---
license: cc-by-4.0
language:
- multilingual
tags:
- streaming-cot
- chain-of-thought
- speech
- qa
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.parquet
  - split: eval
    path: data/eval.parquet
- config_name: high_quality
  data_files:
  - split: train
    path: data/high_quality_train.parquet
  - split: eval
    path: data/high_quality_eval.parquet
---

# LifeSpeechQAStreamingCoT — Source-Expanded Release

**Version:** vNext-source-expanded

## Overview

Multi-source release. This release adds source diversity beyond the original FLEURS seed data.

## Source Distribution

- FLEURS: 4091 rows ()
- VoxPopuli: 4321 rows ()
- MultilingualLibriSpeech: ❌ 

## Stats

| Metric | Value |
|--------|-------|
| Active rows | 8,412 |
| Train/Eval | 6,944/1,468 |
| High quality | 8,412 |
| SFT-ready | 100.0% |
| Target grounded | 99.5% |

## Schema

Top-level fields: `id`, `split`, `task_type`, `turn_type`, `input_modality`, `output_modality`, `audio`, `input`, `timestamps`, `streaming`, `target`, `quality`, `source`, `metadata`

Use `target.response` for SFT training.

## Limitations
- Audio is external reference only (no bundled bytes)
- 
- QA is FLEURS + VoxPopuli only; additional speech QA sources limited
- 
- Reasoning is template-generated, not LLM-written

## Usage

```python
from datasets import load_dataset
ds = load_dataset("skyzhou06/LifeSpeechQAStreamingCoT")
ds_hq = load_dataset("skyzhou06/LifeSpeechQAStreamingCoT", "high_quality")
```