script

libriheavy.py

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+import json
+import gzip
+
+import datasets
+import numpy as np
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = """\
+Libriheavy is a labeled version of Librilight.
+This (unofficial) huggingface dataset contains the medium (4500 hours) split of the Libriheavy dataset with alignments and mel spectrograms.
+"""
+
+_URL = """\
+https://github.com/k2-fsa/libriheavy
+"""
+
+_CITATION = """\
+@article{kang2023libriheavy,
+  title={Libriheavy: a 50,000 hours asr corpus with punctuation casing and context},
+  author={Kang, Wei and Yang, Xiaoyu and Yao, Zengwei and Kuang, Fangjun and Yang, Yifan and Guo, Liyong and Lin, Long and Povey, Daniel},
+  journal={arXiv preprint arXiv:2309.08105},
+  year={2023}
+}
+"""
+
+class LibriheavyConfig(datasets.BuilderConfig):
+    """BuilderConfig for Libriheavy."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Libriheavy.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(LibriheavyConfig, self).__init__(**kwargs)
+
+
+class Libriheavy(datasets.GeneratorBasedBuilder):
+    """Libriheavy dataset."""
+
+    BUILDER_CONFIGS = [
+        LibriheavyConfig(name="libriheavy", version=datasets.Version("1.0.0"), description="Libriheavy dataset."),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "audio": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "word_segments": datasets.Sequence(
+                        {
+                            "start": datasets.Value("int32"),
+                            "end": datasets.Value("int32"),
+                            "word": datasets.Value("string"),
+                        }
+                    ),
+                    "mel_spectrogram": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # first, we load speaker_list.json
+        speaker_list = "medium_data/speaker_list.json"
+        speaker_list = dl_manager.download_and_extract(speaker_list)
+        with open(speaker_list, "r") as f:
+            speaker_list = json.load(f)
+        # now we load the individual speaker metadata
+        speaker_metadata = {}
+        for speaker_id, metadata_path in speaker_list.items():
+            metadata_path = f"medium_data/{metadata_path}"
+            metadata_path = dl_manager.download_and_extract(metadata_path)
+            with open(metadata_path, "r") as f:
+                speaker_metadata[speaker_id] = json.load(f)
+        speaker_chunks = []
+        for speaker_id, metadata in speaker_metadata.items():
+            for chunk_id, chunk in metadata["chunks"].items():
+                speaker_chunks.append(
+                    {
+                        "speaker_id": speaker_id,
+                        "id": f"{speaker_id}_{chunk_id}",
+                        "audio": dl_manager.download(f"medium_data/{chunk['npz'].replace('.gz', '')}"),
+                        "text": dl_manager.download(f"medium_data/{chunk['json']}"),
+                    }
+                )
+        # shuffle the chunks
+        np.random.seed(42)
+        np.random.shuffle(speaker_chunks)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"speaker_chunks": speaker_chunks},
+            )
+        ]
+
+    def _generate_examples(self, speaker_chunks):
+        """Yields examples."""
+        for chunk in speaker_chunks:
+            npz = dict(np.load(chunk["audio"], allow_pickle=True))
+            utterances = npz.keys()
+            with gzip.open(chunk["text"], "rt") as f:
+                text = json.load(f)
+            for utterance_id, utterance in text.items():
+                result =  {
+                    "id": chunk["speaker_id"] + "_" + utterance_id,
+                    "speaker_id": chunk["speaker_id"],
+                    "audio": chunk["audio"],
+                    "text": chunk["text"],
+                    "word_segments": [
+                        {"start": segment[0], "end": segment[1], "word": segment[2]} for segment in utterance["word_segments"]
+                    ],
+                    "mel_spectrogram": npz[str(utterance_id)].item()["mel"][0][0],
+                }
+                yield chunk["speaker_id"] + "_" + utterance_id, result