hbiny/appworld-tmp

AppWorld-Vox

Synthetic English audio for AppWorld tasks, with hesitation, correction and interruption variants. Each sample contains the task setup, task annotation, and at most one selected WAV per type. Start with manifest.jsonl to load audio; use the format reference to interpret annotations and timing.

This export has 189 samples from 63 families and 1,275 WAVs. Train contains 54 samples / 371 WAVs; test contains 135 samples / 904 WAVs. Split assignments inherit the existing task split, with no family shared between train and test. Clean baseline audio is included for all 189 samples, using the same per-sample speaker as the existing variants.

Available audio

Type	WAVs	Expected control intent	Definition
clean	189	execute	Original normalized instruction with no generated fillers or inserted pauses.
filler	181	execute	Hesitations/fillers or added pauses preserve the original request.
false-start	189	execute	A mistaken value is corrected immediately within the same utterance.
addition	174	execute	An initially omitted condition or argument is added after the initial request.
revision	179	execute	A value in a completed initial request is corrected in a later utterance.
cancel	182	cancel	A later utterance cancels the entire request; rollback is not requested.
halt	181	wait	A later utterance asks the agent to pause and await further instructions.

162 samples have all six perturbation types plus clean (seven conditions). The remaining 48 sample/type combinations are skipped because no eligible LLM-selected WAV was available; samples are retained when at least one type is available. reports/missing.json records every skip. These are not 1,275 independent tasks: several audio variants share one sample's task and database.

Audio is 24 kHz, mono, 16-bit PCM WAV. All audio was copied unchanged from the selected generation output. Other types can also include fillers: labels describe the primary perturbation. See statistics.json for duration, speaker-ID and split statistics.

Directory layout

Appworld-vox/
├── README.md
├── manifest.jsonl                 # One record per WAV; canonical index
├── splits.json                    # Sample IDs in each split
├── statistics.json                # Counts and audio properties
├── docs/FORMAT.md                 # Fields, timing coordinates, provenance
├── tools/validate_dataset.py      # Standalone standard-library validator
├── reports/                       # Selection, skips, hashes, verification
├── train/
│   └── <task_id>/
│       ├── specs.json             # Authoritative instruction and task setup
│       ├── dbs/                   # Task database snapshots
│       ├── ground_truth/          # Evaluation code and answers
│       ├── annotation.json        # Pause/correction candidates and spoken text
│       ├── variants.json          # Available types and missing_types
│       ├── audio/<type>.wav       # Up to seven WAVs; missing types have no file
│       └── metadata/<type>.json   # TTS, filler/pause events and LLM selection
└── test/
    └── <task_id>/                 # Same layout as train

All manifest paths are relative to this root. Absolute paths inside provenance metadata refer to the original generation environment and are not required to read this export.

Read one sample

Run this from the dataset root using Python 3.9 or newer; no third-party package is needed:

import json
import wave
from pathlib import Path

root = Path.cwd()
with (root / "manifest.jsonl").open() as f:
    row = next(json.loads(line) for line in f if line.strip())
metadata = json.loads((root / row["metadata"]).read_text())
print(row["task_id"], row["type"], row["expected_control_intent"])
print(metadata["source_metadata"]["spoken_utterances"])
with wave.open(str(root / row["audio"]), "rb") as wav:
    pcm_bytes = wav.readframes(wav.getnframes())
    sample_rate = wav.getframerate()

Filter manifest rows by split, type or task_id as needed. For a comparison across all six conditions, group by task_id and retain only IDs with all six perturbation types (162 samples); these also have clean audio. Do not fill missing conditions with another sample's audio.

Selection and task sources

For the six perturbation types, selection follows the LLM default recommendation implemented by the annotation-review backend at export time. Within each sample/type, choose the best naturalness rank that passes current-source and WAV-hash checks and Audio semantic equivalence. False-start, addition and revision additionally require Text semantic equivalence. Rank 1 is skipped if it fails these gates; singleton candidates receive the same semantic checks. Unknown/uncertain judgments are not accepted. Human Yes/No reviews are preserved but do not drive this LLM selection. Clean originals were subsequently imported at user request from tasks_llm/<task_id>/audio_natural/clean/; each has exactly one source candidate. Their generation ASR and quality flags are preserved, but this import did not add LLM semantic or human listening approval. Clean metadata uses llm_selection: null and records import_selection instead. No new LLM or TTS generation was run during export.

Task assets normally come from the original tasks directory; annotations and audio come from tasks_llm. Six tasks use existing TTS-aligned instruction overrides from tasks_tts_1, and three of those also use synchronized evaluation answers. reports/task_source_overrides.json preserves the exact before/after documents and hashes. Use each sample's specs.json as the instruction authority.

Clean and the first four perturbation types target task execution with the original final goal. Cancel and halt require separate control-behavior evaluation: passing or failing the original completion test alone does not establish correct cancellation or waiting. ground_truth/ is evaluation material, not agent input. Running tasks requires a compatible AppWorld runtime, which is not bundled here.

Filler positions and metadata

metadata/<type>.json preserves the actual TTS utterances, source annotation/variant documents, ASR/WER, speaker and generation settings, LLM judgments and ranking reasons. source_metadata.utterance_reports[].timeline retains filler labels, neighboring words, boundary context and alignment flags. inserted_pause_intervals gives added short/long silence spans in final WAV frames and seconds, with exclusive end positions.

Inserted silence spans are not filler speech spans. ASR word times use source-utterance coordinates, while inserted intervals use final-WAV coordinates. Exact filler word boundaries are not human verified. See docs/FORMAT.md before using timing for alignment or playback overlays.

Verify the export

From this directory, run:

python3 tools/validate_dataset.py

Alternatively, pass a relocated dataset root as the first argument. The tool checks manifest paths, WAV bytes/headers, metadata consistency, pause bounds, missing-type records, split membership and inventory hashes. It loads no model, follows no historical source paths and writes no files. A successful run prints JSON with status: passed; failures exit with a nonzero status.

reports/validation.json records original export verification. reports/documentation_audit.json records the subsequent standalone structural audit. Inventory hash exclusions are explicit in reports/inventory_policy.json. Verification does not certify human listening quality or task execution success.

The original dataset was replaced only after verification, and its temporary backup was removed as requested; reports/lifecycle.json records this. reports/rebuild_appworld_vox.py is a historical script requiring the original internal sources, not a portable generation pipeline. Embedded source documents remain available if those original paths are unavailable. No new license is assigned by this export; license and citation information were not supplied.

Clean import: reports/clean_import.json lists all 189 sources, destination records and validation results. Earlier selection, lifecycle and documentation audit reports describe the initial six-type export; the latest sample-update report records the current seven-condition validation. Existing perturbation WAVs, including previously identified semantic issues, were not replaced or reselected.

Sample audio refresh (2026-10-19): 5a83b05_2 and 9871968_3 were updated from the regenerated source outputs, including clean. The six perturbation types use the latest eligible annotation-review LLM defaults; clean uses the regenerated original. Annotation normalization, metadata, pause intervals and manifest hashes were refreshed. See reports/sample_update_20261019.json for before/after hashes, actual utterances and current validation. Earlier snapshot/import reports are historical.

Ranked repair refresh: selected sample/type audio and metadata were updated after current text/audio semantic review and old/new naturalness comparison. See reports/ranked_repair_20261020T020225128139Z.json for the selected sources and previous/new WAV hashes.