base_model: kenpath/svara-tts-v1
models:
- kenpath/svara-tts-v1
- canopylabs/3b-hi-ft-research_release
- mlx-community/svara-tts-v1-4bit
- mlx-community/svara-tts-v1-8bit
license: apache-2.0
language:
- hi
- bn
- mr
- te
- kn
- bho
- mag
- hne
- mai
- as
- brx
- doi
- gu
- ml
- pa
- ta
- ne
- sa
- en
tags:
- text-to-speech
- speech-synthesis
- multilingual
- indic
- orpheus
- snac
- mlx
- mlx-audio
task_categories:
- text-to-speech
pipeline_tag: text-to-speech
pretty_name: Svara-TTS v1 (MLX, bfloat16)
datasets:
- SYSPIN
- RASA
- IndicTTS
- SPICOR
library_name: mlx
Svara-TTS v1 — MLX bfloat16
Parent model:
kenpath/svara-tts-v1— full upstream weights, model card, training data, and evaluation. All credit for the model itself goes to the Kenpath team. This repo only contains an MLX-format conversion for inference on Apple Silicon.Orpheus base:
canopylabs/3b-hi-ft-research_release— Canopy Labs' Orpheus Hindi research release, which Svara was fine-tuned from.
Full-precision (bfloat16) MLX port of kenpath/svara-tts-v1 — an autoregressive multilingual text-to-speech model for 19 Indian languages, in the Orpheus / SNAC family. Same numerical precision as upstream, repackaged in MLX-native format (~6.6 GB sharded safetensors).
For smaller memory footprints, use the 4-bit or 8-bit quantized variants linked below.
Built for mlx-audio on Apple Silicon.
Usage
Requires mlx-audio with TTS extras:
pip install "mlx-audio[tts]"
Python
import numpy as np
import soundfile as sf
import mlx.core as mx
from mlx_audio.tts.utils import load_model
model = load_model("mlx-community/svara-tts-v1")
chunks = []
for result in model.generate(
text="नमस्ते, आप कैसे हैं? मैं ठीक हूँ।",
voice="Hindi (Female)",
temperature=0.75,
top_p=0.9,
top_k=40,
repetition_penalty=1.1,
max_tokens=1200,
):
chunks.append(result.audio)
audio = mx.concatenate(chunks, axis=0)
sf.write("hello_hi.wav", np.asarray(audio), model.sample_rate) # 24 kHz
CLI
mlx_audio.tts.generate \
--model mlx-community/svara-tts-v1 \
--text "नमस्ते, आप कैसे हैं?" \
--voice "Hindi (Female)" \
--temperature 0.75 \
--top_p 0.9
Voices
Use a string of the form "<Language Name> (<Gender>)":
| Language | Voices |
|---|---|
| Hindi | Hindi (Male), Hindi (Female) |
| Bengali | Bengali (Male), Bengali (Female) |
| Marathi | Marathi (Male), Marathi (Female) |
| Telugu | Telugu (Male), Telugu (Female) |
| Kannada | Kannada (Male), Kannada (Female) |
| Tamil | Tamil (Male), Tamil (Female) |
| Malayalam | Malayalam (Male), Malayalam (Female) |
| Gujarati | Gujarati (Male), Gujarati (Female) |
| Punjabi | Punjabi (Male), Punjabi (Female) |
| Assamese | Assamese (Male), Assamese (Female) |
| Bhojpuri | Bhojpuri (Male), Bhojpuri (Female) |
| Magahi | Magahi (Male), Magahi (Female) |
| Maithili | Maithili (Male), Maithili (Female) |
| Chhattisgarhi | Chhattisgarhi (Male), Chhattisgarhi (Female) |
| Bodo | Bodo (Male), Bodo (Female) |
| Dogri | Dogri (Male), Dogri (Female) |
| Nepali | Nepali (Male), Nepali (Female) |
| Sanskrit | Sanskrit (Male), Sanskrit (Female) |
| English (Indian) | English (Indian) (Male), English (Indian) (Female) |
Total: 38 voices across 19 languages.
Sampling Recommendations
The upstream svara-tts-inference repo uses these defaults; they're a good starting point:
| Parameter | Value |
|---|---|
temperature |
0.75 |
top_p |
0.9 |
top_k |
40 |
repetition_penalty |
1.1 |
max_tokens |
1200–2048 |
Architecture
- Backbone: Llama-3.2-3B (fine-tuned from
canopylabs/3b-hi-ft-research_release, Canopy's Orpheus Hindi base). - Codec: SNAC 24 kHz — 3-level hierarchical RVQ, 7 codes per ~10 ms frame. Loaded automatically by
mlx-audio. - Output: 24 kHz mono PCM.
Other Quants
- 8-bit MLX:
mlx-community/svara-tts-v1-8bit(~3.5 GB) - 4-bit MLX:
mlx-community/svara-tts-v1-4bit(~1.9 GB)
License
Apache 2.0 — see base model card for full details.