Instructions to use audarai/Audar-TTS-V1-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use audarai/Audar-TTS-V1-Turbo with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="audarai/Audar-TTS-V1-Turbo",
	filename="Audar-TTS-V1-Turbo-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "\"The answer to the universe is 42\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use audarai/Audar-TTS-V1-Turbo with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M

Use Docker

docker model run hf.co/audarai/Audar-TTS-V1-Turbo:Q4_K_M

LM Studio
Jan
Ollama
How to use audarai/Audar-TTS-V1-Turbo with Ollama:
```
ollama run hf.co/audarai/Audar-TTS-V1-Turbo:Q4_K_M
```

Unsloth Studio

How to use audarai/Audar-TTS-V1-Turbo with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for audarai/Audar-TTS-V1-Turbo to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for audarai/Audar-TTS-V1-Turbo to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for audarai/Audar-TTS-V1-Turbo to start chatting

How to use audarai/Audar-TTS-V1-Turbo with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "audarai/Audar-TTS-V1-Turbo:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use audarai/Audar-TTS-V1-Turbo with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default audarai/Audar-TTS-V1-Turbo:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use audarai/Audar-TTS-V1-Turbo with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf audarai/Audar-TTS-V1-Turbo:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "audarai/Audar-TTS-V1-Turbo:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use audarai/Audar-TTS-V1-Turbo with Docker Model Runner:
```
docker model run hf.co/audarai/Audar-TTS-V1-Turbo:Q4_K_M
```

Lemonade

How to use audarai/Audar-TTS-V1-Turbo with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull audarai/Audar-TTS-V1-Turbo:Q4_K_M

Run and chat with the model

lemonade run user.Audar-TTS-V1-Turbo-Q4_K_M

List all available models

lemonade list

Audar-TTS-V1-Turbo · GGUF

Open, Arabic-first, expressive zero-shot text-to-speech — the balanced production default.

From Arabic to the world.

🎧 Voice Gallery · 💻 Local Deployment · 📦 GGUF Variants · 📜 License

Audar-TTS-V1-Turbo is the balanced production tier of the Audar-TTS family — a ~1.64B-parameter community-licensed speech model that trades a little of Flash's raw speed for higher fidelity and steadier prosody, while staying efficient. It is Arabic-first (including Gulf/Emirati and other dialects), fully bilingual with English, and clones any voice from a 5–15 second reference clip with no per-speaker fine-tuning. This repository ships it as GGUF quantizations for llama.cpp.

Turbo treats speech synthesis as next-token prediction: a language-model backbone predicts discrete audar-codec acoustic tokens, which a lightweight neural codec decodes to 24 kHz audio. There is no phonemizer and no per-language G2P — so Arabic dialects and Arabic⇄English code-switching are handled from data rather than brittle pronunciation rules.

GGUF weights under the AudarAI Community License v1.0 — free for research and limited commercial use by smaller organizations; larger/enterprise use needs an AudarAI Enterprise License. See License.

Highlights

🗣️ Zero-shot cloning	🎭 Expressive control	🌍 Arabic-first + English
Clone any voice from a 5–15 s reference clip — no fine-tuning.	8 inline tags — `[laughs]` `[whispers]` `[excited]` `[curious]` …	MSA + Gulf/Emirati dialects, code-switching, no phonemizer / no G2P.

📦 GGUF · Q4 / Q5 / Q8	🔊 Studio-clean 24 kHz	🛡️ Responsible by design
Runs on CPU, GPU and edge via `llama.cpp`.	Single-codebook 50 Hz neural codec (audar-codec).	Consent-first cloning · responsible-use guidance.

🎧 Voice Gallery

Six ready-to-use voices ship with Audar-TTS, free to use. They are synthetic voices created by interpolating multiple speakers — they do not replicate or resemble any real individual. Each sample is a zero-shot clone: the same reference voice speaks a fresh English and Arabic line. (Captions match the audio word-for-word.)

