initial commit

README.md

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+annotations_creators:
+- crowdsourced
+language:
+- sv-SE
+language_creators:
+- crowdsourced
+license:
+- cc0-1.0
+multilinguality:
+- monolingual
+pretty_name: Babelbox Voice
+size_categories:
+- 100K<n<1M
+source_datasets: []
+tags:
+- NST
+task_categories:
+- automatic-speech-recognition
+task_ids: []

babelbox_voice.py

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+""" Babelbox Voice Dataset"""
+
+import csv
+import os
+import urllib
+
+import datasets
+import requests
+import glob
+import gzip
+from typing import List
+from datasets.utils.py_utils import size_str
+logger = datasets.logging.get_logger(__name__)
+import torchaudio
+import torch
+from tqdm import tqdm
+
+_CITATION = """\
+@inproceedings{babelboxvoice:2022,
+  author = {Andersson, O. and Bjelkenhed, M. and Bielsa, M. et al},
+  title = {Babelbox Voice: A Speech Corpus for training Whisper},
+  year = 2022
+}
+"""
+
+class BabelboxVoiceConfig(datasets.BuilderConfig):
+    """BuilderConfig for BabelboxVoice."""
+
+    def __init__(self, name, version, **kwargs):
+        self.name = name
+        self.version = version
+        self.features = kwargs.pop("features", None)
+        self.description = kwargs.pop("description", None)
+        self.archive_url = kwargs.pop("archive_url", None)
+        self.meta_url = kwargs.pop("meta_url", None)
+
+        description = (
+            f"Babelbox Voice speech to text dataset."
+        )
+        super(BabelboxVoiceConfig, self).__init__(
+            name=name,
+            version=version,
+            **kwargs,
+        )
+
+
+class BabelboxVoice(datasets.GeneratorBasedBuilder):
+    
+    VERSION = datasets.Version("1.0.0")
+    
+    BUILDER_CONFIGS = [
+        BabelboxVoiceConfig(
+                name="nst",
+                version=VERSION,
+                description="This part of Pandora Voice includes data from National Library of Norway",
+                features=["path", "audio", "sentence"],
+                archive_url="/home/jovyan/shared-data/data/nst/archive",
+                meta_url="/home/jovyan/shared-data/data/nst/NST_se.csv"
+            )
+    ]
+         
+    DEFAULT_CONFIG_NAME = "nst"
+
+    def _info(self):
+        description = (
+            "Babelbox Voice is an initiative to help teach machines how real people speak. "
+        )
+        if self.config.name == "nst":
+            features = datasets.Features(
+                {
+                    "path": datasets.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=16_000),
+                    "sentence": datasets.Value("string")
+                }
+            )
+      
+        return datasets.DatasetInfo(
+            description=description,
+            features=features,
+            supervised_keys=None,
+            version=self.config.version
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        
+        archive_dir="/home/jovyan/shared-data/data/nst/archive"
+        archive_files = sorted(glob.glob(archive_dir + '/**.tar.gz'), reverse=False)
+        
+        archive_paths = dl_manager.download(archive_files)
+        
+        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+        
+        meta_url = self.config.meta_url
+        
+        meta_path = dl_manager.download_and_extract(meta_url)
+        
+        metadata = {}
+        with open(meta_path, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for row in tqdm(reader, desc="Reading metadata..."):
+                filename = row['filename_channel_1']
+                sentence = row['text']
+                metadata[filename] = sentence
+        
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, 
+                gen_kwargs={
+                    "local_extracted_archive_paths": local_extracted_archive_paths,
+                    "archives": [dl_manager.iter_archive(path) for path in archive_paths],
+                    "metadata": metadata
+                })
+        ]
+    
+    def _generate_examples(self, local_extracted_archive_paths, archives, metadata):
+           
+            sampling_rate = 16000
+                              
+            for i, audio_archive in enumerate(archives):
+                for path, file in audio_archive:
+                    if local_extracted_archive_paths == False:
+                        path = os.path.join(local_extracted_archive_paths[i], path) 
+                    result = dict()          
+                    result["path"] = path
+                    result["audio"] = {"path": path, "bytes": file.read()}
+                    result["sentence"] = metadata[path] 
+                    yield path, result
+