---
title: Praxis-Briefing
emoji: 🩺
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 5.50.0
python_version: "3.12"
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - build-small-hackathon
  - backyard-ai
  - tiny-titan
  - off-brand
  - best-demo
  - well-tuned
  - field-notes
  - speech-to-speech
  - german
  - healthcare
  - voice-assistant
  - liquid-ai
  - lfm2
  - fine-tuned
  - gradio
  - track:backyard
  - achievement:welltuned
  - achievement:offbrand
  - achievement:fieldnotes
short_description: German speech-to-speech briefing for a GP, on LFM2.5-Audio
models:
  - LiquidAI/LFM2.5-Audio-1.5B
  - jempf/peitho-1.5b-v6
datasets:
  - jempf/peitho-v7-data
  - wikimedia/wikipedia
---

# 🩺 Praxis-Briefing — a German voice for Liquid's LFM2.5-Audio

**Build Small Hackathon submission · Track: Backyard AI.** We taught Liquid AI's
[`LFM2.5-Audio-1.5B`](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) — a model
that ships **English-only** for speech-to-speech — to **speak and understand German**,
and wrapped it in a hands-free voice assistant for a solo physician's morning routine.

Ask Peitho a spoken question about today's (fictional) appointment schedule; it answers
out loud, in one short German sentence, in real time over WebRTC.

> **Build Small Hackathon** — Track: **🏡 Backyard AI** · Badges: **🐜 Tiny Titan**,
> **🎯 Well-Tuned**, **🎨 Off-Brand**, **📓 Field Notes**, **🎬 Best Demo**
>
> - 🎥 Demo video: **[Watch the demo on Vimeo](https://vimeo.com/1201527821)**
> - 📣 Social post: **[See the post on X](https://x.com/juliankempf99/status/2066589626991522303)**
> - 📓 Field notes write-up: [`FIELD_NOTES.md`](FIELD_NOTES.md)
> - 🧠 Model (1.5B, fine-tuned): [`jempf/peitho-1.5b-v6`](https://huggingface.co/jempf/peitho-1.5b-v6)

> ⚠️ **Not a medical device.** The schedule is mock data, there is no connection to any
> real practice management system, and answers are for demonstration only.

---

## Why this matters for a physician

A solo GP ("Hausärztin") starts every day context-switching between a screen and a patient.
The keyboard is a tax on attention: looking things up, typing notes, clicking through a
practice system — all while a patient is in the room.

**Voice is the natural interface for a clinician whose hands and eyes are busy.** A small,
fast, *on-device-class* speech-to-speech model means:

- **Hands-free, eyes-up.** Ask "Wer ist mein nächster Patient?" while washing hands or
  walking to the next room — no screen, no keyboard.
- **Local language, local trust.** German in, German out. A US-English-only model is a
  non-starter in a German practice; the fine-tune is the whole point.
- **Small enough to run privately.** At 1.5B parameters this class of model can run close
  to the data — important when the domain is patient information.
- **Latency that fits a conversation.** Real-time speech-to-speech (not record → upload →
  transcribe → LLM → TTS) keeps the interaction natural.

The mock "Tagesplan" use case is deliberately simple, but it stands in for the real prize:
a quiet, German-speaking assistant that surfaces the right fact at the right moment without
pulling a doctor out of the moment with their patient.

---

## What we actually built for the hackathon

The headline contribution is **a new language for an audio foundation model**:

1. **A German speech-to-speech dataset pipeline** (`build_v7_dataset.py`) — synthetic,
   reproducible, and resumable end-to-end.
2. **A German fine-tune of LFM2.5-Audio-1.5B** (`jempf/peitho-1.5b-v6`) that produces
   natural German audio output — the base model does not.
3. **This real-time WebRTC demo** that makes the result tangible in a believable clinical
   scenario.

### The dataset: teaching the model to talk

`LFM2.5-Audio-1.5B` is documented as **English-only for speech-to-speech**. A control test
with the unmodified base model produced garbled, mixed-language text and robotic German
audio — confirming that **German capability has to be trained in, not prompted in.**

So we generated a synthetic German speech-to-speech corpus:

| Stage | Tooling | What happens |
|-------|---------|--------------|
| **1. Text** | Claude (Haiku + Sonnet) | ~10k German Q&A pairs across **8 intent buckets**: factual, smalltalk, casual, identity, instruction-following, refusal, and more. The **factual** bucket is seeded from **German Wikipedia** summaries so answers are grounded, not hallucinated. Numbers are spelled out for natural TTS. |
| **2. Speech** | ElevenLabs (`eleven_multilingual_v2`, 24 kHz PCM) | Each turn rendered to audio. **User turns rotate through 4 voices** (acoustic diversity, so the Conformer encoder generalizes to many real speakers); **assistant turns use a single consistent voice** — *this* is what Peitho sounds like. |
| **3. Upload** | 🤗 `huggingface_hub` | Packaged to a Hub dataset repo for training. |

