README.md

---
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: conversation_id
    dtype: string
  - name: split
    dtype: string
  - name: utterance_idx
    sequence: int64
  - name: abstract_symbol
    sequence: string
  - name: start_time
    sequence: float64
  - name: end_time
    sequence: float64
  - name: abs_start_time
    sequence: float64
  - name: abs_end_time
    sequence: float64
  - name: text
    sequence: string
  - name: duration_sec
    sequence: float64
  - name: segment_id
    dtype: int64
  - name: segment_conversation_id
    dtype: string
  - name: rir
    dtype: bool
  splits:
  - name: train
    num_bytes: 25575970863.525
    num_examples: 30313
  - name: validation
    num_bytes: 3028603290.34
    num_examples: 3595
  - name: test
    num_bytes: 3133192896.73
    num_examples: 3674
  download_size: 29252180615
  dataset_size: 31737767050.595
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: cc
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- diarization
- asr
---
# 🗣️ LibriConvo-Segmented

**LibriConvo-Segmented** is a segmented version of the **LibriConvo** corpus — a **simulated two-speaker conversational dataset** built using *Speaker-Aware Conversation Simulation (SASC)*.  
It is designed for **training and evaluation of multi-speaker speech processing systems**, including **speaker diarization**, **automatic speech recognition (ASR)**, and **overlapping speech modeling**.

This segmented version provides ≤30-second conversational fragments derived from full LibriConvo dialogues, with 40% of them having room impulse responses applied on them.

The full paper, detailing the creation of the corpus, as well as baseline ASR and diarization results can be found here: https://arxiv.org/abs/2510.23320

---

## 🧠 Overview

**LibriConvo** ensures **natural conversational flow** and **contextual coherence** by:

- Organizing LibriTTS utterances by **book** to maintain narrative continuity.
- Using statistics from **CallHome** for pause modeling.  
- Applying **compression** to remove excessively long silences while preserving turn dynamics.  
- Enhancing **acoustic realism** via a novel **Room Impulse Response (RIR) selection procedure**, ranking configurations by spatial plausibility.  
- Producing **speaker-disjoint splits** for robust evaluation and generalization.

In total, the full LibriConvo corpus comprises **240.1 hours** across **1,496 dialogues** with **830 unique speakers**.  
This segmented release provides **shorter, self-contained audio clips** suitable for fine-tuning ASR and diarization models.

---

## 📦 Dataset Summary

| Split | # Segments |
|:------|------------:|
| Train | 30,313     | 
| Validation | 3,595 | 
| Test | 3674 |

**Sampling rate:** 16 kHz  
**Audio format:** WAV (mono)  
**Split criterion:** Speaker-disjoint  

---

## 📂 Data Structure

Each row represents a single speech segment belonging to a simulated conversation between two speakers.

| Field | Type | Description |
|:------|:----:|:------------|
| `conversation_id` | string | Conversation identifier |
| `utterance_idx` | int64 | Utterance index within the conversation |
| `abstract_symbol` | string | High-level symbolic utterance ID ('A' or 'B') |
| `transcript` | string | Text transcription of the utterance |
| `duration_sec` | float64 | Segment duration (seconds) |
| `rir_file` | string | Room impulse response file used |
| `delay_sec` | float64 | Delay applied for realistic speaker overlap |
| `start_time_sec`, `end_time_sec` | float64 | Start and end times within the conversation |
| `abs_start_time_sec`, `abs_end_time_sec` | float64 | Global (absolute) start and end times |
| `segment_id` | int64 | Local segment index |
| `segment_conversation_id` | string | Unique segment identifier |
| `split` | string | One of `train`, `validation`, or `test` |
| `audio` | Audio (16 kHz) | Decoded audio data |
---

## 🚀 Loading the Dataset

```python
from datasets import load_dataset

ds = load_dataset("gedeonmate/LibriConvo-segmented")

print(ds)
# DatasetDict({
#     train: Dataset(...),
#     validation: Dataset(...),
#     test: Dataset(...)
# })
```

---

📚 Citation

If you use the LibriConvo dataset or the associated Speaker-Aware Conversation Simulation (SASC) methodology in your research, please cite the following papers:

```
@misc{gedeon2025libriconvo,
  title         = {LibriConvo: Simulating Conversations from Read Literature for ASR and Diarization},
  author        = {Máté Gedeon and Péter Mihajlik},
  year          = {2025},
  eprint        = {2510.23320},
  archivePrefix = {arXiv},
  primaryClass  = {eess.AS},
  url           = {https://arxiv.org/abs/2510.23320}
}
```

```
@misc{gedeon2025sasc,
      title={From Independence to Interaction: Speaker-Aware Simulation of Multi-Speaker Conversational Timing}, 
      author={Máté Gedeon and Péter Mihajlik},
      year={2025},
      eprint={2509.15808},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2509.15808}, 
}
```