Datasets:
Tasks:
Automatic Speech Recognition
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 5,247 Bytes
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---
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},
}
``` |