convert to parquet

README.md

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+---
+dataset_info:
+  config_name: clean
+  features:
+  - name: file
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: text
+    dtype: string
+  - name: speaker_id
+    dtype: int64
+  - name: chapter_id
+    dtype: int64
+  - name: id
+    dtype: string
+  splits:
+  - name: validation
+    num_bytes: 9677021.0
+    num_examples: 73
+  download_size: 9192059
+  dataset_size: 9677021.0
+configs:
+- config_name: clean
+  data_files:
+  - split: validation
+    path: clean/validation-*
+---

clean/validation-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e69a06fa5edc90921e5e7e39a7084881f8b3ed9c805c574f4f39c6fde27c603
+size 9192059

librispeech_asr_dummy.py

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-# coding=utf-8
-# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""Librispeech automatic speech recognition dataset."""
-
-from __future__ import absolute_import, division, print_function
-
-import glob
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{panayotov2015librispeech,
-  title={Librispeech: an ASR corpus based on public domain audio books},
-  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
-  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
-  pages={5206--5210},
-  year={2015},
-  organization={IEEE}
-}
-"""
-
-_DESCRIPTION = """\
-LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
-prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
-audiobooks from the LibriVox project, and has been carefully segmented and aligned.
-
-Note that in order to limit the required storage for preparing this dataset, the audio
-is stored in the .flac format and is not converted to a float32 array. To convert, the audio
-file to a float32 array, please make use of the `.map()` function as follows:
-
-
-```python
-import soundfile as sf
-
-def map_to_array(batch):
-    speech_array, _ = sf.read(batch["file"])
-    batch["speech"] = speech_array
-    return batch
-
-dataset = dataset.map(map_to_array, remove_columns=["file"])
-```
-"""
-
-_URL = "http://www.openslr.org/12"
-_DL_URL = "https://s3.amazonaws.com/datasets.huggingface.co/librispeech_asr/2.1.0/"
-_DL_URL = "https://s3.amazonaws.com/datasets.huggingface.co/librispeech_asr/2.1.0/"
-
-_DL_URLS = {
-    "clean": {
-        "dev": _DL_URL + "dev_clean.tar.gz",
-    }
-}
-
-
-class LibrispeechASRConfig(datasets.BuilderConfig):
-    """BuilderConfig for LibriSpeechASR."""
-
-    def __init__(self, **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.
-        """
-        super(LibrispeechASRConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
-
-
-class LibrispeechASR(datasets.GeneratorBasedBuilder):
-    """Librispeech dataset."""
-
-    BUILDER_CONFIGS = [
-        LibrispeechASRConfig(name="clean", description="'Clean' speech."),
-        LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "file": datasets.Value("string"),
-                    "audio": datasets.features.Audio(sampling_rate=16_000),
-                    "text": datasets.Value("string"),
-                    "speaker_id": datasets.Value("int64"),
-                    "chapter_id": datasets.Value("int64"),
-                    "id": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=("speech", "text"),
-            homepage=_URL,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download_and_extract(_DL_URLS[self.config.name])
-        return [
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"archive_path": archive_path["dev"], "split_name": f"dev_{self.config.name}"}),
-        ]
-
-    def _generate_examples(self, archive_path, split_name):
-        """Generate examples from a Librispeech archive_path."""
-        transcripts_glob = os.path.join(archive_path, split_name, "*/*/*.txt")
-        for transcript_file in sorted(glob.glob(transcripts_glob)):
-            path = os.path.dirname(transcript_file)
-            with open(os.path.join(path, transcript_file)) as f:
-                for line in f:
-                    line = line.strip()
-                    key, transcript = line.split(" ", 1)
-                    audio_file = f"{key}.flac"
-                    speaker_id, chapter_id = [int(el) for el in key.split("-")[:2]]
-                    example = {
-                        "id": key,
-                        "speaker_id": speaker_id,
-                        "chapter_id": chapter_id,
-                        "file": os.path.join(path, audio_file),
-                        "audio": os.path.join(path, audio_file),
-                        "text": transcript,
-                    }
-                    yield key, example