Svara TTS v1 -- OpenVINO INT4

High-Quality Multilingual Indic Text-to-Speech on Intel Hardware

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Overview

Svara TTS v1 is a 3.3B-parameter autoregressive text-to-speech model built on the LLaMA architecture, fine-tuned to generate natural-sounding speech across 19 Indic languages and Indian English. It uses the SNAC neural audio codec with 7-band interleaved codes to produce high-fidelity 24 kHz audio.

This is the OpenVINO INT4 quantized version, optimized for deployment on Intel hardware including CPUs, integrated GPUs, and NPUs (e.g., Intel Core Ultra / Panther Lake).

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Supported Languages

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Quick Start

Installation

pip install optimum[openvino] transformers torch snac soundfile

Basic Usage

from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer

model_id = "kenpath/svara-tts-v1-openvino-int4"

# Load model -- choose your Intel device
model = OVModelForCausalLM.from_pretrained(model_id, device="CPU")  # or "GPU", "NPU"
tokenizer = AutoTokenizer.from_pretrained(model_id)

Full Text-to-Speech Pipeline

import torch
import numpy as np
import soundfile as sf
from snac import SNAC

# ── Special tokens ──
BOS_TOKEN      = 128000
END_OF_TURN    = 128009
START_OF_SPEECH = 128257
END_OF_SPEECH  = 128258
START_OF_HUMAN = 128259
END_OF_HUMAN   = 128260
START_OF_AI    = 128261
AUDIO_TOKEN    = 156939
AUDIO_TOKENS_START = 128266
BAND_OFFSETS   = [128266, 132362, 136458, 140554, 144650, 148746, 152842]

# ── Build input ──
speaker = "hi_female"   # format: {lang_code}_{male|female}
text = "नमस्ते, आज का मौसम बहुत अच्छा है।"

prompt_text = f"{speaker}: {text}"
text_ids = tokenizer.encode(prompt_text, add_special_tokens=False)
input_ids = torch.tensor([[
    BOS_TOKEN, START_OF_HUMAN, AUDIO_TOKEN,
    *text_ids,
    END_OF_HUMAN, END_OF_TURN, START_OF_AI, START_OF_SPEECH
]])

# ── Generate audio tokens ──
output = model.generate(
    input_ids=input_ids,
    max_new_tokens=4096,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.1,
)

# ── Decode with SNAC ──
generated = output[0][input_ids.shape[1]:]
audio_tokens = [t.item() for t in generated if t.item() >= AUDIO_TOKENS_START]
audio_tokens = audio_tokens[:len(audio_tokens) // 7 * 7]  # align to 7-band frames

codes_0, codes_1, codes_2 = [], [], []
for i in range(0, len(audio_tokens), 7):
    frame = audio_tokens[i:i+7]
    codes_0.append(frame[0] - BAND_OFFSETS[0])
    codes_1.extend([frame[1] - BAND_OFFSETS[1], frame[4] - BAND_OFFSETS[4]])
    codes_2.extend([
        frame[2] - BAND_OFFSETS[2], frame[3] - BAND_OFFSETS[3],
        frame[5] - BAND_OFFSETS[5], frame[6] - BAND_OFFSETS[6],
    ])

snac = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval()
with torch.no_grad():
    audio = snac.decode(torch.tensor([codes_0, codes_1, codes_2]))
audio_np = audio.squeeze().cpu().numpy()

sf.write("output.wav", audio_np, 24000)
print(f"Saved output.wav ({len(audio_np)/24000:.1f}s)")

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Speaker Format

Speakers follow the pattern {language_code}_{gender}:

hi_female    # Hindi female
ta_male      # Tamil male
en_female    # Indian English female
bn_male      # Bengali male

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Intel Hardware Targets

INT4 quantization provides ~3.4x compression vs the original BF16 weights while maintaining speech quality across all supported languages.

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Model Architecture

LlamaForCausalLM (Modified for TTS)
├── Embedding: 156,940 tokens (text + 28,674 audio tokens)
├── 28 x Transformer Blocks
│   ├── Grouped-Query Attention (24 heads, 8 KV heads, dim 128)
│   ├── RMSNorm (eps=1e-5)
│   └── SwiGLU MLP (3072 → 8192 → 3072)
├── RoPE (LLaMA3-style, θ=500K, 128K context)
└── Tied LM Head → 156,940 logits
         ↓
    SNAC Decoder (24 kHz, 7-band, 4096 codebook)
         ↓
      Waveform

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Files

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Citation

@misc{svara-tts-v1-openvino-int4,
  title   = {Svara TTS v1 -- OpenVINO INT4},
  author  = {kenpath},
  year    = {2026},
  url     = {https://huggingface.co/kenpath/svara-tts-v1-openvino-int4},
  note    = {INT4 quantized OpenVINO conversion of kenpath/svara-tts-v1}
}

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Base Model | OpenVINO Docs | SNAC Codec