Datasets:

vnmoorthy
/

pavo-bench

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

Code: github.com/vnmoorthy/pavo-bench · Paper: TMLR 2026 (under review) · Authors: NarasingaMoorthy VeiluKanthaPerumal (UPenn), Mohammed Imthathullah (Google)

pip install git+https://github.com/vnmoorthy/pavo-bench.git

Headline results (vs fixed-cloud baseline, 50,000 voice turns)

Metric	Result	Significance
P95 end-to-end latency (H100, LibriSpeech)	−10.3% (−167 ms)	p = 2×10⁻⁶
Median latency	−34%
Energy per turn	−71%
Coherence-failure rate	7.1% → 0.9% (7.9× reduction)	hard-constraint masking, +110 ms median cost
Meta-controller size	85,041 parameters	—
Meta-controller training	106 seconds on A100	—

The empirical contribution is a two-regime coupling structure (sharp factual-accuracy cliff + gradual semantic degradation) characterized over n = 5,430 measurements across two hardware platforms (H100, Apple M3) and three LLM families (Llama 3.1 8B, Mistral 7B, Gemma2 2B).

Description

PAVO-Bench evaluates 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 when routing spoken-language queries through cascaded ASR, LLM, and TTS components.

The benchmark is organized into three tiers plus component-level ablation. All results were produced on real GPU hardware.

Dataset Files

Tier 1 — Unit-Level Validation

File	Description
`tier1_statistical_results.json`	Statistical reproducibility across 5 trials × 1,000 turns each (seeds 42, 123, 456, 789, 1024).
`tier1_coupling_results.json`	Coupling-cliff calibration — LLM quality degradation vs ASR word-error rate (WER 0–20%).
`tier1_llm_latency_results.json`	Latency profile for `llama3.1:8b` across short / medium / long generation contexts.

Tier 2 — Integration-Level Evaluation

File	Description
`tier2_e2e_results.json`	End-to-end pipeline measurements (cloud_premium, ondevice_fast, hybrid_balanced, pavo_adaptive) on 200 LibriSpeech samples.
`tier2_cross_dataset_results.json`	Cross-dataset ASR (LibriSpeech + FLEURS) for whisper-large-v3 and whisper-tiny.
`tier2_noise_robustness_results.json`	ASR robustness at SNR 5–30 dB plus clean baseline.

Tier 3 — Scale Evaluation

File	Description
`tier3_50k_summary.json`	Summary statistics for the 50K-turn dataset (40K train / 10K test split, complexity 1–5).
`tier3_scaling_results.json`	Per-model latency benchmarks for simple / medium / complex queries.

Component Analysis

File	Description
`component_ablation_results.json`	PAVO-Full vs PAVO-NoCoupling, Always-Cloud, Always-OnDevice, etc.

Usage

from huggingface_hub import hf_hub_download
import json

path = hf_hub_download(
    repo_id="vnmoorthy/pavo-bench",
    filename="tier3_50k_summary.json",
    repo_type="dataset",
)
print(json.load(open(path)))

Or via the pip package:

from pavo_bench import load_dataset, PretrainedPAVORouter, benchmark_router
turns = load_dataset(split="test")
pavo  = PretrainedPAVORouter.from_released()
print(benchmark_router(pavo, turns))

Citation

@article{veilukanthaperumal2026pavo,
  title   = {PAVO: Pipeline-Aware Voice Orchestration with Demand-Conditioned Inference Routing},
  author  = {VeiluKanthaPerumal, NarasingaMoorthy and Imthathullah, Mohammed},
  journal = {Transactions on Machine Learning Research},
  year    = {2026}
}

License

CC-BY 4.0