pseudolabel-science-large-v3-timestamp

# Pseudolabel science context audio using Whisper Large V3

Original audio from [malaysia-ai/science-context-youtube](https://huggingface.co/datasets/malaysia-ai/science-context-youtube), we split every 30 seconds and pseudolabelled using Whisper Large V3.

## how to prepare the dataset

```bash
huggingface-cli download --repo-type dataset \
--include 'science-chunk-*.zip' \
--local-dir './' \
--max-workers 20 \
mesolitica/pseudolabel-science-large-v3-timestamp

wget https://gist.githubusercontent.com/huseinzol05/2e26de4f3b29d99e993b349864ab6c10/raw/9b2251f3ff958770215d70c8d82d311f82791b78/unzip.py
python3 unzip.py
```

# 基于Whisper Large V3为科学语境音频生成伪标签
本数据集的原始音频源自[malaysia-ai/science-context-youtube](https://huggingface.co/datasets/malaysia-ai/science-context-youtube)，我们将音频按每30秒为单位进行切片，并使用Whisper Large V3完成伪标注。

## 数据集制备流程
bash
huggingface-cli download --repo-type dataset
--include 'science-chunk-*.zip'
--local-dir './'
--max-workers 20
mesolitica/pseudolabel-science-large-v3-timestamp

wget https://gist.githubusercontent.com/huseinzol05/2e26de4f3b29d99e993b349864ab6c10/raw/9b2251f3ff958770215d70c8d82d311f82791b78/unzip.py
python3 unzip.py

搜集汇总

背景与挑战

背景概述

该数据集基于malaysia-ai/science-context-youtube的原始音频，通过Whisper Large V3模型进行伪标签处理，并将音频分割为每30秒的片段。它提供了详细的下载和准备指南，适用于科学上下文音频分析任务。