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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - automatic-speech-recognition
+  - text-generation
+  - text-to-speech
+language:
+  - en
+tags:
+  - pavo
+  - benchmark
+  - asr
+  - llm
+  - tts
+  - pipeline-routing
+  - voice-assistant
+  - latency
+  - quality
+  - cost
+  - energy
+pretty_name: PAVO-Bench
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: tier1_statistical
+        path: tier1_statistical_results.json
+      - split: tier1_coupling
+        path: tier1_coupling_results.json
+      - split: tier1_llm_latency
+        path: tier1_llm_latency_results.json
+      - split: tier2_e2e
+        path: tier2_e2e_results.json
+      - split: tier2_cross_dataset
+        path: tier2_cross_dataset_results.json
+      - split: tier2_noise_robustness
+        path: tier2_noise_robustness_results.json
+      - split: tier3_50k_summary
+        path: tier3_50k_summary.json
+      - split: tier3_scaling
+        path: tier3_scaling_results.json
+      - split: component_ablation
+        path: component_ablation_results.json
+---
+
+# PAVO-Bench: 50K-Turn Benchmark for ASR-LLM-TTS Pipeline Routing
+
+**Author:** NarasingaMoorthy VeiluKanthaPerumal, University of Pennsylvania
+
+## Description
+
+PAVO-Bench is a comprehensive benchmark suite for evaluating **ASR-LLM-TTS voice pipeline routing** decisions. It provides 50,000 turns of benchmark data designed to measure how well different pipeline configurations balance **latency**, **quality**, **cost**, and **energy consumption** when routing spoken-language queries through cascaded Automatic Speech Recognition (ASR), Large Language Model (LLM), and Text-to-Speech (TTS) components.
+
+The benchmark is organized into three tiers of increasing scale and complexity, plus component-level ablation studies. All results were produced on GPU hardware.
+
+## Dataset Files
+
+### Tier 1 -- Unit-Level Validation
+
+| File | Description |
+|------|-------------|
+| `tier1_statistical_results.json` | Statistical reproducibility results across 5 trials of 1,000 turns each (seeds 42, 123, 456, 789, 1024). Reports mean, std, and 95% confidence intervals for PAVO latency, quality, cost, and energy metrics. |
+| `tier1_coupling_results.json` | Coupling constraint validation measuring LLM quality degradation as a function of ASR word-error rate (WER 0--20%) using llama3.1:8b. |
+| `tier1_llm_latency_results.json` | LLM latency profiling for llama3.1:8b across short (50 token), medium (200 token), and long (500 token) generation contexts. Reports total latency, time-to-first-token, and tokens/second. |
+
+### Tier 2 -- Integration-Level Evaluation
+
+| File | Description |
+|------|-------------|
+| `tier2_e2e_results.json` | End-to-end pipeline measurements for cloud_premium (whisper-large-v3 + llama3.1:8b) and edge_fast (whisper-tiny + gemma2:2b) configurations on 200 LibriSpeech samples. Includes per-stage latency breakdowns, sample ASR outputs, and sample LLM responses. |
+| `tier2_cross_dataset_results.json` | Cross-dataset ASR evaluation on LibriSpeech and FLEURS for whisper-large-v3 and whisper-tiny models (200 samples each). Reports WER and latency statistics. |
+| `tier2_noise_robustness_results.json` | ASR robustness under white noise at SNR levels 5--30 dB, plus clean baseline. Reports WER degradation across noise conditions. |
+
+### Tier 3 -- Scale Evaluation
+
+| File | Description |
+|------|-------------|
+| `tier3_50k_summary.json` | Summary statistics for the full 50,000-turn PAVO-Bench dataset: 40K train / 10K test split, complexity distribution (levels 1--5), generation time, and error rate. |
+| `tier3_scaling_results.json` | LLM scaling benchmarks across multiple models (gemma2:2b, llama3.1:8b, etc.) with simple, medium, and complex query types. Reports latency, throughput, and real-time suitability. |
+
+### Component Analysis
+
+| File | Description |
+|------|-------------|
+| `component_ablation_results.json` | Ablation study comparing PAVO-Full, PAVO-NoCoupling, and other ablated configurations. Reports latency, quality, cost, energy, coupling violations, and infeasible percentages. |
+
+## Usage
+
+### Load individual JSON files directly
+
+```python
+import json
+from huggingface_hub import hf_hub_download
+
+# Download a specific results file
+path = hf_hub_download(
+    repo_id="<your-username>/pavo-bench",
+    filename="tier3_50k_summary.json",
+    repo_type="dataset",
+)
+
+with open(path) as f:
+    data = json.load(f)
+
+print(f"Total samples: {data['total_samples']}")
+print(f"Train/Test split: {data['train_samples']}/{data['test_samples']}")
+```
+
+### Download all files
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="<your-username>/pavo-bench",
+    repo_type="dataset",
+    local_dir="./pavo-bench-data",
+)
+```
+
+## Benchmark Metrics
+
+- **Latency** (ms): End-to-end and per-component response time
+- **Quality** (0--1): Composite score incorporating ASR accuracy and LLM response quality
+- **Cost** (USD): Per-turn inference cost
+- **Energy** (mJ): Per-turn energy consumption
+- **Coupling violations**: Cases where ASR errors propagate and degrade LLM quality
+
+## Citation
+
+If you use PAVO-Bench in your research, please cite:
+
+```bibtex
+@misc{pavo-bench-2026,
+  author = {VeiluKanthaPerumal, NarasingaMoorthy},
+  title = {PAVO-Bench: A 50K-Turn Benchmark for ASR-LLM-TTS Pipeline Routing},
+  year = {2026},
+  institution = {University of Pennsylvania},
+  url = {https://huggingface.co/datasets/<your-username>/pavo-bench}
+}
+```
+
+## License
+
+This dataset is released under the [Creative Commons Attribution 4.0 International (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.