* feat(tts): add xAI as a Voice (TTS) provider
pipecat already ships an xAI TTS service (XAITTSService, WebSocket
streaming) but dograh never wired it into the service configuration, so
xAI could not be selected as a Voice provider in the cascading pipeline.
Wire it through:
- registry: ServiceProviders.XAI + XAITTSConfiguration (voices
eve/ara/leo/rex/sal, language, computed model) registered in TTSConfig
- service_factory: build XAITTSService in create_tts_service
- check_validity: api-key validation hook
- tests for the factory + docs
The Voice provider dropdown is schema-driven, so xAI appears with no UI
changes.
* fix(tts): validate xAI API key and drop misleading auto-language hint
Addresses review feedback on the xAI Voice provider:
- check_validity: replace the no-op xAI key check with real validation
against xAI's OpenAI-compatible API (models.list on https://api.x.ai/v1),
so a bad BYOK key is caught at configuration time instead of at call time.
- registry: remove the "auto" language hint from the field description.
pipecat's Language enum has no "auto" member, so the factory fell back to
English silently; the description no longer advertises detection we don't do.
- tests: cover xAI key validation (registered, accepts valid, rejects bad).
* fix(tts): validate xAI key against the TTS voices endpoint
xAI supports endpoint-scoped API keys, so a key scoped to Text-to-Speech
may lack the /v1/models ACL and would be wrongly rejected by the previous
models.list() check. Validate against GET /v1/tts/voices instead — the
scope the key actually needs for TTS — treating 401/403 as an invalid key
and connection errors as a clean, actionable message.
* fix: harden xAI TTS integration
---------
Co-authored-by: Sabiha Khan <sabihak89@gmail.com>
Co-authored-by: Sabiha Khan <87858386+chewwbaka@users.noreply.github.com>
* feat: add Smallest AI TTS and STT provider integration
Integrates Smallest AI's Waves (TTS) and Pulse (STT) APIs as selectable
providers in the Dograh platform. Dograh's pipecat fork already contains
the pipecat-level service implementations; this wires them into the API
configuration registry and service factory.
- Added `SMALLEST = "smallest"` to `ServiceProviders` enum
- Registered `SmallestAITTSConfiguration` (lightning-v3.1/v2, voices,
language, speed) and `SmallestAISTTConfiguration` (pulse model, 30+
languages) Pydantic config classes with the TTS/STT registries
- Added factory branches in `create_tts_service` and `create_stt_service`
routing to `SmallestTTSService` and `SmallestSTTService` from pipecat
* fix: update Smallest AI models to v4 naming convention
- TTS: rename lightning-v3.1 → lightning_v3.1, add lightning_v3.1_pro, drop deprecated lightning-v2
- STT: keep pulse only (pulse-pro is not a streaming model)
* fix: change default TTS voice from emily to sophia for lightning_v3.1
emily is not a verified lightning_v3.1 voice; sophia is the pipecat
SmallestTTSService default and confirmed to work with the standard pool.
* fix: replace 9 invalid lightning_v3.1 voice IDs with verified ones
jasmine, james, michael, aria, lara, asel, sarah, rishi, deepika do not
exist in the lightning_v3.1 voice catalog. Replaced with avery, liam,
lucas, olivia, freya, devansh, maya, dhruv, maithili — all verified
against the API.
* fix: smallest ai config validation and tts model compatibility
* chore: ruff fix
* chore: updated smallest ai schema in openapi.json
---------
Co-authored-by: Sabiha Khan <sabihak89@gmail.com>
Co-authored-by: Sabiha Khan <87858386+chewwbaka@users.noreply.github.com>
* feat: add Azure AI multi-provider support (TTS, STT, Embeddings, Realtime)
Enables Azure AI services across all model layers so users with Azure
credits can consolidate billing on a single provider.
- Voice (TTS): AzureSpeechTTSConfiguration via azure_speech provider
- Transcriber (STT): AzureSpeechSTTConfiguration via azure_speech provider
- Embedding: AzureOpenAIEmbeddingsConfiguration via azure provider
- Realtime: AzureRealtimeLLMConfiguration via azure_realtime provider
New files:
- api/services/pipecat/realtime/azure_realtime.py
- api/services/gen_ai/embedding/azure_openai_service.py
- api/tests/test_azure_speech_service_factory.py
The UI picks up all four providers automatically from the schema —
no frontend changes required.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: add validation for URL and params
---------
Co-authored-by: Vishal Dhateria <vishal@finela.ai>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Abhishek Kumar <abhishek@a6k.me>
* Add OpenAI-compatible API option in model configuration
Backend-only cherry-pick from 20617db37a.
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* fix: harden the base url settings in SaaS mode
---------
Co-authored-by: Chris Briddock <briddockchristopher@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* feat: add MiniMax provider support (Chat + TTS)
- Add MiniMax LLM provider using OpenAI-compatible API
- Models: MiniMax-M2.7, MiniMax-M2.7-highspeed
- Default base URL: https://api.minimax.io/v1
- Uses MINIMAX_API_KEY for authentication
- Add MiniMax TTS provider using Pipecat's MiniMaxHttpTTSService
- Models: speech-2.8-hd (default), speech-2.8-turbo
- 6 built-in voices
- Requires group_id configuration
- Add unit tests for both providers
* fix(minimax): validator, temperature, session cleanup, reasoning filter
- check_validity.py: wire MiniMax into _validator_map and enforce
group_id at save time. Without this, saving a config with a valid
key was rejected.
- registry.py: surface temperature on the LLM config (gt=0; MiniMax
rejects 0) and base_url on the TTS config
- service_factory.py:
* Plumb temperature through create_llm_service
* Normalize TTS base_url to include /t2a_v2 — pipecat appends only
?GroupId=... to the URL.
* Use the new MiniMaxLLMService (from pipecat) to strip
<think>...</think> reasoning that MiniMax-M2.7 emits inline in
delta.content (otherwise it leaks straight to TTS).
* Use MiniMaxOwnedSessionTTSService so the per-instance aiohttp
session gets closed in cleanup() instead of leaking sockets/FDs.
- minimax_tts.py: small wrapper around MiniMaxHttpTTSService that owns
the session it was handed (pipecat's caller-owns-session API
conflicts with the ftory's per-instance pattern).
- pipecat submodule: bumps to a commit that adds MiniMaxLLMService — a
thin OpenAILLMService subclass with the streaming <think> filter
(mirrors NvidiaLLMService's pattern for NIM reasoning models).
- Tests updated/added for all of the above.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: octo-patch <octo-patch@github.com>
Co-authored-by: Sabiha Khan <sabihak89@gmail.com>
