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rumik-oss-1-mlx-8bit
rumik-ai/rumik-oss-1-mlx-8bit
rumik-oss-1-mlx-8bit is a text-to-speech model from rumik-ai. Use it when you need text read aloud. It is set up for mlx-audio. The card lists the license as cc-by-nc-4.0.
<p align="center" <img src="https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/rumik-logo.png" alt="rumik.ai" width="100%" </p
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3.4B
3.6 GB on disk
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.safetensors3.6 GB · 99%
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How the weights are stored.
U323.4B · 100%
Base models
Model card
From the Hugging Face model README
<p align="center">
<img src="https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/rumik-logo.png" alt="rumik.ai" width="100%">
</p>
<p align="center">
<a href="https://huggingface.co/rumik-ai/rumik-oss-1"><img src="https://img.shields.io/badge/original_model-rumik--oss_1-555555" alt="original model"></a>
<a href="https://rumik.ai/research/rumik-oss"><img src="https://img.shields.io/badge/blog-overview-555555" alt="blog"></a>
<a href="https://github.com/Blaizzy/mlx-audio"><img src="https://img.shields.io/badge/runs_with-mlx--audio-000000" alt="mlx-audio"></a>
</p>
rumik-oss 1 · MLX 8-bit
8-bit MLX quantization of rumik-oss 1, a 3B multilingual text-to-speech model for 22 Indic languages + English with expressive delivery control and inline vocalizations, producing 24 kHz audio. Runs on Apple silicon with mlx-audio.
Recommended. Near-lossless: 96% next-token agreement with the bf16 reference.
this export
| quantization | 8-bit affine, group size 64, stop predictor kept in bf16 |
| size on disk | 3.6 GB |
| peak memory | 3.8 GB |
| decode speed, M5 MacBook Air (16 GB) | 33-40 tokens/s (0.33-0.40x real time) |
| next-token agreement with bf16 | 96% |
100 audio tokens make one second of speech. The Mimi codec is downloaded automatically on first use.
usage
pip install mlx-audio
from mlx_audio.tts.utils import load
model = load("rumik-ai/rumik-oss-1-mlx-8bit")
for chunk in model.generate(
"नमस्ते, आज आपका दिन कैसा रहा?",
voice="Ira",
instruct="happy, Hindi accent, steady pace",
stream=True,
):
play(chunk.audio) # 24 kHz float32
python -m mlx_audio.tts.generate --model rumik-ai/rumik-oss-1-mlx-8bit \
--text "नमस्ते, आज आपका दिन कैसा रहा?" --voice Ira \
--instruct "happy, Hindi accent, steady pace" --stream
controls
| control | values |
|---|---|
| voice | Ira, Aisha, Siya, Zoya |
| tone | happy, sad, angry, excited, professional |
| accent | Hindi, Telugu, Tamil, Kannada, Bengali, Punjabi, Indian English |
| pace | slow, fast, steady |
| inline | <laugh>, <chuckle>, <sigh> |
The description can also be written inline: <description="excited, Hindi accent, fast pace"> जल्दी आओ! <laugh>
samples
<audio controls preload="none" src="https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/samples/ira.wav"></audio> <audio controls preload="none" src="https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/samples/aisha.wav"></audio> <audio controls preload="none" src="https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/samples/siya.wav"></audio> <audio controls preload="none" src="https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/samples/zoya.wav"></audio>
Samples above are from the original bf16 model; see the original card for benchmarks (IndicEmo, NoVA, WER/CER) and limitations.
license
Research and non-commercial use under tiny aya fire's CC-BY-NC 4.0 with acceptable-use addendum, unchanged from the original. LICENSE and NOTICE are included; this repo adds 8-bit weight quantization as a modification. The Mimi codec is CC-BY-4.0.
citation
@unpublished{govindu2026rumikoss1,
title = {{rumik-oss 1 technical report}},
author = {Govindu Pranav and Anant Shukla and Suryansh Shakya and Aman Anand and Vatsal Bharti},
year = {2026},
note = {In preparation}
}
- Task
- Text-to-Speech
- Library
- mlx-audio
- Type
- rumik_oss
- License
- cc-by-nc-4.0
- Languages
- hi, bn, ta, te, mr, gu
- Created
- Sep 9, 2026
- Updated
- Sep 9, 2026