Load files are added, cleaning is needed

Signed-off-by: Vladislav Sokolovskii <[email protected]>

voxceleb/voxceleb.py

@@ -0,0 +1,194 @@
+# 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
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+import sys
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "voxceleb": "https://public-dataset-model-store.awsdev.asapp.com/users/sshon/public/slue/slue-voxceleb_v0.2_blind.zip"
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class SLUEVoxceleb(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="voxceleb", version=VERSION, description="This part of my dataset covers a first domain"),
+    ]
+
+    #DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "voxceleb": # This is the name of the configuration selected in BUILDER_CONFIGS above
+            # get the current split
+
+            features = datasets.Features(
+        {
+            "id": datasets.Value("string"),
+            "normalized_text": datasets.Value("string"),
+            "speaker_id": datasets.Value("int32"),
+            "split": datasets.Value("string"),
+            "sentiment": datasets.Value("string"),
+            "start_second": datasets.Value("float32"),
+            "end_second": datasets.Value("float32"),
+            "audio_path": datasets.Value("string"),
+        }
+    )              
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "slue-voxceleb", "slue-voxceleb_fine-tune.tsv"),
+                    "split": "fine-tune",
+                    "audio_dir": os.path.join(data_dir, "slue-voxceleb", "fine-tune_raw"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "slue-voxceleb", "slue-voxceleb_dev.tsv"),
+                    "split": "dev",
+                    "audio_dir": os.path.join(data_dir, "slue-voxceleb", "dev_raw"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "slue-voxceleb", "slue-voxceleb_test_blind.tsv"),
+                    "split": "test-blind",
+                    "audio_dir": os.path.join(data_dir, "slue-voxceleb", "test_raw"),
+                },
+            ),
+        ]
+
+            
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split, audio_dir):
+        # read tsv file by rows
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            with open("debug.txt", "w") as f1:
+                f1.write(str(split) + '\n')
+                for row in reader:
+                    f1.write(str(row))
+                    speaker_id = row["speaker_id"]
+                    if not speaker_id.isdigit():
+                        speaker_id = -1
+                    else:
+                        speaker_id = int(speaker_id)
+                    
+                    audio_file = f"{row['id']}.flac"
+                    if split == "test-blind":
+                        yield row["id"], {
+                            "id": row["id"],
+                            "normalized_text": None,
+                            "speaker_id": speaker_id,
+                            "split": split,
+                            "sentiment": None,
+                            "start_second": float(row["start_second"]),
+                            "end_second": float(row["end_second"]),
+                            "audio_path": os.path.join(audio_dir, audio_file),
+                        }
+                    elif split == "fine-tune" or split == "dev":
+                        yield row["id"], {
+                            "id": row["id"],
+                            "normalized_text": row["normalized_text"],
+                            "speaker_id": speaker_id,
+                            "split": split,
+                            "sentiment": row["sentiment"],
+                            "start_second": float(row["start_second"]),
+                            "end_second": float(row["end_second"]),
+                            "audio_path": os.path.join(audio_dir, audio_file), 
+                        }
+

voxpopuli/voxpopuli.py

@@ -0,0 +1,194 @@
+# 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
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+import sys
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "voxpopuli": "https://public-dataset-model-store.awsdev.asapp.com/users/sshon/public/slue/slue-voxpopuli_v0.2_blind.zip"
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class SLUEVoxpopuli(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="voxpopuli", version=VERSION, description="This part of my dataset covers a first domain"),
+    ]
+
+    #DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "voxpopuli": # This is the name of the configuration selected in BUILDER_CONFIGS above
+            # get the current split
+
+            features = datasets.Features(
+        {
+            "id": datasets.Value("string"),
+            "raw_text": datasets.Value("string"),
+            "normalized_text": datasets.Value("string"),
+            "speaker_id": datasets.Value("int32"),
+            "split": datasets.Value("string"),
+            "raw_ner": datasets.Value("string"),
+            "normalized_ner": datasets.Value("string"),
+            "audio_path": datasets.Value("string"),
+        }
+    )              
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "slue-voxpopuli", "slue-voxpopuli_fine-tune.tsv"),
+                    "split": "fine-tune",
+                    "audio_dir": os.path.join(data_dir, "slue-voxpopuli", "fine-tune"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "slue-voxpopuli", "slue-voxpopuli_dev.tsv"),
+                    "split": "dev",
+                    "audio_dir": os.path.join(data_dir, "slue-voxpopuli", "dev"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "slue-voxpopuli", "slue-voxpopuli_test_blind.tsv"),
+                    "split": "test-blind",
+                    "audio_dir": os.path.join(data_dir, "slue-voxpopuli", "test"),
+                },
+            ),
+        ]
+
+            
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split, audio_dir):
+        # read tsv file by rows
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            with open("debug.txt", "w") as f1:
+                f1.write(str(split) + '\n')
+                for row in reader:
+                    f1.write(str(row))
+                    speaker_id = row["speaker_id"]
+                    if not speaker_id.isdigit():
+                        speaker_id = -1
+                    else:
+                        speaker_id = int(speaker_id)
+                    
+                    audio_file = f"{row['id']}.ogg"
+                    if split == "test-blind":
+                        yield row["id"], {
+                            "id": row["id"],
+                            "raw_text": row["raw_text"],
+                            "normalized_text": row["normalized_text"],
+                            "speaker_id": speaker_id,
+                            "split": split,
+                            "audio_path": os.path.join(audio_dir, audio_file),
+                            "raw_ner": None,
+                            "normalized_ner": None
+                        }
+                    elif split == "fine-tune" or split == "dev":
+                        yield row["id"], {
+                            "id": row["id"],
+                            "raw_text": row["raw_text"],
+                            "normalized_text": row["normalized_text"],
+                            "speaker_id": speaker_id,
+                            "split": split,
+                            "raw_ner": row["raw_ner"],
+                            "normalized_ner": row["normalized_ner"],
+                            "audio_path": os.path.join(audio_dir, audio_file),
+                        }
+