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librispeech_train_clean_only.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."""
-
-
-import os
-
-import datasets
-from datasets.tasks import AutomaticSpeechRecognition
-
-
-_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.87
-"""
-
-_URL = "http://www.openslr.org/12"
-_DL_URL = "http://www.openslr.org/resources/12/"
-
-
-_DL_URLS = {
-    "clean": {
-        "train.100": _DL_URL + "train-clean-100.tar.gz",
-        "train.360": _DL_URL + "train-clean-360.tar.gz",
-    },
-    "all": {
-        "train.100": _DL_URL + "train-clean-100.tar.gz",
-        "train.360": _DL_URL + "train-clean-360.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."""
-
-    DEFAULT_WRITER_BATCH_SIZE = 256
-    DEFAULT_CONFIG_NAME = "all"
-    BUILDER_CONFIGS = [LibrispeechASRConfig(name="clean", description="'Clean' speech.")]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "file": datasets.Value("string"),
-                    "audio": datasets.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=("file", "text"),
-            homepage=_URL,
-            citation=_CITATION,
-            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(_DL_URLS[self.config.name])
-        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
-        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
-
-        if self.config.name == "clean" or self.config.name == "all":
-            train_splits = [
-                datasets.SplitGenerator(
-                    name="train.100",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("train.100"),
-                        "files": dl_manager.iter_archive(archive_path["train.100"]),
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name="train.360",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("train.360"),
-                        "files": dl_manager.iter_archive(archive_path["train.360"]),
-                    },
-                ),
-            ]
-
-        return train_splits
-
-    def _generate_examples(self, files, local_extracted_archive):
-        """Generate examples from a LibriSpeech archive_path."""
-        key = 0
-        audio_data = {}
-        transcripts = []
-        for path, f in files:
-            if path.endswith(".flac"):
-                id_ = path.split("/")[-1][: -len(".flac")]
-                audio_data[id_] = f.read()
-            elif path.endswith(".trans.txt"):
-                for line in f:
-                    if line:
-                        line = line.decode("utf-8").strip()
-                        id_, transcript = line.split(" ", 1)
-                        audio_file = f"{id_}.flac"
-                        speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
-                        audio_file = (
-                            os.path.join(local_extracted_archive, audio_file)
-                            if local_extracted_archive
-                            else audio_file
-                        )
-                        transcripts.append(
-                            {
-                                "id": id_,
-                                "speaker_id": speaker_id,
-                                "chapter_id": chapter_id,
-                                "file": audio_file,
-                                "text": transcript,
-                            }
-                        )
-            if audio_data and len(audio_data) == len(transcripts):
-                for transcript in transcripts:
-                    audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
-                    yield key, {"audio": audio, **transcript}
-                    key += 1
-                audio_data = {}
-                transcripts = []