chore(deps): bump actions/upload-artifact from 4 to 7#4
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chore(deps): bump actions/upload-artifact from 4 to 7#4dependabot[bot] wants to merge 38 commits into
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MIT-licensed Python 3.10+ project laid out with hatchling, ruff, mypy, pytest+coverage, and a versioned single-source __version__ module. First README is a stub; it will be rewritten as the toolkit grows.
These five modules are the wire format between every later subsystem (speech, grounding, policy, env, metrics). They're deliberately small and dependency-free so importing parley.core stays cheap. - types: Audio/Transcript/Grounding/Frame/Action/Trace dataclasses. - errors: ParleyError tree (ConfigError / RegistryError / ValidationError). - rng: RngManager with BLAKE2b-derived named sub-streams — process-stable. - registry: typed per-kind registries (speech / perturb / policy / metric / ...). - config: pydantic v2 strict models + YAML loader with source-path errors.
Including determinism checks for derive_seed, stream-identity invariants, extra-field rejection, and YAML/config error paths.
The runtime-checkable Protocol nails down the audio->Transcript contract. The mock frontend (perfect-ASR) lets users isolate downstream errors; the Whisper adapter is import-lazy and gated behind the 'whisper' extra so the default install stays light.
Encodes text as a sequence of windowed sine tones on a log-spaced frequency grid; decodes by FFT peak-picking over vocab bins. Clean audio round-trips exactly, while noise / mu-law / clipping perturbations collapse bin SNR and yield realistic substitutions. This is the trick that lets Parley measure WER end-to-end in CI without shipping a real acoustic model — and it's deliberately separate from the frontend wrapper so other tools can call encode/decode directly.
Audio: Gain, Clip, AdditiveNoise(SNR-calibrated), MuLawCodec, Reverb,
TimeStretch, PitchShift. All implemented in pure numpy so the toolkit
keeps zero heavy deps; quality is benchmark-grade, not production audio.
Linguistic: Disfluency (word stutter), FillerInsertion ("uhm"/"uh"),
AccentSubstitution (configurable lexical remap). Each respects the rate
parameter and uses the supplied numpy Generator only — no hidden state.
Compose threads a single RNG through a list and is identity on empty,
so the "clean" baseline row is just Compose([]).
Covers VERB <COLOR> <SHAPE> [to the DIRECTION] with verb canonicalization and a graceful <unknown> sentinel for failed parses. Six tests covering full / minimal / canonical / unknown / partial inputs. Acts as the reference baseline an LLM grounder can be benched against.
The bare 'env/' rule was eating the parley.env package. Anchor it.
A unit-square workspace populated with colored shape objects. Continuous xy_delta moves; explicit pick/place actions interact with whichever object the effector is on. Success predicates compare the final state against a GoalSpec (pick: holding the right object, place/push: target inside the named direction zone). This is *not* a robotics simulator — it's a controllable testbed that keeps the whole speech->ASR->grounding->policy->action->success chain runnable in milliseconds. Real sims (LIBERO/ManiSkill/RLBench) plug in via the same Environment Protocol.
Three reference policies implementing the VLAPolicy Protocol: - ScriptedPolicy: small approach->pick->transit->place state machine parameterised by Grounding+Scene. Acts as the success-rate ceiling on synthetic data given a particular grounder. - RandomPolicy: uniform xy + 5% pick/place — the floor baseline. - NoisyPolicy: Gaussian-perturbs another policy's actions. Useful for probing metric sensitivity and as an 'imperfect VLA' surrogate. Real models (OpenVLA/Octo/pi-0) plug in here via the same protocol.
…ormat generate_dataset() emits Episodes whose audio is the codec-encoded form of their instruction, closing the loop with the codec ASR: clean audio decodes to the original text, so any non-zero WER attributable purely to perturbations. Templates: pick/place/push the <COLOR> <SHAPE> [to the <DIRECTION>]. The closed vocab returned by vocab_for() also covers filler/disfluency/accent tokens so perturbed text round-trips cleanly. On-disk format: a jsonl index + a sibling .audio.npz blob. The split lets index-only operations (count, list, validate) skip the audio entirely and keeps random-access cheap.
…ness, bootstrap Per stage: - ASR: WER (with sub/ins/del breakdown), CER, KeywordRecall. - Grounding: GroundingExactMatch and GroundingSlotF1 over verb/target/dest. - Action: SuccessRate, ActionMSE/MAE (gated on a reference action sequence in trace.metadata), DTW with path-length normalization. - Efficiency: LatencyPercentiles (p50/p95/p99 + total + RTF). - Robustness: RobustnessDelta — clean-vs-perturbed deltas + mean/max degradation (post-hoc aggregator over per-perturbation means). - Aggregate: summarize() returns mean/SEM/percentile-bootstrap CI; paired_bootstrap_pvalue() for marking significant pipeline pairs. WER/CER use a textbook DP edit-distance with backpointer reconstruction so we can report sub/ins/del counts. Pure numpy; zero new deps. 27 tests covering each error type, partial-credit, and bootstrap edge cases.
