custom_common_voice dataset.

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+dataset_infos.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+pretty_name: Common Voice
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- ab
+- ar
+- as
+- br
+- ca
+- cnh
+- cs
+- cv
+- cy
+- de
+- dv
+- el
+- en
+- eo
+- es
+- et
+- eu
+- fa
+- fi
+- fr
+- fy-NL
+- ga-IE
+- hi
+- hsb
+- hu
+- ia
+- id
+- it
+- ja
+- ka
+- kab
+- ky
+- lg
+- lt
+- lv
+- mn
+- mt
+- nl
+- or
+- pa-IN
+- pl
+- pt
+- rm-sursilv
+- rm-vallader
+- ro
+- ru
+- rw
+- sah
+- sl
+- sv-SE
+- ta
+- th
+- tr
+- tt
+- uk
+- vi
+- vot
+- zh-CN
+- zh-HK
+- zh-TW
+licenses:
+- cc0-1.0
+multilinguality:
+- multilingual
+size_categories:
+  ab:
+  - n<1K
+  ar:
+  - 10K<n<100K
+  as:
+  - n<1K
+  br:
+  - 10K<n<100K
+  ca:
+  - 100K<n<1M
+  cnh:
+  - 1K<n<10K
+  cs:
+  - 10K<n<100K
+  cv:
+  - 10K<n<100K
+  cy:
+  - 10K<n<100K
+  de:
+  - 100K<n<1M
+  dv:
+  - 1K<n<10K
+  el:
+  - 10K<n<100K
+  en:
+  - 100K<n<1M
+  eo:
+  - 10K<n<100K
+  es:
+  - 100K<n<1M
+  et:
+  - 10K<n<100K
+  eu:
+  - 10K<n<100K
+  fa:
+  - 10K<n<100K
+  fi:
+  - 1K<n<10K
+  fr:
+  - 100K<n<1M
+  fy-NL:
+  - 10K<n<100K
+  ga-IE:
+  - 1K<n<10K
+  hi:
+  - n<1K
+  hsb:
+  - 1K<n<10K
+  hu:
+  - 1K<n<10K
+  ia:
+  - 1K<n<10K
+  id:
+  - 10K<n<100K
+  it:
+  - 100K<n<1M
+  ja:
+  - 1K<n<10K
+  ka:
+  - 1K<n<10K
+  kab:
+  - 100K<n<1M
+  ky:
+  - 10K<n<100K
+  lg:
+  - 1K<n<10K
+  lt:
+  - 1K<n<10K
+  lv:
+  - 1K<n<10K
+  mn:
+  - 1K<n<10K
+  mt:
+  - 10K<n<100K
+  nl:
+  - 10K<n<100K
+  or:
+  - 1K<n<10K
+  pa-IN:
+  - 1K<n<10K
+  pl:
+  - 10K<n<100K
+  pt:
+  - 10K<n<100K
+  rm-sursilv:
+  - 1K<n<10K
+  rm-vallader:
+  - 1K<n<10K
+  ro:
+  - 1K<n<10K
+  ru:
+  - 10K<n<100K
+  rw:
+  - 100K<n<1M
+  sah:
+  - 1K<n<10K
+  sl:
+  - 1K<n<10K
+  sv-SE:
+  - 1K<n<10K
+  ta:
+  - 10K<n<100K
+  th:
+  - 10K<n<100K
+  tr:
+  - 1K<n<10K
+  tt:
+  - 10K<n<100K
+  uk:
+  - 10K<n<100K
+  vi:
+  - 1K<n<10K
+  vot:
+  - n<1K
+  zh-CN:
+  - 10K<n<100K
+  zh-HK:
+  - 10K<n<100K
+  zh-TW:
+  - 10K<n<100K
+source_datasets:
+- extended|common_voice
+task_categories:
+- speech-processing
+task_ids:
+- automatic-speech-recognition
+paperswithcode_id: common-voice
+---
+
+# Dataset Card for common_voice
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://commonvoice.mozilla.org/en/datasets
+- **Repository:** https://github.com/common-voice/common-voice
+- **Paper:** https://commonvoice.mozilla.org/en/datasets
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9,283 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help train the accuracy of speech recognition engines.
+
+The dataset currently consists of 7,335 validated hours in 60 languages, but were always adding more voices and languages. Take a look at our Languages page to request a language or start contributing.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point comprises the path to the audio file, called path and its sentence. Additional fields include accent, age, client_id, up_votes down_votes, gender, locale and segment.
+
+`
+{'accent': 'netherlands', 'age': 'fourties', 'client_id': 'bbbcb732e0f422150c30ff3654bbab572e2a617da107bca22ff8b89ab2e4f124d03b6a92c48322862f60bd0179ae07baf0f9b4f9c4e11d581e0cec70f703ba54', 'down_votes': 0, 'gender': 'male', 'locale': 'nl', 'path': 'nl/clips/common_voice_nl_23522441.mp3', 'segment': "''", 'sentence': 'Ik vind dat een dubieuze procedure.', 'up_votes': 2, 'audio': {'path': `nl/clips/common_voice_nl_23522441.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32), 'sampling_rate': 48000}
+`
+
+### Data Fields
+
+client_id: An id for which client (voice) made the recording
+
+path: The path to the audio file
+
+audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+
+sentence: The sentence the user was prompted to speak
+
+up_votes: How many upvotes the audio file has received from reviewers
+
+down_votes: How many downvotes the audio file has received from reviewers
+
+age: The age of the speaker.
+
+gender: The gender of the speaker
+
+accent: Accent of the speaker
+
+locale: The locale of the speaker
+
+segment: Usually empty field
+
+### Data Splits
+
+The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
+
+The validated data is data that has been validated with reviewers and recieved upvotes that the data is of high quality.
+
+The invalidated data is data has been invalidated by reviewers
+and recieved downvotes that the data is of low quality.
+
+The reported data is data that has been reported, for different reasons.
+
+The other data is data that has not yet been reviewed.
+
+The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
+
+### Discussion of Biases
+
+[More Information Needed] 
+
+### Other Known Limitations
+
+[More Information Needed] 
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed] 
+
+### Licensing Information
+
+Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
+
+### Citation Information
+
+```
+@inproceedings{commonvoice:2020,
+  author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
+  title = {Common Voice: A Massively-Multilingual Speech Corpus},
+  booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
+  pages = {4211--4215},
+  year = 2020
+}
+```
+
+### Contributions
+
+Thanks to [@BirgerMoell](https://github.com/BirgerMoell) for adding this dataset.

