The env var added complexity without meaningful benefit — explicit model
selection via the "model" input parameter is sufficient for both image and
video TokenHub tools.
- tools/video/hunyuan_cloud_video.py: drop env var fallback from
_resolve_model(), remove mention from install_instructions
- tests/contracts/test_hunyuan_cloud_video.py: remove
test_resolve_model_from_env and test_resolve_model_input_overrides_env
- .env.example: move TENCENT_TOKENHUB_API_KEY to a dedicated "Tencent
Hunyuan TokenHub API" section, drop TENCENT_TOKENHUB_MODEL comment,
broaden description from "video generation" to generic "Tencent Hunyuan
via TokenHub API"
Introduce a new video generation provider backed by the Tencent TokenHub
API (tokenhub.tencentmaas.com), an OpenAI-compatible gateway for Tencent
Hunyuan video models with simple Bearer-token auth.
- Add hunyuan_cloud_video tool (submit → poll → download) supporting
both text-to-video (hy-video-1.5) and image-to-video (yt-video-2.0)
- Add env vars: TENCENT_TOKENHUB_API_KEY, TENCENT_TOKENHUB_MODEL
- Add contract tests for the new tool
- Document setup, API flow, model pricing, and schema constraints in
PROVIDERS.md
- Update provider tables and capability matrix throughout docs
- .env.example: replace removed speech-1.x mention with the actual
supported backends (s1 / s2-pro / s2.1-pro)
- docs/PROVIDERS.md: add fish.audio section (setup, backend models,
per-byte pricing incl. free s2.1-pro-free tier) plus entries in the
env var summary, Provider-to-Tool Mapping, and Capability Coverage
- skills/INDEX.md: list fish-audio-tts in the TTS & Audio Layer 3 row
Add FishAudioTTS (capability=tts) so tts_selector auto-discovers a new
high-quality, voice-clone-capable provider. Backend model is required per
call: s1 (previous flagship, kept for compatibility), s2-pro (first S2
generation), s2.1-pro (latest flagship — inline emotion tags, 80+
languages), s2.1-pro-free (free tier for drafts). s1-mini and the
speech-1.x tier have been removed from the current fish.audio API and are
no longer supported. Voice cloning via reference_id with voice_id as a
selector-compatible alias. Adds temperature/top_p/repetition_penalty
sampling controls, optional sample_rate, opus output format, and a "low"
latency tier. Cost is estimated per UTF-8 byte to match fish.audio
billing. Includes a Layer 3 skill, .env.example entry, and unit tests.
Verified end-to-end with s2.1-pro + reference_id: generated a 7-segment
Japanese narration successfully.
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.
Implements the Jimeng/Volcengine part of issue #249, as agreed with
@xucailiang (who is handling the Kling provider separately).
Adds a first-class Jimeng video provider that calls the Volcengine
visual API directly (visual.volcengineapi.com) using HMAC-SHA256 V4
request signing with IAM AK/SK credentials. This is the first provider
in OpenMontage to use V4 signing (all others use Bearer token auth).
API flow: POST CVSync2AsyncSubmitTask -> poll CVSync2AsyncGetResult ->
download video_url.
Features:
- Text-to-video and image-to-video (Jimeng 3.0 Pro)
- Configurable frame count (121=5s, 241=10s at 24fps)
- Aspect ratio selection (16:9, 9:16, 1:1, etc.)
- Seed for reproducibility
- Full V4 HMAC-SHA256 request signing (not Bearer token)
- Error handling with Jimeng code 10000 success convention
- API key redaction in error messages (both env vars, no empty-string bug)
Env vars: VOLC_ACCESSKEY + VOLC_SECRETKEY (IAM AK/SK pair).
Idempotency keys include all output-affecting fields.
Files:
- tools/video/jimeng_video.py — new tool (V4 signing + submit/poll/download)
- tests/contracts/test_jimeng_video.py — 46 contract tests (no AK/SK needed)
- .env.example — VOLC_ACCESSKEY + VOLC_SECRETKEY
- docs/PROVIDERS.md — Volcengine Jimeng provider section
End-to-end tested with real Volcengine IAM credentials: generated a
1920x1088 H.264 5.04s video, ffprobe verified.
Test results:
python -m pytest tests/contracts/test_jimeng_video.py -q # 46 passed
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
The provider tools read these env vars but .env.example never listed
them, so a fresh setup silently misses them:
- FAL_AI_API_KEY — read by kling/minimax/veo/seedance/recraft/flux
as an alias for FAL_KEY (os.environ.get('FAL_KEY') or FAL_AI_API_KEY),
but only FAL_KEY was documented.
- REPLICATE_API_TOKEN — required by seedance_replicate; without it the
Replicate-backed Seedance path stays unselectable.
- HIGGSFIELD_API_KEY / _API_SECRET / HIGGSFIELD_KEY — the three forms
higgsfield_video.py accepts (combined key:secret vs split pair).
Doc-only, zero runtime change.
Refs: docs/REVIEW-image-to-video-voice.md §8 #11
Co-Authored-By: Claude <noreply@anthropic.com>
Google's TTS and Imagen tools advertised service-account auth
(GOOGLE_APPLICATION_CREDENTIALS) but only ever authenticated with an API
key string, so users with a service-account JSON could not use either tool.
google_tts.get_status() also over-reported availability when the JSON was
set, then failed at execute() — a silent-availability bug.
Separately, both hand-rolled _load_dotenv parsers kept inline comments as
values, so after `cp .env.example .env` every keyed tool falsely reported
"available" with no real credentials.
Changes:
- Add tools/google_credentials.py: lazy google-auth Bearer-token helper.
- google_tts: authenticate via Cloud TTS Bearer token when only a service
account is configured; make get_status() honest.
- google_imagen: route service-account auth to Vertex AI
({location}-aiplatform.googleapis.com) with project/location resolution,
alongside the existing AI Studio API-key path.
- Fix both _load_dotenv parsers to strip inline comments (quote-aware).
- Add google-auth to requirements; document the new env vars in .env.example.
- .gitignore: never commit GCP service-account key files.
Verified locally with a real service account: TTS produced a valid MP3 and
Imagen produced a valid 1408x768 PNG via Vertex AI. Existing test suite
passes (2 unrelated pre-existing failures only).
Closes#131
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
