---
license: apache-2.0
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- audio
- speech
- asr
- robustness
- conversational
- multi-talker
- overlap
- noise
- far-field
pretty_name: Mega-ASR Conversational Overlap
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: far_field
    path: data/far_field.parquet
  - split: far_field_noise
    path: data/far_field_noise.parquet
  - split: noise
    path: data/noise.parquet
  - split: obstructed_noise
    path: data/obstructed_noise.parquet
  - split: recording_noise
    path: data/recording_noise.parquet
---

# Mega-ASR Conversational Overlap

Mega-ASR Conversational Overlap is a deterministic English ASR diagnostic set
derived from [`AirCaps/mega-asr-noise-a5sv2`](https://huggingface.co/datasets/AirCaps/mega-asr-noise-a5sv2),
which in turn is sampled from the Mega-ASR training corpus
[`zhifeixie/Voices-in-the-Wild-2M`](https://huggingface.co/datasets/zhifeixie/Voices-in-the-Wild-2M).

The existing AirCaps dataset evaluates **single-utterance acoustic robustness**.
This companion dataset evaluates a different failure mode: **two-turn conversational
continuity with slight overlap and unequal turn loudness**. It does not replace the
single-utterance set, and results should identify which benchmark was used.

We used Mega-ASR-Train-derived material because, in our experiments, the standard
Mega-ASR test set was not acoustically challenging enough to clearly distinguish
robust ASR systems. This is a fixed diagnostic suite and should not be mixed into
training when reporting results on it.

## Contents

The dataset contains **625 conversations / 3.8351 hours**. Each split contains
125 conversations made by pairing all 250 utterances from the corresponding
single-utterance condition. Every source utterance is used exactly once within
its condition; there are no repeats.

| Split | Conversations | Hours |
|---|---:|---:|
| `far_field` | 125 | 0.6938 |
| `far_field_noise` | 125 | 1.0413 |
| `noise` | 125 | 0.7703 |
| `obstructed_noise` | 125 | 0.6831 |
| `recording_noise` | 125 | 0.6467 |

All output audio is mono 16 kHz PCM16 WAV embedded in Parquet.

## Construction

- Pairing occurs only within the same acoustic condition.
- Each conversation contains two distinct source utterances in A-then-B order.
- Turn gains are sampled independently from Uniform[-8, 0) dB.
- Leading and trailing padding are independently sampled from 250 to 1,000 ms.
- All conversations contain slight overlap:
  - 42 per split at 50-100 ms;
  - 42 per split at >100-200 ms;
  - 41 per split at >200-300 ms.
- No new noise is added; noise/reverberation/obstruction is inherited from the source condition.
- Scene-wide attenuation is applied only when required to remain below -1 dBFS.
- The reference is the original Mega-ASR text for turn A followed by the original
  Mega-ASR text for turn B. These are not silver labels.

The source manifests do not expose reliable speaker identities. Therefore each
scene is guaranteed to use two distinct utterances, but is not guaranteed to use
two distinct speakers.

## Saved predictions and benchmark results

Raw predictions are provided for matched error analysis; they are **not labels**.
All WER values below use corpus-level aggregation and the English normalizer
documented in `results/benchmark_summary.json`.

| System | Overall WER |
|---|---:|
| A5S | **21.02%** |
| ElevenLabs Scribe v2 Realtime | 23.52% |
| Kyutai STT 1B EN/FR | 57.41% |

| Condition | N | A5S | ElevenLabs | Kyutai |
|---|---:|---:|---:|---:|
| `far_field` | 125 | 6.06% | 5.51% | 20.70% |
| `far_field_noise` | 125 | 32.61% | 33.74% | 86.24% |
| `noise` | 125 | 22.14% | 28.68% | 54.46% |
| `obstructed_noise` | 125 | 24.97% | 29.53% | 66.22% |
| `recording_noise` | 125 | 9.53% | 10.14% | 37.54% |

A5S used cache-aware greedy streaming at exactly 560 ms, BF16, and batch size 1.
ElevenLabs used Scribe v2 Realtime with forced English, one independent WebSocket
session per conversation, mono PCM16 at 16 kHz, and 100 ms chunks paced in
wall-clock real time. Kyutai used the official causal Mimi-to-LMGen transcript
path with 80 ms audio frames, a configured 0.5 s text delay, BF16, temperature 0,
batch size 1, and fresh streaming state for every conversation. The pinned
PyTorch checkpoint exposes no semantic-VAD extra heads, so no transcript-gating
VAD was active. Kyutai returned a blank transcript for 24.48% of conversations;
these count as deletions in WER.

Each row contains raw predictions and utterance-level S/D/I counts for all three
systems. Aggregate results live under `results/`.

## Loading

```python
from datasets import load_dataset

dataset = load_dataset("AirCaps/mega-asr-conversational-overlap")
example = dataset["noise"][0]
print(example["text"])
print(example["a5s_prediction"])
print(example["kyutai_prediction"])
```

Recent versions of `datasets` may require `torchcodec` for decoded audio. Use
`cast_column("audio", Audio(decode=False))` if only the encoded bytes and metadata
are needed.

## Provenance and limitations

- Immediate source: `AirCaps/mega-asr-noise-a5sv2`
- Upstream source: `zhifeixie/Voices-in-the-Wild-2M`
- Pinned upstream revision: `a8a35d3319737190d6fd3d39157b258eaab35980`
- Construction date: 2026-08-24
- Source usage: 1,250 / 1,250, with zero within-condition repetitions
- Final audit: 625 decoded WAVs, 1,875 matched predictions, zero missing or corrupt rows

Because this is derived from a training corpus rather than a speaker-disjoint
held-out benchmark, it should be treated as a fixed diagnostic suite. Do not
train on it and then report evaluation results on the same rows.

## License and attribution

The upstream dataset is released under Apache-2.0. Users should review both the
upstream dataset card and the Mega-ASR paper for source provenance and limitations.

```bibtex
@misc{xie2026megaasrinthewild2speechrecognition,
  title={Mega-ASR: Towards In-the-wild^2 Speech Recognition via Scaling up Real-world Acoustic Simulation},
  author={Zhifei Xie and Kaiyu Pang and Haobin Zhang and Deheng Ye and Xiaobin Hu and Shuicheng Yan and Chunyan Miao},
  year={2026},
  eprint={2605.19833},
  archivePrefix={arXiv},
  primaryClass={cs.SD},
  url={https://arxiv.org/abs/2605.19833}
}
```