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anhndbk
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ViASRQuantizationBench

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Languages:
Vietnamese
Size:
n<1K
Tags:
vietnamese
benchmark
quantization
asr
wer
cer
License:
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metadata 
language:
  - vi
license: cc-by-nc-sa-4.0
multilinguality: monolingual
size_categories:
  - n<1K
source_datasets:
  - linhtran92/viet_bud500
task_categories:
  - automatic-speech-recognition
tags:
  - vietnamese
  - benchmark
  - quantization
  - asr
  - wer
  - cer
  - calibration
  - evaluation
  - audio
  - speech
pretty_name: ViASR-Bench
dataset_info:
  features:
    - name: id
      dtype: string
    - name: transcript
      dtype: string
    - name: duration_s
      dtype: float32
    - name: sampling_rate
      dtype: int32
    - name: source_split
      dtype: string
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
  splits:
    - name: calibration
      num_examples: 256
    - name: evaluation
      num_examples: 600
  download_size: 73400320
  dataset_size: 73400320

ViASR-Bench 🇻🇳🎙️

Vietnamese benchmark dataset for ASR model quantization — providing both a calibration set and an evaluation set.

ViASR-Bench is a curated sample from BUD500, the largest public Vietnamese podcast speech dataset. It is designed specifically for the two-phase lifecycle of ASR quantization:

Phase Split Purpose
Calibration calibration (256 utterances) Feed to AWQ / GPTQ / SmoothQuant to compute activation statistics and compensation factors
Evaluation evaluation (600 utterances) Measure WER / CER degradation after quantization

Important: This dataset is a subsample of BUD500, not a standalone corpus. Audio content is sourced from BUD500 (CC-BY-NC-SA 4.0) and is intended for non-commercial research use only.

Dataset Summary

Split Utterances Duration Size (~) Source in BUD500
calibration 256 ~10.9 min ~21 MB train split
evaluation 600 ~25.6 min ~49 MB test split
Total 856 ~36.5 min ~70 MB

Audio format: 16 kHz · Mono · PCM-16 (WAV)

Utterance duration statistics:

Split Mean (s) Median (s) P10 (s) P90 (s) Max (s)
calibration 2.56 2.12 1.10 4.48 19.2
evaluation 2.56 2.11 1.09 4.52 19.8

Motivation

Why a Vietnamese-specific ASR quantization benchmark?

Existing quantization tools (AWQ, GPTQ, SmoothQuant) require a calibration dataset whose acoustic distribution matches the target language. Using English speech data to calibrate a Vietnamese ASR model leads to suboptimal quantization because:

  1. 6-tone tonal system. Vietnamese has six tones (ngang, huyền, sắc, hỏi, ngã, nặng) that create unique patterns in mel-spectrograms and encoder feature spaces, distinct from non-tonal languages.

  2. Regional dialect variation. Three major dialects (Northern, Central, Southern) differ significantly in formant distributions and prosody, affecting attention and convolution layers in ASR encoders.

  3. Monosyllabic structure. Vietnamese is predominantly monosyllabic, producing different sequence-length and attention-pattern distributions than English.

Calibrating on English audio leaves these activation patterns unrepresented, degrading quantization quality specifically for Vietnamese phonemes.

Why CER over WER for Vietnamese?

Vietnamese words average 1.7–2.2 characters, making tone confusion errors (e.g., khoẻ → khoề: a single diacritic change) inflate WER disproportionately:

Reference:  "bạn có khoẻ không"   (4 words, 15 chars)
Hypothesis: "bạn có khoề không"

WER = 1/4 = 25.0%   ← one wrong word
CER = 1/15 = 6.7%   ← one wrong character

CER is a more sensitive and fair metric for detecting tone-level degradation caused by quantization on Vietnamese. Both WER and CER are reported.

Source Data: BUD500

BUD500 is the largest public Vietnamese speech dataset (~500 hours of podcast audio with manually verified transcripts, covering diverse topics and all three major dialects).

Property Value
Total duration ~500 hours
Utterances ~150,000
Sampling rate 16,000 Hz
Content type Podcast (multi-topic)
Dialect coverage Northern, Central, Southern
HuggingFace ID linhtran92/viet_bud500
License CC-BY-NC-SA 4.0

Why BUD500 over alternatives?

Dataset Hours Quality Diversity Public
BUD500 ~500h High High ✅
VIVOS 15h High Low (read speech) ✅
FLEURS vi ~10h Medium Medium ✅
CommonVoice vi ~30h Medium Medium ✅
VLSP 100h+ High High ❌

BUD500's size ensures that random sampling still yields diverse coverage. Its podcast format is closer to real-world deployment conditions than studio read-speech datasets like VIVOS.

Sampling Methodology

Non-overlapping splits

Calibration and evaluation sets are guaranteed non-overlapping by transcript:

eval_transcripts = {s["transcript"] for s in eval_samples}

# Calibration: skip any utterance whose transcript appears in eval
if transcript in eval_transcripts:
    continue

This prevents data leakage between calibration and evaluation.

Quality filter

Each utterance passes a 2-condition filter:

keep(u) = True  iff:
  1.0s ≤ duration(u) ≤ 20.0s     # remove noise clips and unusually long segments
  AND  len(transcript(u)) > 0     # non-empty transcript

Sampling procedure 

1. Stream BUD500 (streaming=True) — no full download required
2. Collect buffer = 5× target size from filtered utterances
3. random.sample(buffer, n_target, seed=42)  — fixed seed for reproducibility

Calibration is drawn from BUD500 train split (seed=43). Evaluation is drawn from BUD500 test split (seed=42).

