""" Babelbox Voice Dataset""" import os import csv import codecs import datasets from typing import List from pathlib import Path from tqdm import tqdm import torchaudio from io import BytesIO import pydub from pydub import AudioSegment from pydub.silence import detect_leading_silence import numpy as np logger = datasets.logging.get_logger(__name__) _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 } """ _HF_REPO_PATH = "https://huggingface.co/datasets/babelbox/babelbox_voice/" 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.data_url = kwargs.pop("data_url", None) self.nb_data_shards = kwargs.pop("nb_data_shards", None) self.metadata_url = kwargs.pop("metadata_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 Babel Voice includes data from National Library of Norway", features=["path", "audio", "sentence"], data_url= _HF_REPO_PATH + "resolve/main/data/nst/nst-data-{:0>3d}.tar.gz", nb_data_shards = 42, metadata_url= _HF_REPO_PATH + "resolve/main/data/nst/metadata.tar.gz" ) ] 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), "text": datasets.Value("binary"), "speaker_id": datasets.Value("string"), "sex": datasets.Value("string"), "accent": datasets.Value("string"), } ) else: features = datasets.Features( { "path": datasets.Value("string"), "audio": datasets.features.Audio(sampling_rate=16_000), "text": datasets.Value("binary"), "speaker_id": datasets.Value("string"), "sex": datasets.Value("string"), "accent": datasets.Value("string"), } ) return datasets.DatasetInfo( description=description, features=features, supervised_keys=None, version=self.config.version ) def get_metadata(self, dl_manager, metadata_url): if metadata_url == None: return None metadata_path = dl_manager.download(metadata_url) local_extracted_metadata_path = dl_manager.extract(metadata_path) if not dl_manager.is_streaming else None def clean_sentence(sentence): return (sentence .replace("\\Komma", "",) .replace("\\Punkt", "") .replace("\\Utropstecken", "") .replace("\\Frågetecken", "")) metadata_archive = dl_manager.iter_archive(metadata_path) metadata = {} for path, file in metadata_archive: reader = csv.DictReader(codecs.iterdecode(file, 'utf-8')) for row in tqdm(reader, desc="Reading metadata..."): filename = row['filename_channel_1'] metadata_item = { 'sentence': clean_sentence(row['text']), 'speaker_id': row['Speaker_ID'], 'sex': row['Sex'], 'accent': row['Region_of_Youth'] } metadata[filename] = metadata_item return metadata def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: download_urls = [self.config.data_url.format(i) for i in range(1, self.config.nb_data_shards + 1) ] archive_paths = dl_manager.download(download_urls) local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {} metadata = self.get_metadata(dl_manager, self.config.metadata_url) 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 def get_audiosegment(array): byte_array = np.int16(array * 2 ** 15).tobytes() audio_segment = pydub.AudioSegment(byte_array, frame_rate=16000, sample_width=2, channels=1) return audio_segment def get_leading_and_trailing_silence(audio_array): audio_segment = get_audiosegment(audio_array) leading = detect_leading_silence(audio_segment) trailing = detect_leading_silence(audio_segment.reverse()) return leading, trailing def get_timestamp(audio_array): leading_silence, trailing_silence = get_leading_and_trailing_silence(audio_array) start_len = leading_silence / 1000 start_time = round(start_len / 2, 2) * 2 end_len = (len(audio_array) / sampling_rate) - (trailing_silence / 1000) end_time = round(end_len / 2, 2) * 2 return (start_time, end_time) def filter_sentence(sentence): if "... tyst under denna inspelning ..." in sentence: return False return True def get_audio(file): file_buf = BytesIO(file.read()) array, audio_sr = torchaudio.load(file_buf, format="wav") return array[0], audio_sr 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) metadata_item = metadata[path] audio_array, audio_sr = get_audio(file) if filter_sentence(metadata_item['sentence']) == False: continue audio_len = len(audio_array) / sampling_rate audio_len = round(audio_len / 2, 2) * 2 print(get_leading_and_trailing_silence(audio_array)) text = { "text": metadata_item['sentence'], "offsets": [ {"text": metadata_item['sentence'], "timestamp": get_timestamp(audio_array) } ] } result = { 'path' : path, 'audio': {"path": path, "array": audio_array, "sampling_rate": sampling_rate}, 'text' : text, 'speaker_id' : metadata_item['speaker_id'], 'sex' : metadata_item['sex'], 'accent' : metadata_item['accent'] } yield path, result