The orchestrator that turns a config into per-episode results: - Pipeline composes a SpeechFrontend + Grounder + VLAPolicy by name and records per-stage wall-clock timings on the Trace. - run_episode walks an Episode through Perturbation -> Speech -> Grounding -> Env-rollout, populating Trace fields and timings_ms used by the latency metric. Per-stage RNG streams are derived from the global seed with distinct names so e.g. additive noise can't leak into env spawn. - ContentCache: file-backed (atomic-write) JSON cache keyed by a (pipeline, perturbation, episode_id, seed) hash; cache_dir=None means disabled. - expand_suite: cartesian product of pipelines x (clean + perturb groups) x episodes. Clean is always row 0 so robustness deltas have a baseline. - BenchmarkEngine builds pipelines lazily (codec frontend gets the dataset vocab injected automatically), persists per-run trace JSON to output_dir/traces/, and supports an optional ThreadPoolExecutor for workers > 1. Smoke run on synth(n=4) with 2 pipelines x 3 perturb groups = 24 results; scripted policy is the success-rate ceiling and random is the floor as expected. 13 tests cover cache, suite expansion, end-to-end runs, the threadpool path, and the unknown-pipeline error path.
…d JSON - aggregate_results() groups EpisodeResults by (pipeline, perturbation) and runs each metric through summarize() for mean/SEM/bootstrap CI. Skipped metrics (e.g. action_mse without a reference) are tracked per episode so the n in each cell is honest. - render_markdown() emits a GitHub-friendly table with mean [low, high] cells; render_csv() emits one row per cell with explicit ci_low/high columns for spreadsheet use. - build_leaderboard() ranks pipelines by clean success-rate, ties broken by lower mean degradation across perturbation groups. - dump_report/load_report use a versioned JSON schema (schema_version=1) carrying parley_version + suite_name + rows + leaderboard. Refusing unknown schema_versions on load means future format changes don't silently misread old reports.
Thin glue over the library. Highlights: - run: loads YAML config, optionally overrides dataset path / seed, runs the engine, dumps report.json + a config.resolved.yaml snapshot next to it, and prints a Rich-rendered table inline. - report: re-renders previously-written report.json as markdown / csv / json. The CSV path reconstitutes Summary objects from the JSON dump so it's available without re-running the suite. - list: enumerates registered plugins by kind for discoverability. - validate: parses a YAML and prints a one-line summary. `parley` is exposed as a console script via [project.scripts].
… smoke Includes a synth -> run -> report --format json end-to-end round-trip that exercises the full toolkit through the CLI.
Adds whitespace and line-break normalization. No semantic changes — all 130 tests stay green and mypy strict is still clean. Future commits can assume `ruff format` is the canonical layout, which is what CI enforces.
Dependabot is grouped so dev-tooling bumps (ruff, mypy, pytest) arrive as one PR per week instead of a stream of singletons. CODEOWNERS keeps review routing trivial for now (single maintainer).
- ci.yml: ruff check, ruff format --check, mypy on a Python 3.12 lint job; pytest+coverage matrix across Python 3.10..3.13 on Ubuntu plus a macOS 3.12 sanity job; a separate smoke-cli job runs the full `parley synth + run + validate` round trip against the quickstart example so CI breaks loudly if any end-to-end glue regresses. - codeql.yml: weekly security scan + on every push/PR. - release.yml: tag-triggered build + PyPI publish via trusted publishing (id-token: write, no API tokens needed).
Mirrors what CI enforces so committers catch lint/type breakage before push instead of waiting on the runners.
…cimate) + sweep helpers Three channel-flavored perturbations cribbed from the VoIP / telephony literature: - PacketLoss: drop contiguous packets at a configurable rate. Zeros rather than concealment — we're not doing PLC. - BandLimit: brick-wall FFT band-pass; default 300-3400 Hz matches the ITU-T G.712 narrowband telephony passband. - SpectralDecimate: zero out the top fraction of FFT bins, a poor-man's perceptual-codec proxy that's deterministic and dep-free. suites.py adds three programmatic sweep builders: - snr_sweep over CHiME/MUSAN-style SNR ladders. - codec_sweep: mu_law / telephone / spectral_decimate / packet_loss. - linguistic_sweep: disfluency / filler / accent_subst. These are for ad-hoc / notebook use; YAML configs can still list each PerturbationGroup explicitly when that's clearer. 9 new tests.
Two robustness-science staples cribbed from the speech/fairness eval literature (see docs/design-notes.md): - sensitivity_index() computes ΔTask / ΔInput per (pipeline, perturbation) using each pipeline's own "clean" row as baseline. The slope is interpretable: a 1-point increase in the upstream metric (WER, ...) costs N points of downstream task success. A degenerate ΔInput=0 with non-zero ΔTask becomes math.inf — surfaces 'perturbation didn't move the input metric but the task collapsed anyway' (a pipeline brittleness signal that survives WER staying flat). - worst_group_report() — per pipeline, returns the minimum value of a target metric across grouped rows. Currently grouped by perturbation but the surface accommodates future axes (accent stratum, speaker id) without redesign. 7 tests cover fragility comparison, zero-delta handling, missing-baseline skip, and the secondary-metric path.
