Datasets:

khursanirevo
/

multiturn_ks_embedded

Tasks:

Automatic Speech Recognition

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audio audioduration (s) 12.2 30

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khursanirevo/multiturn_ks_embedded

Dataset Description

Multiturn dialogue dataset with embedded audio and multi-language transcripts from 3 YouTube videos.

Features

Audio: Embedded stereo audio (WAV format, bytes embedded directly in dataset)
Segments: Speaker turn-level annotations with timestamps for English and Malay
Multi-language: Transcripts in 9 languages (en, ms, zh-Hans, zh-Hant, ru, id, ar, ja, ko)
Video ID: YouTube video identifier for each chunk
Chunking: 30-second chunks with 0.5s overlap
Self-contained: No external audio files needed

Columns

audio: Embedded stereo audio as bytes (WAV, 24kHz)
video_id: YouTube video identifier
sentence: Full transcript for the chunk (English)
segments_en: JSON list of English speaker turns with speaker, start, end, text fields
segments_ms: JSON list of Malay speaker turns with speaker, start, end, text fields
total_speakers: Number of speakers in chunk (typically 2)
sentence_ms, sentence_en, etc.: Transcripts in each language

Usage

from datasets import load_dataset
import json
import io
import soundfile as sf

# Load dataset
dataset = load_dataset("khursanirevo/multiturn_ks_embedded")

# Access a chunk
chunk = dataset[0]

# Load embedded audio
audio_bytes = chunk["audio"]
buffer = io.BytesIO(audio_bytes)
audio, sample_rate = sf.read(buffer)

print(f"Audio shape: {audio.shape}")
print(f"Sample rate: {sample_rate}")
print(f"Duration: {len(audio)/sample_rate:.1f}s")

# Access speaker turns
video_id = chunk["video_id"]
segments_en = json.loads(chunk["segments_en"])
segments_ms = json.loads(chunk["segments_ms"])

print(f"From video: {video_id}")
print(f"\nEnglish segments:")
for seg in segments_en[:3]:
    speaker = seg['speaker']
    start = seg['start']
    end = seg['end']
    text = seg['text'][:60]
    print(f"  Speaker {speaker} ({start}s-{end}s): {text}...")