Create README.md

README.md

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+---
+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**.