Design choices that matter for a *speech* model:

- **Acoustic diversity on input, consistency on output.** Many voices teach robust listening;
  one voice gives the model a stable identity to speak with.
- **Intent coverage, not just facts.** Greetings, identity, instruction-following and explicit
  refusals teach the model *how to behave*, not only *what to know*.
- **Wikipedia-grounded factual turns** keep the German it learns true and broad.

### The model

`jempf/peitho-1.5b-v6` is the LFM2.5-Audio-1.5B base with our German fine-tune weights
overlaid. Named after **Peitho**, the Greek goddess of persuasion and eloquence.

It is an early checkpoint: fluent, natural German speech-to-speech, but a 1.5B model is not
a reliable knowledge base. In this demo we lean on its real strength — German voice
interaction — and ground the actual facts via the in-app schedule and few-shot prompting.
Closing the grounding gap is exactly what the next dataset iteration targets.

---

## How the demo works

`app.py` loads stock `LFM2.5-Audio-1.5B` via `liquid_audio.demo.model`, then overlays the
v6 German weights from `jempf/peitho-1.5b-v6` with `accelerate.load_checkpoint_in_model`.
`chat.py` serves a real-time WebRTC speech-to-speech UI:

- **Push-to-talk** voice via [`fastrtc`](https://github.com/gradio-app/fastrtc) with VAD —
  speak, pause, and Peitho replies with interleaved text + audio.
- The day's **Tagesplan** is injected into the conversation so answers are grounded in it,
  with a few-shot pattern so the small model answers from the plan.
- Sampled decoding (`temp=0.2, topk=10`) for steady, repeatable answers.

### Running it / configuration

WebRTC on Hugging Face Spaces needs a **TURN relay**. Because the free community TURN
servers (`turn.fastrtc.org`) are currently down
([fastrtc#429](https://github.com/gradio-app/fastrtc/issues/429)), this Space uses
**Cloudflare Calls** TURN with your own free keys. Add two Space secrets:

| Secret | Where to get it |
|--------|-----------------|
| `CLOUDFLARE_TURN_KEY_ID` | Cloudflare dashboard → Calls → create a TURN App |
| `CLOUDFLARE_TURN_KEY_API_TOKEN` | same TURN App |

Without them the app still boots (STUN-only), but real-time mic relay may not connect.
Edit the mock schedule and prompts in `briefing.py`.

---

## How it maps to the prize pool

- **🏡 Backyard AI (track):** a practical tool for a real person we know — a solo GP. Voice is
  the right interface for a clinician whose hands and eyes are on the patient, not a keyboard.
- **🐜 Tiny Titan:** the whole thing runs on a **1.5B** model — and not a text model, a full
  **speech-to-speech** model doing real-time German audio in and audio out. Tiny weights, big lift.
- **🎯 Well-Tuned:** the German capability is a **fine-tune we published** to the Hub
  ([`jempf/peitho-1.5b-v6`](https://huggingface.co/jempf/peitho-1.5b-v6)) — the base model is
  English-only, so the fine-tune is the entire contribution.
- **🎨 Off-Brand:** a fully custom **EHR-style clinical interface** — bespoke light theme, an
  electronic-health-record top bar, a tabular Sprechstundenplan, voice example chips, and a clean
  answer panel. No default Gradio look left.
- **📓 Field Notes:** a written build report — see [`FIELD_NOTES.md`](FIELD_NOTES.md).
- **🎬 Best Demo:** a voice-first demo that films well — ask in German, hear German back, live.

---

## Stack

- **Model:** [`LiquidAI/LFM2.5-Audio-1.5B`](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B)
  + German fine-tune [`jempf/peitho-1.5b-v6`](https://huggingface.co/jempf/peitho-1.5b-v6)
- **Audio runtime:** [`liquid-audio`](https://github.com/Liquid4All/liquid-audio) (Mimi codec, interleaved text+audio)
- **Real-time transport:** `fastrtc` (WebRTC + VAD), Cloudflare TURN
- **UI:** Gradio 5
- **Dataset pipeline:** Claude (text) + ElevenLabs (TTS) + German Wikipedia + 🤗 Hub