First version of the daps dataset.

daps.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.
+
+"""DAPS Dataset"""
+
+import glob
+import os
+
+import datasets
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{mysore2014can,
+  title={Can we automatically transform speech recorded on common consumer devices in real-world environments into professional production quality speech?—a dataset, insights, and challenges},
+  author={Mysore, Gautham J},
+  journal={IEEE Signal Processing Letters},
+  volume={22},
+  number={8},
+  pages={1006--1010},
+  year={2014},
+  publisher={IEEE}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+The DAPS (Device and Produced Speech) dataset is a collection of aligned versions of professionally produced studio speech recordings and recordings of the same speech on common consumer devices (tablet and smartphone) in real-world environments. It has 15 versions of audio (3 professional versions and 12 consumer device/real-world environment combinations). Each version consists of about 4 1/2 hours of data (about 14 minutes from each of 20 speakers).
+"""
+
+_HOMEPAGE = "https://ccrma.stanford.edu/~gautham/Site/daps.html"
+
+_LICENSE = "Creative Commons Attribution Non Commercial 4.0 International"
+
+# 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 = "https://zenodo.org/record/4660670/files/daps.tar.gz"
+
+
+class DapsDataset(datasets.GeneratorBasedBuilder):
+    """The DAPS (Device and Produced Speech) dataset is a collection of aligned versions of professionally produced studio speech recordings and recordings of the same speech on common consumer devices (tablet and smartphone) in real-world environments."""
+
+    VERSION = datasets.Version("2.12.0")
+
+    DEFAULT_CONFIG_NAME = "aligned_examples"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "recording_environment": datasets.Value("string"),
+                "speaker_id": datasets.Value("string"),
+                "script_id": datasets.Value("string"),
+                "clean_path": datasets.Value("string"),
+                "produced_path": datasets.Value("string"),
+                "device_path": datasets.Value("string"),
+                "clean_audio": datasets.Audio(sampling_rate=44_100),
+                "produced_audio": datasets.Audio(sampling_rate=44_100),
+                "device_audio": datasets.Audio(sampling_rate=44_100),
+            }
+        )
+        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):
+        """Returns SplitGenerators."""
+        # 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
+        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": data_dir,
+                },
+            )
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath):
+        gt = ["clean", "produced"]
+        environments = [
+            "ipad_balcony1",
+            "ipad_livingroom1",
+            "ipadflat_office1",
+            "ipad_bedroom1",
+            "ipad_office1",
+            "iphone_balcony1",
+            "ipad_confroom1",
+            "ipad_office2",
+            "iphone_bedroom1",
+            "ipad_confroom2",
+            "ipadflat_confroom1",
+            "iphone_livingroom1",
+        ]
+        # example path: daps/iphone_bedroom1/m8_script5_iphone_bedroom1.wav
+        for env in environments:
+            for device_path in glob.glob(os.path.join(filepath, env) + "/*.wav"):
+                speaker_id = os.path.basename(device_path).split("_")[-4]
+                script_id = os.path.basename(device_path).split("_")[-3]
+                clean_path = device_path.replace(env, "clean")
+                produced_path = device_path.replace(env, "produced")
+                with open(clean_path, "rb") as f:
+                    clean_audio = {"path": clean_path, "bytes": f.read()}
+                with open(produced_path, "rb") as f:
+                    produced_audio = {"path": produced_path, "bytes": f.read()}
+                with open(device_path, "rb") as f:
+                    device_audio = {"path": device_path, "bytes": f.read()}
+                yield f"{speaker_id}_{script_id}_{env}", {
+                    "recording_environment": env,
+                    "speaker_id": speaker_id,
+                    "script_id": script_id,
+                    "clean_path": clean_path,
+                    "produced_path": produced_path,
+                    "device_path": device_path,
+                    "clean_audio": clean_audio,
+                    "produced_audio": produced_audio,
+                    "device_audio": device_audio,
+                }