Delete speechio.py

speechio.py

@@ -1,184 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-"""
-Aishell dataset.
-"""
-
-import os
-import datasets
-from huggingface_hub import list_repo_files
-import gzip
-import json
-
-repo_id = "yuekai/speechio"
-
-_DESCRIPTION = """\
-speechio test set 
-"""
-_HOMEPAGE = "https://github.com/SpeechColab"
-
-SPEECHIO_TESTSET_INDEX=26
-_SUBSETS = []
-for i in range(SPEECHIO_TESTSET_INDEX + 1):
-    idx = f"{i}".zfill(2)
-    _SUBSETS.append(f"SPEECHIO_ASR_ZH000{idx}")
-
-_BASE_DATA_URL = f"https://huggingface.co/datasets/{repo_id}/resolve/main/"
-
-_AUDIO_ARCHIVE_URL = _BASE_DATA_URL + "data/speechio_cuts_{subset}.{archive_id:08}.tar.gz"
-
-_META_URL = _BASE_DATA_URL + "data/speechio_cuts_{subset}.{archive_id:08}.jsonl.gz"
-
-FILES = list_repo_files(repo_id, repo_type="dataset")
-
-logger = datasets.utils.logging.get_logger(__name__)
-
-
-class CustomAudioConfig(datasets.BuilderConfig):
-    """BuilderConfig for the dataset."""
-
-    def __init__(self, name, *args, **kwargs):
-        """BuilderConfig for the dataset.
-        """
-        super().__init__(name=name, *args, **kwargs)
-        assert name in _SUBSETS, f"Unknown subset {name}"
-        self.subsets_to_download = (name,)
-
-
-class SPEECHIO(datasets.GeneratorBasedBuilder):
-    """
-    Aishell is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
-    labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
-    and unsupervised training (this implementation contains only labelled data for now).
-    Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
-    and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
-    sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
-    for speech recognition training, and to filter out segments with low-quality transcription. For system training,
-    Aishell provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
-    For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage,
-    and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand,
-    are re-processed by professional human transcribers to ensure high transcription quality.
-    """
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [CustomAudioConfig(name=subset) for subset in _SUBSETS]
-
-    DEFAULT_WRITER_BATCH_SIZE = 128
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "segment_id": datasets.Value("string"),
-                "speaker": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "audio": datasets.Audio(sampling_rate=16_000),
-                "original_full_path": datasets.Value("string"),  # relative path to full audio in original data dirs
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-        )
-
-    def _split_generators(self, dl_manager):
-        
-        splits = (self.config.name,)
-
-
-        split_to_n_archives = {
-            split: int(len([file for file in FILES if f"cuts_{split}" in file]) / 2)
-            for split in splits
-        }
-
-        # 2. prepare sharded archives with audio files
-        audio_archives_urls = {
-            split: 
-                [
-                    _AUDIO_ARCHIVE_URL.format(subset=split, 
-                                              archive_id=i)
-                    for i in range(split_to_n_archives[split])
-                ]
-            for split in splits
-        }
-
-        audio_archives_paths = dl_manager.download(audio_archives_urls)
-
-        local_audio_archives_paths = dl_manager.extract(audio_archives_paths) if not dl_manager.is_streaming \
-            else None
-
-        # 3. prepare sharded metadata csv files
-        meta_urls = {
-            split: [
-                    _META_URL.format(subset=split, archive_id=i)
-                    for i in range(split_to_n_archives[split])
-                ]
-            for split in splits
-        }
-
-        meta_paths = dl_manager.download(meta_urls)
-
-       
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "audio_archives_iterators": [
-                        dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths[self.config.name]
-                    ],
-                    "local_audio_archives_paths": local_audio_archives_paths[
-                        self.config.name] if local_audio_archives_paths else None,
-                    "meta_paths": meta_paths[self.config.name]
-                },
-            ),
-        ]
-
-    def _generate_examples(self, audio_archives_iterators, local_audio_archives_paths, meta_paths):
-
-        def load_meta(file_path):
-            data = {}
-
-            with gzip.open(file_path, 'rt', encoding='utf-8') as f:
-                for line in f:
-                    item = json.loads(line)
-                    data[item["id"]] = item
-            return data
-
-        assert len(audio_archives_iterators) == len(meta_paths)
-        if local_audio_archives_paths:
-            assert len(audio_archives_iterators) == len(local_audio_archives_paths)
-
-        for i, (meta_path, audio_archive_iterator) in enumerate(zip(meta_paths, audio_archives_iterators)):
-            meta_dict = load_meta(meta_path)
-
-            for audio_path_in_archive, audio_file in audio_archive_iterator:
-                # `audio_path_in_archive` is like "data/aishell_cuts_test.00000000/BAC/BAC009S0764W0393-359.wav"
-                audio_filename = os.path.split(audio_path_in_archive)[-1]
-
-                audio_id = audio_filename.split(".wav")[0]
-                audio_meta = meta_dict[audio_id]
-
-                audio_meta["segment_id"] = audio_id
-                audio_meta["original_full_path"] = audio_meta["recording"]["sources"][0]["source"]
-                audio_meta["text"] = audio_meta['supervisions'][0]['text']
-                audio_meta["speaker"] = audio_meta['supervisions'][0]['speaker']
-
-                path = os.path.join(local_audio_archives_paths[i], audio_path_in_archive) if local_audio_archives_paths \
-                    else audio_path_in_archive
-
-                yield audio_id, {
-                    "audio": {"path": path , "bytes": audio_file.read()},
-                    **{feature: value for feature, value in audio_meta.items() if feature in self.info.features}
-                }