README.md

---
language:
- en
task_categories:
- audio-to-audio
- text-to-speech
tags:
- speech-to-speech
- spoken-dialogue
- speech-language-model
- webdataset
- distillation
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.tar
  - split: validation
    path: data/validation-*.tar
---
# FlexiSLM-Data — Speech-to-Speech Part (2.43M filtered samples, 385G in size)

[![Paper](https://img.shields.io/badge/arXiv-2606.31247-b31b1b.svg)](https://arxiv.org/abs/2606.31247)
[![Demo](https://img.shields.io/badge/Demo-flexislm.github.io-blue.svg)](https://flexislm.github.io/)
[![Code](https://img.shields.io/badge/Code-AmphionTeam%2FFlexiSLM-181717.svg?logo=github)](https://github.com/AmphionTeam/FlexiSLM)

- **Paper:** https://arxiv.org/abs/2606.31247
- **Demo page:** https://flexislm.github.io/
- **Code:** https://github.com/AmphionTeam/FlexiSLM

**FlexiSLM-Data** is a large-scale, single-turn English speech-to-speech dialogue dataset
for training [FlexiSLM](https://github.com/AmphionTeam/FlexiSLM), a spoken language model.
This repository contains the paired prompt-and-response audio portion of the release in
WebDataset format.

**Related data releases**

- [**FlexiSLM/FlexiSLM-Data-4M-s2s**](https://huggingface.co/datasets/FlexiSLM/FlexiSLM-Data-4M-s2s) is the larger speech-to-speech release with prompt deduplication and WER-based filtering. Its question audios are stored in 44k wav, while response audios are stored in mp3. Size is around 2.8T.
- [**FlexiSLM/FlexiSLM-Data-2M-s2s-compact**](https://huggingface.co/datasets/FlexiSLM/FlexiSLM-Data-2M-s2s-compact) (this repo) is the filtered, mp3-compressed subset of FlexiSLM/FlexiSLM-Data-4M-s2s that **takes only 385G storage** while maintaining high data quality. 
- [**FlexiSLM/FlexiSLM-Data-5M-t2t**](https://huggingface.co/datasets/FlexiSLM/FlexiSLM-Data-5M-t2t) provides the raw text input-output pairs without audio.

## Data construction pipeline

1. **Prompt collection and response generation** (released in [**FlexiSLM/FlexiSLM-Data-5M-t2t**](https://huggingface.co/datasets/FlexiSLM/FlexiSLM-Data-5M-t2t))
Text prompts are collected from public QA,
   instruction-following, and dialogue datasets (see the table below). For multi-turn
   datasets, the user's first-turn utterance is used as the prompt; samples whose first
   turn is a generic greeting (e.g. "Hello") are skipped.
Then, all text responses are generated with
   [**Qwen3-Omni-30B-A3B**](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct);
   the responses shipped with the source datasets are discarded. We use a spoken language
   model rather than a text-only LLM because Qwen3-Omni produces speech-friendly
   responses: short, conversational, and free of formatting that does not transfer to
   speech (bullet points, code blocks, long enumerations). The *text* version of the
   prompt is fed to the model rather than its synthesized speech, since text-input
   responses are typically more accurate.
2. **Speech synthesis.** (released in [**FlexiSLM/FlexiSLM-Data-4M-s2s**](https://huggingface.co/datasets/FlexiSLM/FlexiSLM-Data-4M-s2s)) Responses are synthesized with **Qwen3-TTS** using the fixed
   speaker "Ryan". Prompts are synthesized with
   [**Fish-Audio TTS**](https://huggingface.co/fishaudio/s1-mini), with speaker prompts
   randomly sampled from English Emilia utterances longer than 5 seconds. This yields the
   4,222,459 samples and 26,735 hours of audio shipped here.
3. **Quality filtering and mp3-format compression**. (released in [**FlexiSLM/FlexiSLM-Data-2M-s2s-compact**](https://huggingface.co/datasets/FlexiSLM/FlexiSLM-Data-2M-s2s-compact)) Before any shards are written, this release applies prompt
  deduplication and response-text filtering. The kept subset uses normalized prompt
  deduplication first, then drops responses that contain `!`, contain `?`, look
  non-English, or look like code. From 4,222,459 raw rows, 1,790,681 are filtered out
  and 2,431,778 remain. The punctuation-based filtering is empirical: we find that questions with more knowledge density are responded more formally, while casual questions tend to be responded with `!` or `?`.

