# coding=utf-8 import json import re from collections import OrderedDict import datasets from .audio_utils import get_waveform_from_audio_or_stored_zip from .meta import SUBSET_NAMES_AND_PATHS BASE_DIR = "https://huggingface.co/datasets/bofenghuang/stt-pseudo-labeled-whisper-large-v3-multilingual/resolve/main/" # BASE_DIR = "" VERSION = "0.0.1" _DESCRIPTION = "" # todo def jload(f, mode="r"): """Load a .json file into a dictionary.""" with open(f, mode) as f: return json.load(f) def jsonl_load(f, mode="r"): """Load a .jsonl file into a dictionary.""" with open(f, mode) as f: return [json.loads(l.strip()) for l in f] # SUBSET_NAMES_AND_PATHS = jload("./meta.json") class MultilingualWhisperLargeV3PseudoLabeledSpeechDatasetConfig(datasets.BuilderConfig): """BuilderConfig for stt-pseudo-labeled-whisper-large-v3-multilingual.""" def __init__(self, name, path, audio_zip_files, text_file, version, **kwargs): self.base_data_path = path self.audio_zip_files = audio_zip_files self.text_file = text_file description = f"stt-pseudo-labeled-whisper-large-v3-multilingual speech to text dataset in {name}." super(MultilingualWhisperLargeV3PseudoLabeledSpeechDatasetConfig, self).__init__( name=name, version=datasets.Version(version), description=description, **kwargs, ) class MultilingualWhisperLargeV3PseudoLabeledSpeechDataset(datasets.GeneratorBasedBuilder): """stt-pseudo-labeled-whisper-large-v3-multilingual dataset.""" VERSION = datasets.Version(VERSION) DEFAULT_CONFIG_NAME = "en-yodas-000" BUILDER_CONFIGS = [ MultilingualWhisperLargeV3PseudoLabeledSpeechDatasetConfig( name, SUBSET_NAMES_AND_PATHS[name]["dir"], SUBSET_NAMES_AND_PATHS[name]["audio_zip_files"], SUBSET_NAMES_AND_PATHS[name]["text_file"], version=VERSION, ) for name in SUBSET_NAMES_AND_PATHS ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( OrderedDict( [ # ("id", datasets.Value("string")), # ("utt_id", datasets.Value("string")), ("audio_filepath", datasets.Value("string")), ("audio", datasets.Audio(sampling_rate=16_000)), ("duration", datasets.Value("float")), ("text", datasets.Value("string")), ("whisper_transcript", datasets.Value("string")), ("text_norm", datasets.Value("string")), ("whisper_transcript_norm", datasets.Value("string")), ("wer", datasets.Value("float")), ("prev_text", datasets.Value("string")), ("prev_whisper_transcript", datasets.Value("string")), ] ) ), supervised_keys=None, homepage="", # TODO citation="", # TODO ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" if dl_manager.is_streaming: raise NotImplementedError("The streaming mode is not supported yet.") print("Downloading audio and text...") audio_tar_files = dl_manager.download( [f"{BASE_DIR}{self.config.base_data_path}/{audio_zip_file}" for audio_zip_file in self.config.audio_zip_files] ) text_file = dl_manager.download(f"{BASE_DIR}{self.config.base_data_path}/{self.config.text_file}") snapshot_path = text_file.split(self.config.base_data_path)[0] text_archives = jsonl_load(text_file) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ # "is_streaming": dl_manager.is_streaming, "text_archives": text_archives, "snapshot_path": snapshot_path, }, ), ] def _generate_examples(self, text_archives, snapshot_path): """Yields examples.""" id_ = 0 for sample in text_archives: # replace path audio_filepath = sample["audio_zip_filepath"] audio_filepath = re.sub( rf"^.*/{self.config.base_data_path}", f"{snapshot_path}/{self.config.base_data_path}", audio_filepath ) # read wav directly from zipped file waveform, sample_rate = get_waveform_from_audio_or_stored_zip(audio_filepath) result = { "id": id_, "audio_filepath": audio_filepath, "audio": { "path": audio_filepath, # "bytes": waveform, "array": waveform, "sampling_rate": sample_rate, }, "duration": sample["duration"], "text": sample["text"], "whisper_transcript": sample["whisper_transcript"], "text_norm": sample["text_norm"], "whisper_transcript_norm": sample["whisper_transcript_norm"], "wer": sample["wer"], "prev_text": sample["prev_text"], "prev_whisper_transcript": sample["prev_whisper_transcript"], } yield id_, result id_ += 1