Voice	Reference & samples — Audar-TTS Turbo
demo_male_1 _Male _{warm, confident}	_{REFERENCE VOICE} English Oh, you have to hear this — [excited] we just closed the biggest deal of the entire year, and honestly, I still can't quite believe it! العربية لا يمكنني الانتظار لأخبرك — [excited] لقد أنجزنا المشروع أخيراً بعد كلّ هذا التعب، [laughs] وصدّقني، إنه أجمل شعورٍ على الإطلاق!
demo_male_2 _Male _{soft, intimate}	_{REFERENCE VOICE} English Come a little closer for a second — [whispers] I've been planning something special all week long, [mischievously] and you are going to absolutely love it. العربية تعال، اقترب قليلاً — [whispers] لقد خطّطتُ لمفاجأةٍ رائعة طوال الأسبوع، [mischievously] وأنا واثقٌ تماماً أنّها ستُدهشك حقاً!
demo_male_3 _Male _{bright, curious}	_{REFERENCE VOICE} English Wait, really? [curious] You built the whole thing yourself over the weekend? [excited] That is genuinely incredible — tell me everything, right now! العربية لحظة، حقاً؟ [curious] هل بنيتَ كلّ هذا بنفسك في يومين فقط؟ [excited] هذا مذهلٌ فعلاً — احكِ لي كلّ التفاصيل الآن!
demo_female_1 _Female _{vibrant, joyful}	_{REFERENCE VOICE} English Guess what just arrived in the mail — [excited] the acceptance letter we have been waiting for, [laughs] and I actually screamed out loud! العربية خمّن ماذا وصل في البريد للتوّ — [excited] رسالة القبول التي انتظرناها طويلاً، [laughs] لقد صرختُ من شدّة الفرح!
demo_female_2 _Female _{velvety, playful}	_{REFERENCE VOICE} English Okay, lean in for just a moment — [whispers] I found the most perfect little café downtown, [mischievously] and it is going to be our new secret spot. العربية حسناً، اقتربي قليلاً — [whispers] وجدتُ مقهىً صغيراً رائعاً في وسط المدينة، [mischievously] وسيكون مكاننا السريّ الجديد!
demo_female_3 _Female _{airy, dreamy}	_{REFERENCE VOICE} English You won't believe the view from up here — [excited] the whole city is glowing at sunset, [laughs] it honestly looks just like a dream! العربية لن تصدّقي هذا المنظر من هنا — [excited] المدينة كلّها تتلألأ عند الغروب، [laughs] وكأنّها لوحةٌ من حلمٍ جميل!

_{🎛️ Demo generation settings: temperature = 1.0 · repetition_penalty = 1.1 · top_k = 40 · top_p = 0.9 — tuned for maximum expressiveness ([laughs], [excited], [whispers]…). A low repetition_penalty (≈1.1) is what lets laughter through — a higher value suppresses it. For steadier, more neutral delivery, lower temperature toward 0.6–0.7.}

Model summary

Model	Audar-TTS-V1-Turbo (GGUF)
Task	Text-to-speech (autoregressive, neural-codec)
Backbone	Qwen2.5-1.5B-class decoder-only transformer
Parameters	~1.64B
Distribution	GGUF — Q4_K_M / Q5_K_M / Q8_0
Vocabulary	217,225 (text + 65,536 audar-codec speech tokens + control tokens)
Context length	32,768 tokens
Companion codec	audar-codec (a NeuCodec fine-tuned for Arabic) → 24 kHz output
Languages	Arabic (MSA + dialects incl. Gulf/Emirati) and English
License	AudarAI Community License v1.0

The Audar-TTS family

Tier	Params	Best for
Flash	~553M	Real-time, edge/on-device, high-throughput serving
Turbo (this model)	~1.64B	Balanced quality and latency — the everyday default
Pro (coming soon)	Larger	Maximum expressiveness and fidelity

All tiers share one prompt/conditioning protocol, so you can move between them without changing your integration.