Transcript normalization

All transcripts are stored in Unicode NFC normalized form. Before computing WER/CER, apply the same normalization to model hypotheses:

import unicodedata, re

def normalise_vi(text: str) -> str:
    text = unicodedata.normalize("NFC", text)   # critical for Vietnamese diacritics
    text = text.lower()
    text = re.sub(r"[^\w\s]", " ", text, flags=re.UNICODE)
    text = re.sub(r"\s+", " ", text).strip()
    return text

NFC normalization is critical because Vietnamese characters can be encoded two ways:

  • Decomposed: e + combining hook above + combining dot below
  • Precomposed: single codepoint ệ

Without normalization, visually identical strings may have non-zero edit distance.

Usage

Installation 

pip install datasets soundfile jiwer transformers torch tqdm numpy

Load the dataset 

from datasets import load_dataset

# Evaluation split
eval_ds = load_dataset("your-org/vi-asr-bench", split="evaluation")

# Calibration split
calib_ds = load_dataset("your-org/vi-asr-bench", split="calibration")

# Each sample:
# {
#   "id":            "sample_0042",
#   "audio":         {"array": np.ndarray, "sampling_rate": 16000},
#   "transcript":    "xin chào thế giới",
#   "duration_s":    3.21,
#   "source_split":  "test"
# }

AWQ / GPTQ Calibration 

import torch
from datasets import load_dataset

calib_ds = load_dataset("your-org/vi-asr-bench", split="calibration")

# Extract audio arrays for the encoder
audio_inputs = [
    torch.tensor(sample["audio"]["array"]).unsqueeze(0)   # (1, T)
    for sample in calib_ds
]

# Feed into your quantization calibration loop:
# for audio in audio_inputs:
#     model.encoder(audio)   # collect activation statistics

WER / CER Evaluation 

import unicodedata, re
from datasets import load_dataset
from jiwer import wer, cer
from transformers import pipeline

def normalise_vi(text):
    text = unicodedata.normalize("NFC", text)
    text = text.lower()
    text = re.sub(r"[^\w\s]", " ", text, flags=re.UNICODE)
    return re.sub(r"\s+", " ", text).strip()

eval_ds  = load_dataset("your-org/vi-asr-bench", split="evaluation")
asr_pipe = pipeline("automatic-speech-recognition",
                    model="your-quantized-model",
                    generate_kwargs={"language": "vi", "task": "transcribe"})

references  = [normalise_vi(s["transcript"]) for s in eval_ds]
hypotheses  = [normalise_vi(r["text"]) for r in asr_pipe(
                   [s["audio"] for s in eval_ds], batch_size=8)]

print(f"WER: {wer(references, hypotheses)*100:.2f}%")
print(f"CER: {cer(references, hypotheses)*100:.2f}%")

Compare quantized vs. baseline 

python vi_asr_eval.py \
    --model   openai/whisper-large-v3 \
    --compare your-org/whisper-large-v3-int4 \
    --output  results.json

Expected output format:

================================================================
Metric        openai/whisper-large-v3   your-org/...-int4    Delta
----------------------------------------------------------------
WER                          8.24%              8.91%       +0.67%
CER                          3.11%              3.38%       +0.27%

Reproducibility

Full metadata is stored in metadata.json for each split:

{
  "split": "evaluation",
  "n_samples": 600,
  "seed": 42,
  "source": "linhtran92/viet_bud500",
  "source_split": "test",
  "sampling_rate": 16000,
  "total_duration_s": 1536.0,
  "total_duration_min": 25.6,
  "total_size_mb": 49.1,
  "filter": {"min_dur_s": 1.0, "max_dur_s": 20.0},
  "normalization": "unicode_nfc"
}

Relation to ViWiki-Bench

ViASR-Bench is the speech counterpart of ViWiki-Bench (Vietnamese LLM benchmark). Together they form a complete Vietnamese quantization benchmark suite:

ViWiki-Bench (LLM) ViASR-Bench (ASR)
Input modality Text (token IDs) Audio (float32 arrays)
Calibration 128 sequences from Wikipedia vi 256 utterances from BUD500 train
Evaluation ~280k tokens from Wikipedia vi 600 utterances from BUD500 test
Metric Perplexity (PPL) WER / CER
Source dataset wikimedia/wikipedia 20231101.vi linhtran92/viet_bud500

Limitations

  • Single source: Only BUD500 (podcast). Read speech (VIVOS), spontaneous conversation, and children's speech are not represented.
  • No dialect labels: BUD500 does not tag individual utterances with Northern/Central/Southern dialect, so stratified sampling by dialect is not possible.
  • Small evaluation set: 600 utterances (~26 min) is sufficient for relative comparison between quantization methods, but not for high-precision absolute WER estimation (vs. LibriSpeech test-clean at 5.4 hours).
  • Non-commercial license: Inherited from BUD500 (CC-BY-NC-SA 4.0). Not suitable for commercial product deployment.

Citation

If you use ViASR-Bench in your research, please cite both this dataset and BUD500:

@techreport{viasr_bench2024,
  title     = {ViASR-Bench: A Vietnamese Benchmark Dataset for
               ASR Model Quantization Calibration and Evaluation},
  author    = {AnhND},
  year      = {2026},
  note      = {Technical Report v1.0. Sampled from linhtran92/viet\_bud500},
  url       = {https://huggingface.co/datasets/your-org/vi-asr-bench}
}

@dataset{bud500,
  title     = {BUD500: A Large-Scale Vietnamese Podcast Speech Dataset},
  author    = {Linh Tran},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/linhtran92/viet_bud500}
}

License

This dataset inherits the license of its source: CC-BY-NC-SA 4.0 (Non-Commercial).

The sampling and evaluation code is released under MIT License.