Add scripts/fetch_data.py, scripts/prep_data.py

.gitignore

@@ -159,3 +159,6 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 .idea/
+
+/data
+.DS_Store

kendex.py

@@ -1,137 +0,0 @@
-import csv
-import json
-import os
-
-import datasets
-
-_CITATION = """\
-@InProceedings{
-    huggingface:dataset,
-    title = {Kendex: A TTS Dataset for the Glory of GOD!},
-    author={michaelnetbiz},
-    year={2023}
-}
-"""
-
-_DESCRIPTION = """\
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
-"""
-
-_HOMEPAGE = "https://michaelnet.biz/kendex"
-
-_LICENSE = "MIT"
-
-# 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 = {
-    "kendex": "https://huggingface.co/kendex-kendex.zip",
-}
-
-
-class Kendex(datasets.GeneratorBasedBuilder):
-    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="kendex", version=VERSION,
-                               description="This is kendex"),
-    ]
-
-    # It's not mandatory to have a default configuration. Just use one if it make sense.
-    DEFAULT_CONFIG_NAME = "kendex"
-
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        # This is the name of the configuration selected in BUILDER_CONFIGS above
-
-        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
-            # Here we define them above because they are different between the two configurations
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # 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, "train.jsonl"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.jsonl"),
-                    "split": "dev",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test.jsonl"),
-                    "split": "test"
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "first_domain":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option1": data["option1"],
-                        "answer": "" if split == "test" else data["answer"],
-                    }
-                else:
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option2": data["option2"],
-                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
-                    }

requirements.txt

@@ -1,49 +1,90 @@
 aiohttp==3.8.5
 aiosignal==1.3.1
+anyio==4.0.0
 appnope==0.1.3
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.2.3
 asttokens==2.4.0
+async-lru==2.0.4
 async-timeout==4.0.3
 attrs==23.1.0
 audioread==3.0.0
+Babel==2.12.1
 backcall==0.2.0
+beautifulsoup4==4.12.2
+bleach==6.0.0
+boto3==1.28.61
+botocore==1.31.61
 certifi==2023.7.22
 cffi==1.15.1
 charset-normalizer==3.2.0
 comm==0.1.4
 datasets==2.14.5
-debugpy==1.7.0
+debugpy==1.8.0
 decorator==5.1.1
+defusedxml==0.7.1
 dill==0.3.7
 executing==1.2.0
-filelock==3.12.3
+fastjsonschema==2.18.0
+filelock==3.12.4
+fqdn==1.5.1
 frozenlist==1.4.0
 fsspec==2023.6.0
 huggingface-hub==0.16.4
 idna==3.4
 ipykernel==6.25.2
 ipython==8.15.0
+ipython-genutils==0.2.0
+ipywidgets==8.1.1
+isoduration==20.11.0
 jedi==0.19.0
+Jinja2==3.1.2
+jmespath==1.0.1
 joblib==1.3.2
+json5==0.9.14
+jsonpointer==2.4
+jsonschema==4.19.0
+jsonschema-specifications==2023.7.1
+jupyter==1.0.0
 jupyter-console==6.6.3
+jupyter-events==0.7.0
+jupyter-lsp==2.2.0
 jupyter_client==8.3.1
 jupyter_core==5.3.1
+jupyter_server==2.7.3
+jupyter_server_terminals==0.4.4
+jupyterlab==4.0.6
+jupyterlab-pygments==0.2.2
+jupyterlab-widgets==3.0.9
+jupyterlab_server==2.25.0
 lazy_loader==0.3
 librosa==0.10.1
 llvmlite==0.40.1
+MarkupSafe==2.1.3
 matplotlib-inline==0.1.6
+mistune==3.0.1
 msgpack==1.0.5
 multidict==6.0.4
 multiprocess==0.70.15
-nest-asyncio==1.5.7
+nbclient==0.8.0
+nbconvert==7.8.0
+nbformat==5.9.2
+nest-asyncio==1.5.8
+notebook==7.0.3
+notebook_shim==0.2.3
 numba==0.57.1
-numpy==1.24.4
+numpy==1.26.0
+overrides==7.4.0
 packaging==23.1
 pandas==2.1.0
+pandocfilters==1.5.0
 parso==0.8.3
 pexpect==4.8.0
 pickleshare==0.7.5
 platformdirs==3.10.0
 pooch==1.7.0
+prometheus-client==0.17.1
 prompt-toolkit==3.0.39
 psutil==5.9.5
 ptyprocess==0.7.0
@@ -52,23 +93,44 @@ pyarrow==13.0.0
 pycparser==2.21
 Pygments==2.16.1
 python-dateutil==2.8.2
+python-json-logger==2.0.7
 pytz==2023.3.post1
 PyYAML==6.0.1
 pyzmq==25.1.1
+qtconsole==5.4.4
+QtPy==2.4.0
+referencing==0.30.2
+regex==2023.10.3
 requests==2.31.0
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rpds-py==0.10.3
+s3transfer==0.7.0
+safetensors==0.3.3
 scikit-learn==1.3.0
 scipy==1.11.2
+Send2Trash==1.8.2
 six==1.16.0
+sniffio==1.3.0
 soundfile==0.12.1
+soupsieve==2.5
 soxr==0.3.6
 stack-data==0.6.2
+terminado==0.17.1
 threadpoolctl==3.2.0
+tinycss2==1.2.1
+tokenizers==0.14.0
 tornado==6.3.3
 tqdm==4.66.1
-traitlets==5.9.0
+traitlets==5.10.0
+transformers==4.34.0
 typing_extensions==4.7.1
 tzdata==2023.3
-urllib3==2.0.4
+uri-template==1.3.0
 wcwidth==0.2.6
+webcolors==1.13
+webencodings==0.5.1
+websocket-client==1.6.3
+widgetsnbextension==4.0.9
 xxhash==3.3.0
 yarl==1.9.2

scripts/fetch_data.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+
+import os
+import boto3
+import tarfile
+from pathlib import Path
+
+DATASET = 'kendex.tar.gz'
+
+repo_root = Path(__file__).resolve().parent.parent
+
+local_path = os.path.join(repo_root, 'data', DATASET)
+
+if not Path(local_path).exists():
+    s3 = boto3.resource('s3')
+    kendex = s3.Bucket('kendex')
+    kendex.download_file(DATASET, local_path)
+    print('downloaded')
+
+expanded_path = os.path.join(repo_root, 'data', 'kendex')
+
+if not Path(expanded_path).exists():
+    file = tarfile.open(local_path)
+    file.extractall(expanded_path)
+    file.close()
+    print('untar\'d, gunzip\'d')

scripts/prep_data.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+import os
+from pathlib import Path
+
+import huggingface_hub
+from datasets import Audio, Dataset
+import pandas as pd
+
+repo_root = Path(__file__).resolve().parent.parent
+
+metadata = os.path.join(repo_root, 'data', 'kendex', 'metadata.csv')
+wavs = os.path.join(repo_root, 'data', 'kendex', 'wavs')
+df = pd.read_csv(metadata, delimiter='|', header=None)
+
+dataset = Dataset.from_pandas(pd.DataFrame({
+    'audio': pd.Series([
+        os.path.join(wavs, f"{f}.wav") for f in df[0]
+    ]),
+    'text': df[1],
+}))
+
+dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
+
+huggingface_hub.login()
+
+dataset.push_to_hub("michaelnetbiz/kendex")