The synth generator was rolling a 10% chance of omitting the direction for place/push verbs, but the env's success predicate requires a destination zone for those verbs, making those episodes unsatisfiable and pushing the scripted policy's clean success-rate to ~80% instead of 100%. Found it via the programmatic example's worst-group report — a nice example of the toolkit catching its own bugs. Now: pick verbs have no destination; place/push always carry one. include_directions=False degrades place/push to picks rather than emitting an unsatisfiable goal.
- quickstart.yaml: smallest interesting run (1 pipeline, 16 episodes, one mild perturbation). - robustness_panel.yaml: scripted vs random across 11 perturbations covering acoustic / channel / linguistic axes. - snr_sweep.yaml: a five-rung SNR ladder — the canonical degradation curve. - programmatic/custom_suite.py: builds the same kind of config in code using snr_sweep() / codec_sweep(), runs, prints headline table + sensitivity index + worst-group report. All three configs pass `parley validate`. examples/README.md indexes them.
ASCII diagram of the call graph from CLI through runner into the four pluggable subsystems, with a wire-format table mapping each parley.core.types dataclass to its origin -> sink, and sections on determinism, caching, parallelism, and the eval-toolkit lineage the design borrows from (HELM, lm-eval, Inspect).
- usage.md: install, the five subcommands, what `parley run` writes, programmatic usage, plugging in a real frontend, reproducibility. - metrics.md: every metric — what it measures, what scale, when to use, grouped by stage (ASR / grounding / action / efficiency / robustness / bookkeeping). - api-reference.md: curated public surface across core / data / runner / report / metrics / perturb. Anything not listed there is internal.
Grounds Parley's design in current (2023-2026) systems with verifiable URLs: VLA policies (RT-1/2, OpenVLA, Octo, pi_0, GR00T N1, RDT-1B...), speech LLMs (Whisper, Qwen2-Audio, SALMONN, Moshi, SeamlessM4T...), robot benchmarks (LIBERO, CALVIN, RLBench, ManiSkill, SimplerEnv, VLABench...), and eval-harness architecture patterns (HELM, lm-evaluation-harness, MTEB, Inspect). Also documents what the design is deliberately *not* good at — synth env isn't a physics sim, the codec ASR is robust to surprisingly low SNRs, action chunking and real RIRs are out of scope for v0. Better to be honest than oversell.
- README: badges, why-this-toolkit-exists, 60-second tour (CLI + Python), feature summary, doc index. - CHANGELOG: Keep-a-Changelog format with the 0.1.0 inventory under Added. - CONTRIBUTING: the four CI gates, plugin-registration recipe, commit style guide. - CODE_OF_CONDUCT: Contributor Covenant 2.1. - SECURITY: GitHub private-vuln-reporting flow, plus an honest note on the actual attack surface (npz + YAML).
…key, per-thread pipelines Three correctness issues found in review: 1. Linguistic perturbations were silent. They rewrite instruction.text, but run_episode kept feeding the *original* codec audio to the ASR, so disfluency/filler/accent_subst all measured WER=0. Now: when the frontend is the codec, re-encode the perturbed text before transcribe. (Real-audio adapters like Whisper are unaffected — they don't touch instruction.text in their perturbation path.) 2. The cache key was (pipeline, perturbation, episode, seed) by NAME only. Two suites reusing a group name 'noise' with different snr_db collided. cache_key now takes a config_fingerprint folding in the resolved pipeline + perturbation params + env + metrics + max_steps. 3. The engine shared one lazily-built Pipeline instance across the ThreadPoolExecutor, but policies hold per-episode state (reset/act). Under workers>1 we now build a private pipeline per run unit.
n_in=0 produced a negative linspace and out-of-bounds indexing in TimeStretch/PitchShift. Guard and return the empty array unchanged.
- linguistic perturbation moves WER - cache key param fingerprint + no cross-param collision - empty-input resample - threaded run matches serial (policy-state race guard) Plus CHANGELOG entries under [Unreleased].
Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 4 to 7. - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](actions/upload-artifact@v4...v7) --- updated-dependencies: - dependency-name: actions/upload-artifact dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com>
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Bumps actions/upload-artifact from 4 to 7.
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043fb46Merge pull request #797 from actions/yacaovsnc/update-dependency634250cInclude changes in typespec/ts-http-runtime 0.3.5e454baaReadme: bump all the example versions to v7 (#796)74fad66Update the readme with direct upload details (#795)bbbca2dSupport direct file uploads (#764)589182cUpgrade the module to ESM and bump dependencies (#762)47309c9Merge pull request #754 from actions/Link-/add-proxy-integration-tests02a8460Add proxy integration testb7c566aMerge pull request #745 from actions/upload-artifact-v6-releasee516bc8docs: correct description of Node.js 24 support in READMEDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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