Upload audio-kw-in-context.py

audio-kw-in-context.py

@@ -1,145 +1,147 @@
-# 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.
-"""sil-ai/audio-kw-in-context is a subset of MLCommons/ml_spoken_words focusing on keywords found in the Bible'"""
-
-import json
-import os
-
-import datasets
-
-_CITATION = """\
-@InProceedings{huggingface:audio-kw-in-context,
-title = {audio-kw-in-context},
-author={Joshua Nemecek
-},
-year={2022}
-}
-"""
-_DESCRIPTION = 'sil-ai/audio-kw-in-context is a subset of MLCommons/ml_spoken_words focusing on keywords found in the Bible'
-_LANGUAGES = ['eng', 'ind', 'spa']
-_LANG_ISO_DICT = {'en':'eng','es':'spa','id':'ind'}
-_HOMEPAGE = 'https://ai.sil.org'
-_URLS = {"metadata": "bible-keyword-context.json",
-         "files": {lang: f'https://audio-keyword-spotting.s3.amazonaws.com/HF-context-v2/{lang}-kw-archive.tar.gz' for lang in _LANGUAGES},
-         }
-_LICENSE = 'CC-BY 4.0'
-_GENDERS = ["MALE", "FEMALE", "OTHER", "NAN"]
-
-class AudioKwInContextConfig(datasets.BuilderConfig):
-    """BuilderConfig for Audio-Kw-In-Context"""
-    def __init__(self, language='', **kwargs):
-        super(AudioKwInContextConfig, self).__init__(**kwargs)
-        self.language = _LANG_ISO_DICT.get(language, language)
-
-class AudioKwInContext(datasets.GeneratorBasedBuilder):
-    """Audio-Kw-In-Context class"""
-    BUILDER_CONFIGS = [AudioKwInContextConfig(name=x, description=f'Audio keyword spotting for language code {x}', language=x) for x in _LANGUAGES]
-    
-    DEFAULT_CONFIG_NAME = ''
-
-    BUILDER_CONFIG_CLASS = AudioKwInContextConfig
-
-    VERSION = datasets.Version("0.0.2")
-
-    def _info(self):
-        features = datasets.Features(
-                {
-                    "file": datasets.Value("string"),
-                    "language": datasets.Value("string"),
-                    "speaker_id": datasets.Value("string"),
-                    "sentence": datasets.Value("string"),
-                    "keywords": datasets.Sequence(datasets.Value("string")),  
-                    "audio": datasets.Audio(sampling_rate=16_000),
-                    "start_times": datasets.Sequence(datasets.Value("float32")),
-                    "end_times": datasets.Sequence(datasets.Value("float32")),
-                    "confidence": datasets.Sequence(datasets.Value("float32")),
-                }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,  
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        
-        if self.config.language == '':
-            raise ValueError('Please specify a language.')
-        elif self.config.language not in _LANGUAGES:
-            raise ValueError(f'{self.config.language} does not appear in the list of languages: {_LANGUAGES}')
-
-        data_dir = dl_manager.download(_URLS['metadata'])
-        with open(data_dir, 'r') as f:
-            filemeta = json.load(f)
-
-        audio_dir = dl_manager.download_and_extract(_URLS['files'][self.config.name])
-
-        langmeta = filemeta[self.config.language]
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "audio_dir": audio_dir,
-                    "data": langmeta,
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "audio_dir": audio_dir,
-                    "data": langmeta,
-                    "split": "dev",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "audio_dir": audio_dir,
-                    "data": langmeta,
-                    "split": "test",
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, audio_dir, data, split):
-        for key, row in enumerate(data[split]):
-            try:
-                tfile = os.path.join(audio_dir, row['file'])
-                if not tfile.endswith('.mp3'):
-                    os.rename(tfile, tfile + '.mp3')
-                    tfile += '.mp3'
-                yield key, {
-                    "file": tfile,
-                    "sentence": row.get('sentence'),
-                    "language": self.config.language,
-                    "speaker_id": row.get('speaker_id',row.get('client_id')),
-                    "keywords": row['keywords'],  
-                    "audio": tfile,
-                    "start_times": row.get('start_times'),
-                    "end_times": row.get('end_times'),
-                    "confidence": row.get('confidence'),
-                }
-            except Exception as e:
-                print(e)
-                print(f'In split {split}: {row["file"]} failed to load. Data may be missing.')
