--- 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](https://huggingface.co/datasets/linhtran92/viet_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: ```python 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 ```bash pip install datasets soundfile jiwer transformers torch tqdm numpy ``` ### Load the dataset ```python 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 ```python 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 ```python 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 ```bash 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: ```json { "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: ```bibtex @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**.