Configuration

kiosk-core and kiosk-ui are configured through environment variables (see Environment Variables).

The three model-hosting services (audio-analyzer, text-to-speech, rag-service) are configured through YAML files that the kiosk pins and mounts into the containers. The most common changes are the model and the inference device.

Model Selection

Each model-hosting service reads the model identifier from the same pinned config file used for device selection:

Service	File	Model fields
audio-analyzer	configs/audio-analyzer/config.yaml	models.asr.name (e.g. whisper-tiny, whisper-base); sentiment.model (optional)
text-to-speech	configs/text-to-speech/config.yaml	models.tts.name (e.g. microsoft/speecht5_tts, Qwen-TTS variant); model_variant
rag-service	rag-service/config.yaml	models.llm.hf_id, models.embedding.hf_id, retrieval.reranker.hf_id; per-model weight_format (int4, int8, fp16)

Use Hugging Face IDs where the field name is hf_id. Models are downloaded and exported on first start into the per-service models/ directory; subsequent starts reuse the cache.

Supported / validated models

The kiosk ships with the following defaults. These are the models the stack has been validated with — they are the recommended starting point. The Devices column lists the supported inference devices for each:

Service	Field	Default (validated)	Other examples	Devices
audio-analyzer ASR	models.asr.name	whisper-base	whisper-tiny, whisper-small, whisper-medium, whisper-large	CPU, GPU (GPU requires provider: openvino)
audio-analyzer sentiment	sentiment.model	speechbrain/emotion-recognition-wav2vec2-IEMOCAP	other SpeechBrain emotion-recognition models	CPU, GPU (disabled by default)
text-to-speech	models.tts.name	microsoft/speecht5_tts (SpeechT5)	Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice (Qwen-TTS)	CPU, GPU (int4 on iGPU produces noise; use fp16 or int8 on GPU)
rag-service LLM	models.llm.hf_id	Qwen/Qwen3-4B-Instruct-2507	other OpenVINO-exportable instruct LLMs	CPU, GPU (GPU recommended for acceptable latency)
rag-service embedding	models.embedding.hf_id	BAAI/bge-large-en-v1.5	BAAI/bge-base-en-v1.5, BAAI/bge-small-en-v1.5	CPU, GPU (CPU is usually fast enough)
rag-service reranker	retrieval.reranker.hf_id	BAAI/bge-reranker-base	BAAI/bge-reranker-large	CPU, GPU (optional)

[!IMPORTANT] Changing models is at your own discretion. The defaults above are the only combinations validated with this stack. Configuring models, variants, devices, or precisions other than the defaults may negatively affect the functionality, accuracy, latency, or stability of the application. You are responsible for ensuring the configuration you choose is correct and works for your use case — make changes only if you understand the implications.

In particular:

• Some models do not function properly at aggressive quantization. If a model produces garbled, empty, or low-quality output at int4, switch that model’s weight_format/dtype to int8 or fp16.

• A model must be exportable to OpenVINO IR for the OpenVINO backend; not every Hugging Face model is supported.

• Larger models increase first-run download/export time, memory use, and per-request latency, and may not fit on the selected device.

• After any change, restart the affected service and verify it loads and responds correctly before relying on it.

Inference Device

Each model-hosting service reads its device from a pinned config file:

Service	File	Fields
audio-analyzer	configs/audio-analyzer/config.yaml	models.asr.device, sentiment.device
text-to-speech	configs/text-to-speech/config.yaml	models.tts.device
rag-service	rag-service/config.yaml	models.llm.device, models.embedding.device, retrieval.reranker.device

The supported devices for each model are listed in the Supported / validated models table above.

Use uppercase device names (CPU, GPU). rag-service expects them as quoted strings; audio-analyzer and text-to-speech unquoted.

After editing, restart the affected service and confirm OpenVINO picked the device:

docker compose up -d --build --force-recreate <service-name>
docker compose logs <service-name> | grep -i -E "device|compiling|GPU|CPU"

OpenVINO prints a Compiling model on <DEVICE> line on first load.

GPU execution is delegated to the OpenVINO backend used by each service. Whether a given model actually runs on GPU and how it performs depends on the OpenVINO version and operator coverage for that model.

Environment Variables

kiosk-core has no config file. All settings are controlled through environment variables.

kiosk-core API (main:app)

Variable	Default	Description
KIOSK_CORE_ANALYZER_URL	http://127.0.0.1:8010/v1/audio/transcriptions	audio-analyzer transcription endpoint
KIOSK_CORE_RAG_URL	http://127.0.0.1:8020/api/v1/query	RAG query endpoint
KIOSK_CORE_TTS_URL	http://127.0.0.1:8011/v1/audio/speech	TTS speech synthesis endpoint
KIOSK_CORE_TTS_MODEL	qwen-tts	Model name sent to the TTS service
KIOSK_CORE_TTS_VOICE	(unset)	Voice name sent to the TTS service
KIOSK_CORE_TTS_LANGUAGE	English	Language sent to the TTS service
KIOSK_CORE_TTS_INSTRUCTIONS	(unset)	Optional style instructions for TTS
KIOSK_CORE_SAMPLE_RATE	16000	Default audio sample rate in Hz
KIOSK_CORE_CHUNK_SECONDS	4.0	Length of each audio chunk sent to audio-analyzer
KIOSK_CORE_SILENCE_TIMEOUT_SECONDS	1.5	Silence duration after speech that ends a session
KIOSK_CORE_MAX_SESSION_SECONDS	20.0	Hard cap on session duration
KIOSK_CORE_SILENCE_THRESHOLD	900	RMS threshold below which audio is treated as silence
KIOSK_CORE_BLOCK_DURATION_SECONDS	0.1	PortAudio capture block size
KIOSK_CORE_PREROLL_SECONDS	0.3	Audio buffered before speech starts
KIOSK_CORE_HTTP_TIMEOUT_SECONDS	120.0	HTTP client timeout for downstream calls

Gradio UI (gradio_app.py)

Variable	Default	Description
KIOSK_CORE_UI_BASE_URL	http://127.0.0.1:8012	Base URL of the kiosk-core API
KIOSK_CORE_UI_ANALYZER_URL	http://127.0.0.1:8010/v1/audio/transcriptions	Passed to start-file sessions as analyzer_url
KIOSK_CORE_UI_RAG_URL	http://127.0.0.1:8020/api/v1/query	Passed to start-file sessions as rag_url
KIOSK_CORE_UI_TTS_URL	http://127.0.0.1:8011/v1/audio/speech	Passed to start-file sessions as tts_url
KIOSK_CORE_UI_TIMEOUT_SECONDS	120.0	HTTP client timeout in the UI
KIOSK_CORE_UI_POLL_INTERVAL_SECONDS	0.35	How often the UI polls for session state updates

Compose Defaults

When running with the top-level docker-compose.yml, the defaults are wired to the internal Compose network:

• KIOSK_CORE_ANALYZER_URL=http://audio-analyzer:8010/v1/audio/transcriptions

• KIOSK_CORE_RAG_URL=http://rag-service:8020/api/v1/query

• KIOSK_CORE_TTS_URL=http://text-to-speech:8011/v1/audio/speech

• KIOSK_CORE_UI_BASE_URL=http://kiosk-core:8012

Most deployments should leave these values unchanged. Override them only when kiosk-core or kiosk-ui must call services outside the local Compose stack.

Session Parameters

Session parameters (chunk duration, silence threshold, etc.) can also be provided per-request in the POST body for /api/v1/sessions/start and /api/v1/sessions/start-file. Per-request values take precedence over the environment variable defaults.