common_voice.py

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+# coding=utf-8
+# Copyright 2021 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.
+""" Common Voice Dataset"""
+
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_DATA_URL = "https://drive.google.com/uc?export=download&id=13PIifohVjBB5ffSCm4eXyF8O9I30HOj2"
+
+_DESCRIPTION = """\
+Common Voice is Mozilla's initiative to help teach machines how real people speak.
+The dataset currently consists of 7,335 validated hours of speech in 60 languages, but we’re always adding more voices and languages.
+"""
+
+_LANGUAGES = {
+    "vi": {
+        "Language": "Vietnamese",
+        "Date": "2020-12-11",
+        "Size": "50 MB",
+        "Version": "vi_1h_2020-12-11",
+        "Validated_Hr_Total": 0.74,
+        "Overall_Hr_Total": 1,
+        "Number_Of_Voice": 62,
+    },
+}
+
+
+class CustomCommonVoiceConfig(datasets.BuilderConfig):
+    """BuilderConfig for CommonVoice."""
+
+    def __init__(self, name, sub_version, **kwargs):
+        """
+        Args:
+          data_dir: `string`, the path to the folder containing the files in the
+            downloaded .tar
+          citation: `string`, citation for the data set
+          url: `string`, url for information about the data set
+          **kwargs: keyword arguments forwarded to super.
+        """
+        self.sub_version = sub_version
+        self.language = kwargs.pop("language", None)
+        self.date_of_snapshot = kwargs.pop("date", None)
+        self.size = kwargs.pop("size", None)
+        self.validated_hr_total = kwargs.pop("val_hrs", None)
+        self.total_hr_total = kwargs.pop("total_hrs", None)
+        self.num_of_voice = kwargs.pop("num_of_voice", None)
+        description = f"Common Voice speech to text dataset in {self.language} version " \
+                      f"{self.sub_version} of {self.date_of_snapshot}. " \
+                      f"The dataset comprises {self.validated_hr_total} of validated transcribed speech data from " \
+                      f"{self.num_of_voice} speakers. The dataset has a size of {self.size} "
+        super(CustomCommonVoiceConfig, self).__init__(
+            name=name, version=datasets.Version("1.0", ""), description=description, **kwargs
+        )
+
+
+class CustomCommonVoice(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+    BUILDER_CONFIGS = [
+        CustomCommonVoiceConfig(
+            name=lang_id,
+            language=_LANGUAGES[lang_id]["Language"],
+            sub_version=_LANGUAGES[lang_id]["Version"],
+            date=_LANGUAGES[lang_id]["Date"],
+            size=_LANGUAGES[lang_id]["Size"],
+            val_hrs=_LANGUAGES[lang_id]["Validated_Hr_Total"],
+            total_hrs=_LANGUAGES[lang_id]["Overall_Hr_Total"],
+            num_of_voice=_LANGUAGES[lang_id]["Number_Of_Voice"],
+        )
+        for lang_id in _LANGUAGES.keys()
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "client_id": datasets.Value("string"),
+                "path": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=48_000),
+                "sentence": datasets.Value("string"),
+                "up_votes": datasets.Value("int64"),
+                "down_votes": datasets.Value("int64"),
+                "age": datasets.Value("string"),
+                "gender": datasets.Value("string"),
+                "accent": datasets.Value("string"),
+                "locale": datasets.Value("string"),
+                "segment": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            task_templates=[
+                AutomaticSpeechRecognition(audio_file_path_column="path", transcription_column="sentence")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        archive = dl_manager.download(_DATA_URL)
+        path_to_data = "/".join("data_1")
+        path_to_clips = "/".join([path_to_data, "audio"])
+        path_to_script = "/".join([path_to_data, "script"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dl_manager.iter_archive(archive),
