README.md

metadata

dataset_info:
  features:
    - name: file_name
      dtype: string
    - name: transcript
      dtype: string
    - name: labels
      dtype: string
  splits:
    - name: train_val
      num_examples: 10802
    - name: test
      num_examples: 2000
configs:
  - config_name: default
    data_files:
      - split: train_val
        path: train_val.parquet
      - split: test
        path: test.parquet
task_categories:
  - automatic-speech-recognition
  - text-classification
  - token-classification
language:
  - vi
license: apache-2.0
extra_gated_prompt: >
  ### ViToSA 2.0 Dataset Access Agreement
     
  The ViToSA 2.0 dataset contains LLM-augmented Vietnamese speech recordings and
  transcripts with annotated toxic spans, created by researchers at the
  University of Information Technology, VNU-HCM.

  ⚠️ **Disclaimer:** This dataset includes explicit and offensive language. Use
  responsibly for research on content moderation and language modeling.
   #### Terms of Use
  - Use of the Dataset is permitted only for **non-commercial research and
  educational purposes**.  

  - Redistribution of raw data (audio, transcripts, or annotations) is **not
  allowed**.  

  - You must properly cite the original paper. 

  - Do not attempt to identify individuals or use the Dataset to harass,
  discriminate, or harm.  

  - The Dataset is provided *“as is”* without warranty.  

  - Access may be revoked if these terms are violated, and you must delete all
  copies.
   By requesting access, you confirm that you have read and agreed to these terms.
extra_gated_fields:
  Full Name: text
  Affiliation: text
  Email: text
  Intended use: text
  Country: country
  By clicking 'Request Access', I confirm that I have read, understood, and accepted the ViToSA Data Access Agreement: checkbox
extra_gated_description: >-
  The information you provide will be used solely to evaluate dataset access
  requests and will not be shared beyond the ViToSA maintainers.
extra_gated_button_content: Request Access
size_categories:
  - 10K<n<100K

ViToSA 2.0: A Multi-Task Approach Towards Robust Vietnamese Audio-Based Toxic Span Detection

This is the official repository for the ViToSA 2.0 dataset and model framework, introduced in the paper A Multi-Task Approach Towards Robust Vietnamese Audio-Based Toxic Span Detection, accepted at ICASSP 2026.
The dataset and multi-task framework were developed by researchers from the University of Information Technology, VNU-HCM.

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Citation Information

If you use this dataset or framework, please cite the following paper:

@INPROCEEDINGS{11460540,
  author={Huynh, Vy Le-Phuong and Do, Huy Ba and Nguyen, Luan Thanh},
  booktitle={ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  title={A Multi-Task Approach Towards Robust Vietnamese Audio-Based Toxic Span Detection},
  year={2026},
  volume={},
  number={},
  pages={18772-18776},
  keywords={Low earth orbit satellites;Feeds;Digital audio broadcasting;Broadcasting;Central Processing Unit;Electronic circuits;Radio access networks;Regional area networks;Protocols;HTTP;audio-based toxic span detection;multi-task learning;knowledge distillation;Vietnamese speech processing;content moderation},
  doi={10.1109/ICASSP55912.2026.11460540}
}

---

Abstract

Detecting toxic spans in spoken content is crucial for content moderation, but conventional two-stage pipelines that run Automatic Speech Recognition (ASR) followed by a Toxic Span Detection (TSD) model suffer from high latency and system complexity. To address these limitations, we propose a multi-task framework for Vietnamese audio-based toxic span detection that unifies ASR and TSD modules within a single model. Our approach utilizes a PhoWhisper encoder-decoder backbone and attaches a token classification head, enhanced with a BiLSTM and a Conditional Random Field (CRF) layer, directly to the decoder’s hidden states. To improve the model’s semantic understanding, we employ a knowledge distillation strategy to transfer rich textual knowledge from a pre-trained PhoBERT teacher model to our speech-based student model. Trained on a dataset augmented by a high-quality LLM-as-annotator-based pipeline, our model achieves a state-of-the-art F1-macro score of 0.9212. Furthermore, it significantly reduces inference latency by over 56% on GPU compared to the traditional two-stage approach, demonstrating a more efficient and robust solution for real-world deployment.

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Data Structure

The dataset is distributed across two main Parquet files:

Split	File Name	File Size	# Examples
Train & Validation	train_val.parquet	~ 982 kB	[TO BE UPDATED]
Test	test.parquet	~ 91.7 kB	[TO BE UPDATED]
Total		~ 1.08 MB	[TO BE UPDATED]

Note: The train and validation sets are combined into a single train_val.parquet file.

• Each entry consists of:
  • file_name (string): The name of the audio file (e.g., audio1.wav).
  • transcript (string): The speech transcription in Vietnamese.
  • labels (string): A string representation of a list of binary labels indicating toxic spans at the token/word level (e.g., "[0, 1, 1, 0, ...]").

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🔊 Example Usage: Load and Evaluate

Here’s a minimal example showing how to load the dataset from Hugging Face using the datasets library and parse the token-level labels:

import ast
import io
import numpy as np
import soundfile as sf
from datasets import load_dataset
from IPython.display import Audio, display

# 1. Load both dataset versions
ds_v1 = load_dataset("UIT-ViToSA/ViToSA-1.0")
ds_v2 = load_dataset("UIT-ViToSA/ViToSA-2.0")

# 2. Pick a sample from ViToSA 2.0 (test split)
v2_sample = ds_v2["test"][0]
target_filename = v2_sample["file_name"]
transcript = v2_sample["transcript"]
parsed_labels = ast.literal_eval(v2_sample["labels"])

print(f"Target Audio: {target_filename}")
print(f"Transcript:   {transcript}")
print(f"V2 Labels:    {parsed_labels}")

# 3. Lookup the corresponding audio in ViToSA 1.0 test split
df_v1_test = ds_v1["test"].to_pandas()
matched_row = df_v1_test[df_v1_test["file_name"] == target_filename].iloc[0]

# 4. Decode and listen to the audio
audio_bytes = matched_row["audio"]["bytes"]
data, sr = sf.read(io.BytesIO(audio_bytes))
if data.ndim > 1:
    data = data.mean(axis=1)
data = np.clip(data, -1, 1).astype(np.float32)

display(Audio(data, rate=sr))

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Dataset Access Agreement

⚠️ Disclaimer: This dataset contains explicit and offensive language.
Use responsibly for research on content moderation and language modeling. Access is restricted to non-commercial research and educational use under the following terms:

• Redistribution of raw data is not allowed
• Proper citation is required (see above)
• Any violation of the terms may result in access revocation Full agreement text is provided in the gated access form.

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License

This dataset is released under the Apache 2.0 License.

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Repository

GitHub: https://github.com/vitosa-research/ViToSA-Dataset

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Contact

Please feel free to contact us by email luannt@uit.edu.vn if you have any further information!