Introduces CLA.md (individual CLA: contributors keep all rights to their
work, grant the project the right to offer contributions under additional
license terms; includes a written commitment in section 6 that the engine
remains open source) and a CLA Assistant Lite workflow that gates every PR
on a one-comment signature, with signatures stored in-repo on the
cla-signatures branch.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The greedy score mixed incommensurate scales: cosine similarity bounded
to [-1, 1] against an absolute list index that grows with the pool. For
a candidate j positions later to be preferred at the default
diversity=0.5, its similarity advantage had to exceed j -- impossible for
the non-negative cosines real footage embeddings produce. diversify()
therefore returned the input order verbatim, placing exact-duplicate
clips in adjacent edit slots, the one thing its docstring promises to
prevent. The threshold where the knob started working also depended on
pool size (0.66 at 4 candidates, 0.95 at 11).
Normalize the position term to [0, 1] so both terms share a scale. The
documented endpoints hold exactly as before: diversity=0 returns input
order, diversity=1 picks the most mutually dissimilar. Enumerating the
position also drops the O(n^2) remaining.index() lookup per candidate.
Closes#392
execute() sends sampleCount=number_of_images and estimate_cost() bills
0.04 * n, but result handling decoded only predictions[0] and wrote it to
a single output_path. Images 2..n were dropped: never decoded, never
written, absent from artifacts. The user paid for n and received one.
The result also misreported the drop rather than failing loudly --
images_generated returned len(predictions) (what the API sent) while
artifacts held a single path, so an agent picking between variants read a
count that did not match the artifact list.
Add _output_paths() and loop over every prediction, mirroring the pattern
already used by openai_image and grok_image: suffix multi-image paths
_1/_2/... so none overwrite each other, keep the exact requested path when
n=1, return all paths in artifacts, and report images_generated as the
count actually written.
Closes#388
resolveAsset() built `file:///` + a POSIX absolute path (which already
starts with /), yielding `file:////Users/...` (four slashes). Split the
branch: POSIX paths get `file://` + path (three slashes total), Windows
drive paths keep the explicit extra slash. Same fix across the five
compositions that share resolveAsset().
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
(cherry picked from commit 226a52d1e9b56b9da8b94e8e95ef6b753666abb0)
Add an Azure AI Speech transcription tool. It is opt-in: when
AZURE_SPEECH_KEY is configured the agent may prefer it for cloud STT,
while the local faster-whisper `transcriber` stays the default offline
path. Shared pipeline manifests are intentionally left unchanged, so no
default provider selection is altered for existing users.
- tools/analysis/azure_stt.py: new `azure_stt` tool (capability=analysis,
provider=azure) calling the Fast Transcription REST API. The local file
is uploaded via multipart and transcribed synchronously with word-level
timestamps and optional diarization — no Blob storage or async polling.
Output schema mirrors `transcriber` exactly, so it is a drop-in for
`subtitle_gen` and other transcript consumers. Follows the existing
provider-tool conventions (env-var status check, `_transcribe` helper,
cost_usd/model on the result, fallback="transcriber").
- Auto-discovered by the registry; no registry or selector changes.
- tests/tools/test_azure_stt.py: contract, discovery, status, response
mapping, execute guardrails, and a mocked-network success path (no live
API calls).
- .agents/skills + .claude/skills: azure-speech-to-text Layer-3 skill.
- docs/PROVIDERS.md: Azure AI Speech setup, API notes, and pricing.
- .env.example, skills/INDEX.md, AGENT_GUIDE.md: document the optional
cloud STT path alongside the default whisper transcriber.
Include every request field that can alter Kling video, image, avatar, or lip-sync media in the public idempotency contract. Add a shared regression matrix that detects future cache-key collisions while excluding transport-only controls.
Isolate Kling contract tests from the singleton registry so discovery state cannot leak into later selector tests. Align lip-sync face, audio, and timing payloads with the current official API and extend the live smoke coverage.
Review findings from PR #333:
P1: _download_via_uri assumed output_video.uri is always files/<id>.
The API can return a full resource URI or a ready-made
.../files/<id>:download?alt=media download URL, which produced an
invalid poll path with a second :download appended. New
_file_id_from_uri() extracts the bare id from every documented shape;
regression tests cover the full-URL form plus a parametrized matrix of
URI shapes.
P2: docs/PROVIDERS.md still described the Google key as TTS + Imagen
only. The shared-key section now covers gemini_omni_video (model id,
~$0.10/sec pricing table, paid-tier-only, edit-turn billing note), and
the env snippet, provider-to-tool mapping, and capability coverage
tables include the new provider.
Add gemini_omni_video, a native Gemini API provider wrapping
gemini-omni-flash-preview via the Interactions API. Text-to-video,
image/reference-to-video with <FIRST_FRAME>/<IMAGE_REF_N> prompt tags,
and stateful edit_video turns via previous_interaction_id — the only
provider in the fleet that can refine a clip without regenerating it.
Reuses the existing GOOGLE_API_KEY / GEMINI_API_KEY, so one Google key
now unlocks images, TTS, and video.
- New Layer 3 skill .agents/skills/gemini-omni (prompting, edit-loop
rules, tag/timecode syntax, preview limits) sourced from official
Google docs; linked via agent_skills and the AGENT_GUIDE Layer 3 map
- ai-video-gen gains the Gemini API gateway row + editing pointer
- veo_video/sora_video fallback lists and video_selector agent_skills
reference the new provider; quality_score 0.85 with rationale
- Contract tests: registry discovery, selector routing, status from
env keys, uri + inline delivery, edit turns, typed image parts,
store=false editability, cost clamp
_validate_artifacts_for_stage looked up CANONICAL_STAGE_ARTIFACTS[stage]
unconditionally, but the valid stage list comes from the pipeline manifest via
get_pipeline_stages(), which declares stages beyond the 9 canonical ones — e.g.
character-animation adds `character_design`/`rig_plan`. Such a stage passes the
`stage in valid_stages` guard, then raised an unhandled KeyError on the
canonical lookup, so those stages could never be checkpointed (the crash hits
write/read_checkpoint and friends, even for in_progress checkpoints).
Look the canonical artifact up defensively with `.get()` and skip the
required-artifact check when there is none. Canonical stages still require their
artifact when completed.
_ts_srt/_ts_vtt computed the seconds and millisecond fields independently:
`ms = int(round((seconds % 1) * 1000))`. When the fractional part is >= 0.9995
that rounds to 1000, emitting a malformed 4-digit `…,1000` value with no carry
into the seconds field (and, at 59.9999/3599.9999, no carry into minutes/hours).
For example 0.9999s became `00:00:00,1000` instead of `00:00:01,000`. ASR word
and segment end-times routinely land on such fractional boundaries, and the
resulting cue is rejected or mistimed by strict SRT/VTT parsers (ffmpeg
subtitles filter, VLC, browser WebVTT).
Decompose from a single rounded total-milliseconds value so the carry
propagates across all fields. Both formatters now share one `_hmsms` helper.