## Prompt sources

Only the **user prompts** come from these datasets; their original answers are not used.

| Dataset | Type | # Prompts |
|---|---|---|
| [TriviaQA](https://huggingface.co/datasets/mandarjoshi/trivia_qa) | QA | 138K |
| [WebQuestions](https://huggingface.co/datasets/stanfordnlp/web_questions) | QA | 3.8K |
| [TyDiQA](https://huggingface.co/datasets/SEACrowd/tydiqa) | Multilingual QA | 167K |
| [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) | Instruction | 52K |
| [SmolTalk2](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2) | Instruction / Dialogue | 385K |
| [SODA](https://huggingface.co/datasets/allenai/soda) | Dialogue | 1.48M |
| [Magpie-Pro](https://huggingface.co/datasets/Magpie-Align/Llama-3-Magpie-Pro-1M-v0.1) | Instruction / Dialogue | 1M |
| [UltraChat](https://huggingface.co/datasets/openbmb/UltraChat) | Multi-turn Dialogue | 949K |
| [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf) | Dialogue / Preference | 167K |
| [WildChat](https://huggingface.co/datasets/allenai/WildChat) | Real-user Dialogue | 159K |

## Statistics of this data

| | value |
|---|---|
| Samples (train) | 2,425,778 across 243 shards |
| Samples (validation) | 6,000 across 1 shard |
| Samples (total) | 2,431,778 |
| Filtered out | 1,790,681 of 4,222,459 raw rows |
| Prompt dedup | 91,869 duplicate prompts removed |
| Punctuation filter | 1,375,207 removed (`!` / `?`) |
| Non-English filter | 116,412 removed |
| Code filter | 207,193 removed |
| Prompt audio | 4,088.9 hours (14,720,133.735 s) |
| Response audio | 10,728.2 hours (38,621,498.771 s) |
| Total audio | 14,817.1 hours (53,341,632.505 s) |
| Mean prompt duration | 6.05 s |
| Mean response duration | 15.88 s |

## Layout

Each sample is three tar members sharing an eight-digit key, unique across every
shard and both splits:

```
00000001.question.mp3   # user prompt, transcoded from WAV during packing
00000001.response.mp3   # assistant reply, Qwen3-TTS speaker "Ryan"
00000001.json           # transcripts and per-sample metadata
```

- `train`: 2425778 samples across 243 shards (`data/train-{00000..00242}-of-00243.tar`)
- `validation`: 6000 samples across 1 shard (`data/validation-{00000..00000}-of-00001.tar`)

Exact per-shard sample counts and byte offsets live in `shards.json`;
per-sample records live in `manifest.jsonl`.

Each metadata record carries:

| field | description |
|---|---|
| `key` | Eight-digit WebDataset key |
| `uuid` | Stable identifier, shared with the t2t release |
| `split` | `train` or `validation` |
| `shard` | Shard the sample lives in |
| `question_text` / `response_text` | Transcripts of the two audio members |
| `question_duration` / `response_duration` | Seconds |
| `total_audio_duration` | Seconds |
| `num_tokens_est` | Estimated audio tokens at 12 tokens/s |

## Loading

```python
from datasets import load_dataset
dataset = load_dataset("FlexiSLM/FlexiSLM-Data-2M-s2s-compact", split="train", streaming=True)
```

Or straight from WebDataset, using the brace pattern recorded in `shards.json`:

```python
import webdataset as wds
url = "https://huggingface.co/datasets/FlexiSLM/FlexiSLM-Data-2M-s2s-compact/resolve/main/data/train-{00000..00099}-of-01406.tar"
dataset = wds.WebDataset(f"pipe:curl -sL {url}")
```

## Filtering

Filtering is applied before any tar shards are written. The packer first performs
prompt deduplication, then keeps only responses that pass all of the following checks:

- response does not contain `!`
- response does not contain `?`
- response does not look non-English
- response does not look like code

The punctuation-based filtering is empirical: we find that questions with more knowledge density are responded more formally, while casual questions tend to be responded with `!` or `?`. Higher knowledge density data aligns with knwoledge benchmarks like llama-questions better.


The filtered release was built from 4,222,459 raw rows. The exact drop counts are:

| reason | dropped |
|---|---:|
| duplicate prompts | 91,869 |
| contains `!` | 966,181 |
| contains `?` | 409,026 |
| looks non-English | 116,412 |
| looks like code | 207,193 |

Total removed: 1,790,681 rows.

## Limitations

- **Single-turn only.** Multi-turn sources are truncated to their first user turn.
- **Filtered, but not perfect.** The release removes the most obvious punctuation,
  non-English, and code-like responses, but some imperfect samples may still remain.
- **Synthetic speech.** All audio is TTS-generated, with a single fixed response voice;
  it does not reflect real conversational acoustics, noise, or speaker diversity on the
  response side.
- **Model-generated text.** Every response comes from Qwen3-Omni-30B-A3B and inherits its
  biases and factual errors; none are human-verified.
- **Derived data.** Prompts inherit the licenses and terms of their source datasets;
  please check each source before redistribution.

## References

- Qwen3-Omni — Xu et al., 2025
- Qwen3-TTS — Hu et al., 2026
- Fish-Audio TTS — Liao et al., 2024
- Emilia — He et al., 2024
- MLS — Pratap et al., 2020
- LibriSpeech — Panayotov et al., 2015
- LLaSO-Instruct — Sun et al., 2025
- TriviaQA — Joshi et al., 2017
- WebQuestions — Berant et al., 2013
- TyDiQA — Clark et al., 2020
- Alpaca — Taori et al., 2023
- SODA — Kim et al., 2023
- Magpie — Xu et al., 2025
- UltraChat — Ding et al., 2023
- HH-RLHF — Bai et al., 2022
- WildChat — Zhao et al., 2024