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: text
    sequence: string
  - name: duration_sec
    sequence: float64
  - name: rir
    dtype: bool
  splits:
  - name: train
    num_bytes: 24925891266.516
    num_examples: 1199
  - name: validation
    num_bytes: 3108447220.0
    num_examples: 137
  - name: test
    num_bytes: 3172529166.0
    num_examples: 160
  download_size: 29092766585
  dataset_size: 31206867652.516
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---


# 🗣️ LibriConvo-Raw

**LibriConvo-Raw** is the **full-length, unsegmented version** of the **LibriConvo** corpus — a **simulated two-speaker conversational dataset** created using *Speaker-Aware Conversation Simulation (SASC)*.  
It is designed for **training and evaluation of conversational speech systems**, particularly for **multi-speaker ASR**, **speaker diarization**, and **overlap detection**.

Unlike the segmented release, this version contains **complete simulated dialogues** with natural temporal structure, pauses, and overlaps preserved exactly as modeled by SASC.

The full paper describing the dataset generation, simulation pipeline, and baseline results is available at:  
🔗 [https://arxiv.org/abs/2510.23320](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 version is particularly suited for **end-to-end conversational modeling**, **long-form ASR**, **diarization pretraining**, and **speaker interaction analysis**.

---

## 🎧 Dataset Summary

| Split | # Conversations | Duration (approx.) |
|:------|----------------:|-------------------:|
| Train | 1,199 | ~193.7 hours |
| Validation | 137 | ~23.1 hours |
| Test | 160 | ~23.4 hours |

**Total duration:** ~240.1 hours  
**Unique speakers:** 830  
**Sampling rate:** 16 kHz  
**Audio format:** WAV (mono)  
**Split criterion:** Speaker-disjoint  
**RIR coverage:** ~40% of conversations include room impulse response convolution

---

## 📂 Data Structure

Each row in the dataset represents a **complete two-speaker conversation** with full dialogue audio and time-aligned utterances.

| Field | Type | Description |
|:------|:----:|:------------|
| `conversation_id` | string | Unique conversation identifier |
| `split` | string | One of `train`, `validation`, or `test` |
| `utterance_idx` | sequence(int64) | Ordered list of utterance indices |
| `abstract_symbol` | sequence(string) | Speaker label sequence (`A` or `B`) |
| `start_time` | sequence(float64) | Start time of each utterance (seconds) |
| `end_time` | sequence(float64) | End time of each utterance (seconds) |
| `text` | sequence(string) | Transcription of each utterance |
| `duration_sec` | sequence(float64) | Duration of each utterance (seconds) |
| `rir` | bool | Indicates if a Room Impulse Response was applied |
| `audio` | Audio (16 kHz) | Full conversation waveform |

---

## 🗂️ Example

```python
from datasets import load_dataset

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

sample = ds["train"][0]

print(sample["conversation_id"])
print(sample["text"][:5])   # First few utterances
```

---

📚 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}, 
}
```