Upload librispeech_asr.py

librispeech_asr.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": {
+        "dev": _DL_URL + "dev-clean.tar.gz",
+    },
+    "other": {
+        "test": _DL_URL + "test-other.tar.gz",
+        "dev": _DL_URL + "dev-other.tar.gz",
+        "train.500": _DL_URL + "train-other-500.tar.gz",
+    },
+    "all": {
+        "dev.clean": _DL_URL + "dev-clean.tar.gz",
+        "dev.other": _DL_URL + "dev-other.tar.gz",
+        "test.clean": _DL_URL + "test-clean.tar.gz",
+        "test.other": _DL_URL + "test-other.tar.gz",
+        "train.clean.100": _DL_URL + "train-clean-100.tar.gz",
+        "train.clean.360": _DL_URL + "train-clean-360.tar.gz",
+        "train.other.500": _DL_URL + "train-other-500.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."),
+        LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."),
+        LibrispeechASRConfig(name="all", description="Combined clean and other dataset."),
+    ]
+
+    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":
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("dev"),
+                        "files": dl_manager.iter_archive(archive_path["dev"]),
+                    },
+                )
+            ]
+            
+        elif self.config.name == "other":
+            train_splits = [
+                datasets.SplitGenerator(
+                    name="train.500",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.500"),
+                        "files": dl_manager.iter_archive(archive_path["train.500"]),
+                    },
+                )
+            ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("dev"),
+                        "files": dl_manager.iter_archive(archive_path["dev"]),
+                    },
+                )
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test"),
+                        "files": dl_manager.iter_archive(archive_path["test"]),
+                    },
+                )
+            ]
+        elif self.config.name == "all":
+            train_splits = [
+                datasets.SplitGenerator(
+                    name="train.clean.100",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.clean.100"),
+                        "files": dl_manager.iter_archive(archive_path["train.clean.100"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train.clean.360",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.clean.360"),
+                        "files": dl_manager.iter_archive(archive_path["train.clean.360"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train.other.500",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.other.500"),
+                        "files": dl_manager.iter_archive(archive_path["train.other.500"]),
+                    },
+                ),
+            ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name="validation.clean",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("validation.clean"),
+                        "files": dl_manager.iter_archive(archive_path["dev.clean"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="validation.other",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("validation.other"),
+                        "files": dl_manager.iter_archive(archive_path["dev.other"]),
+                    },
+                ),
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name="test.clean",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test.clean"),
+                        "files": dl_manager.iter_archive(archive_path["test.clean"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="test.other",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test.other"),
+                        "files": dl_manager.iter_archive(archive_path["test.other"]),
+                    },
+                ),
+            ]
+
+        return train_splits + dev_splits + test_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 = []