ESpeech-tuchniyzhab

# Tuchniy Zhab YouTube Audio Dataset ## Dataset Description This dataset contains 306 hours of processed audio segments extracted from the "Tuchniy Zhab" YouTube channel with corresponding metadata. Each audio file represents a segment from the channel's videos and content, processed at 44.1kHz sample rate. ### Dataset Summary - **Language**: Russian - **Task**: TTS, ASR, Quality Assessment - **Audio format**: MP3, 44.1kHz sample rate - **Structure**: Segmented audio files with JSON metadata - **Source**: Tuchniy Zhab YouTube channel content ## Dataset Structure ### Data Fields #### Basic Information - `audio`: Audio data (44.1kHz sample rate, MP3 format) - `file_name`: Name of the audio segment file (format: `<original_name>_<idx>.mp3`) - `segment_index`: Index of the audio segment within the original video - `original_name`: Original name of the YouTube video recording #### Transcription and Timing - `text`: Transcribed text of the audio segment - `start`: Start time of the segment in seconds - `end`: End time of the segment in seconds - `words`: Word-level timestamps and confidence scores #### Speaker Information - `speaker`: Speaker identifier (e.g., "SPEAKER_00") #### Quality Metrics - `emos_overall`: EMOS overall quality score - `noise_confidence`: Noise detection confidence  #### Segment Structure - `num_sentences`: Number of sentences (for merged segments) - `original_segments`: Original subsegments data (for merged segments) #### VAD (Voice Activity Detection) - `vad_trimmed`: Whether VAD trimming was applied - `vad_start`: VAD start time - `trim_ratio`: Ratio of trimmed audio ### Data Splits - **Train**: All available YouTube video segments ## Dataset Creation ### Source Data The dataset consists of audio content extracted from the "Tuchniy Zhab" YouTube channel. The channel produces various types of content primarily in Russian. Each YouTube video has been processed and segmented into multiple audio clips, with each segment saved as a separate MP3 file along with its transcription and metadata. ## Usage ### Loading the Dataset Load and extract the tar archive files using: ```bash cat tuchniyzhab_archive.tar.aa tuchniyzhab_archive.tar.ab tuchniyzhab_archive.tar.ac > tuchniyzhab_archive.tar && tar -xf tuchniyzhab_archive.tar ``` ### Citation Information ```bibtex @dataset{tuchniyzhab_youtube_audio_dataset, title={Tuchniy Zhab YouTube Audio Dataset}, author={Denis Petrov}, year={2025}, url={https://huggingface.co/datasets/ESpeech/ESpeech-tuchniyzhab/} } ```# Tuchniy Zhab YouTube音频数据集 ## 数据集描述 本数据集包含从“Tuchniy Zhab”YouTube频道提取的306小时经预处理的音频片段及对应元数据。每条音频文件均源自该频道的视频内容片段，采样率为44.1kHz。 ### 数据集摘要 - **语言**：俄语 - **任务**：文本转语音（TTS, Text-to-Speech）、自动语音识别（ASR, Automatic Speech Recognition）、质量评估 - **音频格式**：MP3，采样率44.1kHz - **结构**：带JSON元数据的分段音频文件 - **来源**：Tuchniy Zhab YouTube频道的视频内容 ## 数据集结构 ### 数据字段 #### 基础信息 - `audio`：音频数据（采样率44.1kHz，MP3格式） - `file_name`：音频片段文件名（格式：`<original_name>_<idx>.mp3`） - `segment_index`：原始视频内的音频片段索引 - `original_name`：原始YouTube视频的名称 #### 转录与时序信息 - `text`：音频片段的转录文本 - `start`：片段的起始时间（单位：秒） - `end`：片段的结束时间（单位：秒） - `words`：词级时间戳与置信度得分 #### 说话人信息 - `speaker`：说话人标识符（例如："SPEAKER_00"） #### 质量指标 - `emos_overall`：EMOS整体质量得分 - `noise_confidence`：噪声检测置信度  #### 分段结构 - `num_sentences`：句子数量（针对合并后的片段） - `original_segments`：原始子片段数据（针对合并后的片段） #### 语音活动检测（VAD, Voice Activity Detection） - `vad_trimmed`：是否应用了VAD修剪 - `vad_start`：VAD起始时间 - `trim_ratio`：音频修剪比例 ### 数据划分 - **训练集**：所有可用的YouTube视频片段 ## 数据集构建 ### 源数据 本数据集的音频内容均提取自“Tuchniy Zhab”YouTube频道，该频道主要发布俄语内容的各类视频。每条YouTube视频均经过处理并分割为多个音频片段，每个片段保存为独立的MP3文件，同时附带其转录文本与元数据。 ## 使用说明 ### 加载数据集 使用以下命令解压tar归档文件： bash cat tuchniyzhab_archive.tar.aa tuchniyzhab_archive.tar.ab tuchniyzhab_archive.tar.ac > tuchniyzhab_archive.tar && tar -xf tuchniyzhab_archive.tar ### 引用信息 bibtex @dataset{tuchniyzhab_youtube_audio_dataset, title={Tuchniy Zhab YouTube音频数据集}, author={Denis Petrov}, year={2025}, url={https://huggingface.co/datasets/ESpeech/ESpeech-tuchniyzhab/} }

搜集汇总

背景与挑战

背景概述

该数据集包含从俄语YouTube频道'Tuchniy Zhab'提取的306小时音频片段，采样率为44.1kHz，格式为MP3，并配有转录文本、说话人信息和质量指标等元数据，适用于TTS、ASR和质量评估任务。