+# 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.
+"""sil-ai/audio-kw-in-context is a subset of MLCommons/ml_spoken_words focusing on keywords found in the Bible'"""
+
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+@InProceedings{huggingface:audio-kw-in-context,
+title = {audio-kw-in-context},
+author={Joshua Nemecek
+},
+year={2022}
+}
+"""
+_DESCRIPTION = 'sil-ai/audio-kw-in-context is a subset of MLCommons/ml_spoken_words focusing on keywords found in the Bible'
+_LANGUAGES = ['eng', 'ind', 'spa']
+_LANG_ISO_DICT = {'en':'eng','es':'spa','id':'ind'}
+_HOMEPAGE = 'https://ai.sil.org'
+_URLS = {"metadata": "bible-keyword-context.json",
+         "files": {lang: f'https://audio-keyword-spotting.s3.amazonaws.com/HF-context-v2/{lang}-kw-archive.tar.gz' for lang in _LANGUAGES},
+         }
+_LICENSE = 'CC-BY 4.0'
+_GENDERS = ["MALE", "FEMALE", "OTHER", "NAN"]
+
+class AudioKwInContextConfig(datasets.BuilderConfig):
+    """BuilderConfig for Audio-Kw-In-Context"""
+    def __init__(self, language='', **kwargs):
+        super(AudioKwInContextConfig, self).__init__(**kwargs)
+        self.language = _LANG_ISO_DICT.get(language, language)
+
+class AudioKwInContext(datasets.GeneratorBasedBuilder):
+    """Audio-Kw-In-Context class"""
+    BUILDER_CONFIGS = [AudioKwInContextConfig(name=x, description=f'Audio keyword spotting for language code {x}', language=x) for x in _LANGUAGES]
+    
+    DEFAULT_CONFIG_NAME = ''
+
+    BUILDER_CONFIG_CLASS = AudioKwInContextConfig
+
+    VERSION = datasets.Version("0.0.2")
+
+    def _info(self):
+        features = datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "language": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    "keywords": datasets.Sequence(datasets.Value("string")),  
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "start_times": datasets.Sequence(datasets.Value("float32")),
+                    "end_times": datasets.Sequence(datasets.Value("float32")),
+                    "confidence": datasets.Sequence(datasets.Value("float32")),
+                }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,  
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        
+        if self.config.language == '':
+            raise ValueError('Please specify a language.')
+        elif self.config.language not in _LANGUAGES:
+            raise ValueError(f'{self.config.language} does not appear in the list of languages: {_LANGUAGES}')
+
+        data_dir = dl_manager.download(_URLS['metadata'])
+        with open(data_dir, 'r') as f:
+            filemeta = json.load(f)
+
+        audio_dir = dl_manager.download_and_extract(_URLS['files'][self.config.name])
+
+        langmeta = filemeta[self.config.language]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "audio_dir": audio_dir,
+                    "data": langmeta,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "audio_dir": audio_dir,
+                    "data": langmeta,
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "audio_dir": audio_dir,
+                    "data": langmeta,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, audio_dir, data, split):
+        for key, row in enumerate(data[split]):
+            try:
+                trows = row['file'].split('/')
+                trow = '/'.join([trows[0], 'HF-context-v2',trows[1:]])
+                tfile = os.path.join(audio_dir, trow)
+                if not tfile.endswith('.mp3'):
+                    os.rename(tfile, tfile + '.mp3')
+                    tfile += '.mp3'
+                yield key, {
+                    "file": tfile,
+                    "sentence": row.get('sentence'),
+                    "language": self.config.language,
+                    "speaker_id": row.get('speaker_id',row.get('client_id')),
+                    "keywords": row['keywords'],  
+                    "audio": tfile,
+                    "start_times": row.get('start_times'),
+                    "end_times": row.get('end_times'),
+                    "confidence": row.get('confidence'),
+                }
+            except Exception as e:
+                print(e)
+                print(f'In split {split}: {row["file"]} failed to load. Data may be missing.')
                 pass