.different.tsv",
+}
+
+_MOCKS_SETS = [
+    "en.LS-clean",
+]
+#    "en.LS-other",
+#    "en.MCV",
+#    "de.MCV",
+#    "es.MCV",
+#    "fr.MCV",
+#    "it.MCV"]
+
+_MOCKS_SUFFIXES = [
+    "",
+    ".positive",
+    ".similar",
+    ".different",
+    ".subset",
+    ".positive_subset",
+    ".similar_subset",
+    ".different_subset"]
+
+
+class Mocks(datasets.GeneratorBasedBuilder):
+    """Mocks Dataset."""
+    DEFAULT_CONFIG_NAME = "en.LS-clean"
+
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name=subset+suffix, description=subset+suffix)
+            for subset in _MOCKS_SETS for suffix in _MOCKS_SUFFIXES]
+
+    def _info(self):
+        logger.info("info")
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                    "keyword_id": datasets.Value("string"),
+                    "keyword_transcription": datasets.Value("string"),
+                    "test_id": datasets.Value("string"),
+                    "test_transcription": datasets.Value("string"),
+                    "test_audio": datasets.Audio(sampling_rate=16000),
+                    "label": datasets.Value("bool"),
+                }
+            ),
+            homepage=_BASE_URL,
+            citation=_CITATION
+        )
+
+
+    def _split_generators(self, dl_manager):
+        logger.info("split_generator")
+        name_split = self.config.name.split(".")
+        subset_lang = name_split[0]
+        subset_name = name_split[1]
+
+        if len(name_split) == 2:
+            pairs_types = ["positive", "similar", "different"]
+        elif name_split[2] == "subset":
+            pairs_types = ["positive_subset", "similar_subset", "different_subset"]
+        else:
+            pairs_types = [name_split[2]]
+
+        offline_archive_path = dl_manager.download({
+            k: v%(subset_lang, subset_name, "offline") 
+            for k, v in _DL_URLS_TEMPLATE.items()
+            })
+#        online_archive_path = dl_manager.download({
+#            k: v%(subset_lang, subset_name, "online") 
+#            for k, v in _DL_URLS_TEMPLATE.items()
+#            })
+
+        split_offline = [datasets.SplitGenerator(
+                name="offline",
+                gen_kwargs={
+                    "audio_files": dl_manager.iter_archive(offline_archive_path["data"]),
+                    "transcription_keyword": offline_archive_path["transcription"],
+                    "transcription_test": offline_archive_path["transcription"],
+                    "pairs": [offline_archive_path[pair_type] for pair_type in pairs_types],
+                }
+            )
+        ]
+
+#        split_online = [datasets.SplitGenerator(
+#                name="online",
+#                gen_kwargs={
+#                    "audio_files": dl_manager.iter_archive(online_archive_path["data"]),
+#                    "transcription_keyword": offline_archive_path["transcription"],
+#                    "transcription_test": online_archive_path["transcription"],
+#                    "pairs": [online_archive_path[pair_type] for pair_type in pairs_types],
+#                }
+#            )
+#        ]
+
+#        return split_offline + split_online
+        return split_offline
+
+
+    def _read_transcription(self, transcription_path):
+        transcription_metadata = {}
+
+        with open(transcription_path, encoding="utf-8") as f:
+            reader = csv.reader(f, delimiter="\t")
+            next(reader, None)
+
+            for row in reader:
+                _, audio_id = os.path.split(row[0])
+                transcription = row[1]
+                transcription_metadata[audio_id] = {
+                    "audio_id": audio_id,
+                    "transcription": transcription}
+
+        return transcription_metadata
+
+
+    def _generate_examples(self, audio_files, transcription_keyword, transcription_test, pairs):
+        transcription_keyword_metadata = self._read_transcription(transcription_keyword)
+
+        transcription_test_metadata = self._read_transcription(transcription_test)
+
+        pair_metadata = {}
+        for pair in pairs:
+            with open(pair, encoding="utf-8") as f:
+                reader = csv.reader(f, delimiter="\t")
+                next(reader, None)
+
+                for row in reader:
+                    _, keyword_id = os.path.split(row[0])
+                    _, test_id = os.path.split(row[1])
+
+                    if keyword_id not in transcription_keyword_metadata:
+                        logger.error("No transcription and audio for keyword %s"%(keyword_id))
+                        continue
+                    if test_id not in transcription_test_metadata:
+                        logger.error("No transcription and audio for test case %s"%(test_id))
+                        continue
+
+                    if test_id not in pair_metadata:
+                        pair_metadata[test_id] = []
+
+                    pair_metadata[test_id].append([keyword_id, int(row[-1])])
+
+        id_ = 0
+        for test_path, test_f in audio_files:
+            _, test_id = os.path.split(test_path)
+            if test_id in pair_metadata:
+                test_audio = {"bytes": test_f.read()}
+                for keyword_id, label in pair_metadata[test_id]:
+                    yield id_, {
+                        "keyword_id": keyword_id,
+                        "keyword_transcription": transcription_keyword_metadata[keyword_id]["transcription"],
+                        "test_id": test_id,
+                        "test_transcription": transcription_test_metadata[test_id]["transcription"],
+                        "test_audio": test_audio,
+                        "label": label}
+                    id_ += 1
+