GGUF variants

File	Approx. size	Notes
`Audar-TTS-V1-Turbo-Q8_0.gguf`	~1.75 GB	Near-lossless
`Audar-TTS-V1-Turbo-Q5_K_M.gguf`	~1.21 GB	Strong quality/size balance
`Audar-TTS-V1-Turbo-Q4_K_M.gguf`	~1.07 GB	Smallest; best for constrained hardware

The codec — audar-codec

The backbone emits discrete <|speech_N|> acoustic tokens; a codec turns those into a 24 kHz waveform. These tokens are decoded by audar-codec — Audar's fine-tuned NeuCodec, adapted for Arabic on extensive data.

🙏 Credit & thanks to Neuphonic for open-sourcing NeuCodec. audar-codec builds on their work, and the tokens remain NeuCodec-compatible — so you can decode with NeuCodec directly (as shown below), which makes this release fully open and reproducible.

Local deployment (GGUF)

# pip install llama-cpp-python neucodec soundfile torch huggingface_hub
import re, torch, soundfile as sf
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
from neucodec import NeuCodec   # base NeuCodec (public); audar-codec is the Arabic-tuned companion

# 1) Backbone (GGUF) — CPU by default; set n_gpu_layers=-1 to offload to GPU
gguf = hf_hub_download("audarai/Audar-TTS-V1-Turbo", "Audar-TTS-V1-Turbo-Q4_K_M.gguf")
llm  = Llama(model_path=gguf, n_ctx=4096, n_gpu_layers=0, verbose=False)

# 2) Codec — encodes the reference clip and decodes the output
codec = NeuCodec.from_pretrained("neuphonic/neucodec").eval()

# 3) Zero-shot reference: a 5-15 s clip (16 kHz mono) + its transcript
ref_codes = codec.encode_code("reference.wav").squeeze().tolist()
ref_text  = "transcript of the reference clip"
ref = "".join(f"<|speech_{c}|>" for c in ref_codes)

target = "مرحبا! [whispers] أهلاً وسهلاً بك."
prompt = ("user: Convert the text to speech:"
          f"<|REF_TEXT_START|>{ref_text}<|REF_TEXT_END|>"
          f"<|REF_SPEECH_START|>{ref}<|REF_SPEECH_END|>"
          f"<|TARGET_TEXT_START|>{target}<|TARGET_TEXT_END|>"
          "\nassistant:<|TARGET_CODES_START|>")

# 4) Generate speech tokens; stop at <|TARGET_CODES_END|>
tce  = llm.tokenize(b"<|TARGET_CODES_END|>", add_bos=False, special=True)[0]
toks = llm.tokenize(prompt.encode("utf-8"), add_bos=False, special=True)
ids  = []
for tid in llm.generate(toks, temp=1.0, top_k=40, top_p=0.9, repeat_penalty=1.1):
    if tid == tce or len(ids) >= 2048: break
    ids.append(tid)
text = "".join(llm.detokenize([t], special=True).decode("utf-8", "ignore") for t in ids)

# 5) Decode to 24 kHz audio
codes = [int(x) for x in re.findall(r"<\|speech_(\d+)\|>", text)]
wav = codec.decode_code(torch.tensor(codes)[None, None, :]).cpu().numpy()[0, 0, :]
sf.write("out.wav", wav, 24000)

Prefer a managed endpoint? The same model is available via the Audar API/SDK (client.tts, model id audar-tts-v1-turbo).

Recommended sampling: temperature=1.0, top_k=40, top_p=0.9, repeat_penalty=1.1 — the Voice Gallery demo settings (a low repeat_penalty is what lets laughter through). Stop at <|TARGET_CODES_END|>; lower temperature toward 0.6–0.7 for steadier, less expressive delivery.

