Upload librispeech_asr.py

librispeech_asr.py

@@ -0,0 +1,132 @@
+
+"""Librispeech automatic speech recognition dataset."""
+
+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.87
+"""
+
+_URL = "http://www.openslr.org/12"
+_DL_URL = "http://www.openslr.org/resources/12/"
+
+_DL_URLS = {"test": _DL_URL + "test-clean.tar.gz",
+            "train.100": _DL_URL + "train-clean-100.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_CONFIG = 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,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(_DL_URLS)
+        # (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 {}
+
+        train_split = [
+            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"]),
+                },
+            ),
+        ]
+        test_split = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "local_extracted_archive": local_extracted_archive.get("test"),
+                    "files": dl_manager.iter_archive(archive_path["test"]),
+                },
+            )
+        ]
+        return train_split + test_split
+
+    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 = []