+                    "filepath": "/".join([path_to_data, "train_custom_common_voice.csv"]),
+                    "path_to_clips": path_to_clips,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dl_manager.iter_archive(archive),
+                    "filepath": "/".join([path_to_data, "test_custom_common_voice.csv"]),
+                    "path_to_clips": path_to_clips,
+                },
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     gen_kwargs={
+            #         "files": dl_manager.iter_archive(archive),
+            #         "filepath": "/".join([path_to_data, "dev.tsv"]),
+            #         "path_to_clips": path_to_clips,
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name="other",
+            #     gen_kwargs={
+            #         "files": dl_manager.iter_archive(archive),
+            #         "filepath": "/".join([path_to_data, "other.tsv"]),
+            #         "path_to_clips": path_to_clips,
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name="invalidated",
+            #     gen_kwargs={
+            #         "files": dl_manager.iter_archive(archive),
+            #         "filepath": "/".join([path_to_data, "invalidated.tsv"]),
+            #         "path_to_clips": path_to_clips,
+            #     },
+            # ),
+        ]
+
+    def _generate_examples(self, files, filepath, path_to_clips):
+        """Yields examples."""
+        data_fields = list(self._info().features.keys())
+
+        # audio is not a header of the csv files
+        data_fields.remove("audio")
+        path_idx = data_fields.index("path")
+
+        all_field_values = {}
+        metadata_found = False
+        for path, f in files:
+            if path == filepath:
+                metadata_found = True
+                lines = f.readlines()
+                headline = lines[0].decode("utf-8")
+
+                column_names = headline.strip().split("\t")
+                assert (
+                    column_names == data_fields
+                ), f"The file should have {data_fields} as column names, but has {column_names}"
+                for line in lines[1:]:
+                    field_values = line.decode("utf-8").strip().split("\t")
+                    # set full path for mp3 audio file
+                    audio_path = "/".join([path_to_clips, field_values[path_idx]])
+                    all_field_values[audio_path] = field_values
+            elif path.startswith(path_to_clips):
+                assert metadata_found, "Found audio clips before the metadata TSV file."
+                if not all_field_values:
+                    break
+                if path in all_field_values:
+                    field_values = all_field_values[path]
+
+                    # if data is incomplete, fill with empty values
+                    if len(field_values) < len(data_fields):
+                        field_values += (len(data_fields) - len(field_values)) * ["''"]
+
+                    result = {key: value for key, value in zip(data_fields, field_values)}
+
+                    # set audio feature
+                    result["audio"] = {"path": path, "bytes": f.read()}
+
+                    yield path, result

dataset_infos.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3b538721de160b475f8b25377e577d8e8c3dd490c59fdb6839275afc8d5e4c61
+size 2787

main.py

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+# This is a sample Python script.
+
+# Press Shift+F10 to execute it or replace it with your code.
+# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
+
+
+def print_hi(name):
+    # Use a breakpoint in the code line below to debug your script.
+    print(f'Hi, {name}')  # Press Ctrl+F8 to toggle the breakpoint.
+
+
+# Press the green button in the gutter to run the script.
+if __name__ == '__main__':
+    print_hi('PyCharm')
+
+# See PyCharm help at https://www.jetbrains.com/help/pycharm/