Expression tags

Insert tags inline in the target text to shape delivery:

[laughs] · [curious] · [excited] · [sighs] · [exhales] · [mischievously] · [whispers] · [sarcastic]

Tags work in both Arabic and English. Use them sparingly for the most natural results.

Intended use & limitations

Intended use. Voice assistants and agents, narration, accessibility, IVR/telephony, and any application that synthesizes Arabic and English speech — in the cloud, on-premises, or offline. Zero-shot cloning is intended for consented voices only.

Limitations.

Chunk very long inputs at sentence boundaries.
Multi-word or stacked expression tags are more fragile than single tags.
As with all neural TTS, rare names, numbers, and code-switch boundaries can be mispronounced.

Evaluation

Audar-TTS is evaluated on an internal cross-provider Arabic TTS benchmark covering intelligibility (resynthesis WER/CER), predicted naturalness (UTMOSv2, SQUIM), speaker similarity, and expression fidelity. We report results with their caveats: predicted-MOS metrics are not calibrated for Arabic, similarity/expression proxies are English-biased, and a formal human MOS/CMOS study is planned, not yet completed. Qualitatively, Audar-TTS is designed to be competitive with leading systems on Arabic intelligibility and expression control, with particular strength on Gulf-dialect speech.

📊 An audited benchmark table will accompany the forthcoming Audar-TTS technical report.

📜 License

Released under the AudarAI Community License, Version 1.0 — full text: AudarAI Community License v1.0 · repo copy. This is source-available and open-weight, but not unrestricted. In brief (the license text controls):

✅ Research use — free for research, evaluation, benchmarking, education, and prototyping.
✅ Limited commercial use for a Community Entity — an organization within all of these thresholds: < 50 staff, < $2M annual revenue, < $5M lifetime funding, < 100k monthly active users, and < $250k revenue attributable to the model (§3).
✅ Modify, fine-tune, quantize, prune, merge, adapt for permitted uses (§2.4).
⬆️ Enterprise use needs an AudarAI Enterprise License (contact sales) — required if you exceed any threshold, or for enterprise/production-at-scale deployment, model-as-a-service or APIs, High-Risk uses, or training a competing foundation model (§4, §5).
📋 Conditions: don't redistribute the original weights except by linking to the official repo (§8.2); pass on the license + notices with any modified redistribution; no trademark rights; and follow the Acceptable Use Restrictions (Schedule A).

The backbone is adapted from Qwen2.5 (Apache-2.0); the audar-codec decoder is distributed separately and builds on NeuCodec (see §15, Third-Party Components).

Note: the smaller Audar-TTS-V1-Flash tier is released under the more permissive AudarAI Open License v1.0 (unrestricted commercial use).

Responsible use

Clone voices only with the speaker's explicit consent. Do not use Audar-TTS to deceive, defraud, or impersonate real people or organizations, and comply with applicable law (§9).

Citation

@misc{audar-tts-2026,
  title  = {Audar-TTS: Arabic-First Expressive Speech Synthesis},
  author = {Audar AI Research Team},
  year   = {2026},
  note   = {https://www.audarai.com}
}

About AudarAI

Leading Arabic-First Multilingual Audio Intelligence

AudarAI starts with Arabic — and expands to the world.

We are building advanced multilingual audio intelligence that helps individuals, enterprises, and governments communicate across languages, cultures, and borders. By combining Arabic-first speech technology with global multilingual AI, AudarAI transforms voice into understanding, interaction, and connection.

Our work spans speech recognition, speech understanding, voice-enabled digital assistants, human-computer interaction, and intelligent audio systems designed for real-world impact. From empowering people to access technology in their native language to helping organizations communicate globally, AudarAI is shaping a future where every voice can be heard, understood, and connected.

Arabic-first. Multilingual by design. Human-centered at heart.

🌐 www.audarai.com · 🤗 Hugging Face · GitHub · contact@audarai.com

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GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit