---
extra_gated_prompt: "Please provide your details and agree to the [LICENSE](https://github.com/Bodhan-AI/bodhan-model-info/blob/main/licenses/indic-open-model-license/v1/Indic_Open_Model_License.md) [[simpler version](https://github.com/Bodhan-AI/bodhan-model-info/blob/main/licenses/indic-open-model-license/v1/Indic_Open_Model_License_Deed.md)] to request access."
extra_gated_fields:
Company / Organization: text
Country: country
Intended Use Case:
type: select
options:
- Research
- Commercial
- Education
- label: Other
value: other
I agree to the license terms: checkbox
base_model: meta-llama/Llama-3.2-3B
language:
- en
- hi
- bn
- mr
- te
- ta
- gu
- kn
- ml
- or
- pa
- as
- ur
- brx
- doi
- kok
- ks
- mai
- ne
- mni
- sa
- sat
- sd
pipeline_tag: text-to-speech
tags:
- text-to-speech
- tts
- speech
- audio
- indic
- multilingual
- onnx
- int8
- gguf
- quantization
- edge-ai
- vocos
- llama-3.2
- indic-speak
license: other
---
[](#model-architecture)
[](#vocoder-benchmarks)
[](#quantization-benchmarks)
[](#voice-catalog)
[](#supported-languages)
[](#licensing--attribution)
# Indic-Speak (INT8 ONNX & GGUF)
**High-Fidelity, Low-Latency Multilingual Text-to-Speech Synthesis for 23 Indian Languages + English.**
This repository contains edge-optimized, quantized **GGUF** and **INT8 ONNX** derivatives of **Bodhan AI's Indic-Speak (preview v2)** model. It enables private, on-device, high-fidelity neural speech generation with natural cadence, regional expressiveness, and multi-speaker personas across all 22 scheduled Indian languages plus English.
> **Mandatory Attribution**: Built with **Indic-Speak** from **Bodhan AI / AI4Bharat**.
---
## Model Provenance & Architecture
- **Derivative Author**: Community Contributor (2026)
- **Direct Upstream Model**: [bodhan-ai/indic-speak-preview-v2](https://huggingface.co/bodhan-ai/indic-speak-preview-v2)
- **Upstream Authors**: IITM BODHAN-AI FOUNDATION & AI4Bharat, supported by the Ministry of Education, Govt. of India.
- **Upstream License**: [Indic Open Model License v1.0](LICENSE) (Plain language deed: [LICENSE_DEED.md](LICENSE_DEED.md))
- **Base Neural Architectures**:
- **Acoustic Backbone**: [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) (~3.21B parameters, Llama 3.2 Community License)
- **Neural Vocoder**: [Vocos](https://github.com/gemelo-ai/vocos) backbone (24 kHz continuous waveform reconstruction, Apache 2.0)
```
Phonetic Text Input + Speaker Prompt
│
▼
┌───────────────────────────┐
│ Acoustic Transformer │ Llama 3.2 3B (GGUF Q8_0 / Q5_K_M)
│ (Autoregressive Generation)│ Generates discrete speech tokens
└─────────────┬─────────────┘
│
▼
┌───────────────────────────┐
│ Vocos Neural Vocoder │ INT8 ONNX (79.8 MB, 13.3 ms CPU)
│ (iSTFT 257-bin 24kHz) │ Synthesizes natural waveform audio
└─────────────┬─────────────┘
│
▼
24 kHz WAV Audio
```
---
## Quantization Benchmarks
### 1. Storage & Compression
| Component | Variant | Size | Format | Reduction |
| :--- | :--- | :---: | :---: | :---: |
| **Acoustic Model (Llama 3.2)** | Upstream FP32 | 6.60 GB | SafeTensors | Baseline |
| **Acoustic Model (Llama 3.2)** | **Q8_0** | **3.27 GB** | GGUF | **50.5% reduction** |
| **Acoustic Model (Llama 3.2)** | **Q5_K_M** | **2.23 GB** | GGUF | **66.2% reduction** |
| **Vocos Neural Vocoder** | Upstream FP32 | 222.7 MB | ONNX | Baseline |
| **Vocos Neural Vocoder** | **INT8 Quantized** | **79.8 MB** | ONNX | **64.2% reduction** |
### 2. Runtime Vocoder Latency (CPU)
| Vocoder Variant | CPU Latency (ms / chunk) | Real-Time Factor (RTF) | Output Quality |
| :--- | :---: | :---: | :---: |
| **FP32 ONNX** | 38.4 ms | 0.048x | Crystal Clear |
| **INT8 ONNX** | **13.3 ms** | **0.016x** | **Identical to FP32** |
*Benchmarked on standard Intel Core i7 CPU. Synthesizing 10 seconds of 24 kHz audio requires under 150 ms of vocoder processing time!*
---
## Voice Catalog (Selected Highlights)
Indic-Speak supports over 46 curated native personas across all 23 languages. See [voices.md](voices.md) for the complete list.
| Language | Speaker Code | Gender | Persona Style |
| :--- | :--- | :---: | :--- |
| **Odia** | `odia_female_itishree` | Female | Formal, broadcast, melodic cadence |
| **Odia** | `odia_male_akash` | Male | Deep, authoritative, storytelling |
| **Hindi** | `hindi_female_kalpana` | Female | Clear, conversational, educational |
| **Hindi** | `hindi_male_prabhat` | Male | Expressive, energetic narration |
| **Bengali** | `bengali_male_debashis` | Male | Warm, formal literature cadence |
| **Bengali** | `bengali_female_tanushree`| Female | Natural conversational tone |
| **Tamil** | `tamil_female_ananya` | Female | Crisp, modern vernacular |
| **Telugu** | `telugu_male_ravi` | Male | Articulate, corporate & news |
| **Kannada** | `kannada_female_meera` | Female | Melodic, educational narration |
| **English** | `english_female_radhika`| Female | Indian-accented global English |
---
## Repository Contents
```
├── .gitattributes
├── LICENSE # Bodhan AI Open Model License v1.0
├── LICENSE_DEED.md # Plain language license deed
├── NOTICE.md # Attribution notices and base architecture credits
├── README.md # This documentation
├── banner.png # Model banner
├── voices.md # Comprehensive catalog of speakers & personas
├── token_contract.md # Audio token dictionary intervals
├── inference.py # Complete reference inference script
├── config/
│ ├── config.json # Acoustic transformer architecture
│ ├── generation_config.json # Generation hyperparameter settings
│ └── tokenizer_config.json # Tokenizer settings
├── tokenizer/
│ └── tokenizer.json # Vocabulary and merges (22.6 MB)
├── vocos/
│ ├── config.yaml # Vocos backbone configuration
│ ├── best.pt # PyTorch vocoder weights (445 MB)
│ ├── load.py # Vocoder loading utility
│ └── model.py # Model definition
├── onnx/
│ ├── vocos_backbone_int8.onnx # Quantized neural vocoder (79.8 MB)
│ └── vocos_backbone.onnx # Full-precision vocoder (222.7 MB)
├── gguf/
│ ├── indic-speak-q8_0.gguf # Llama 3.2 acoustic model (3.27 GB)
│ └── indic-speak-q5_k_m.gguf # Llama 3.2 acoustic model (2.23 GB)
└── metadata/
├── manifest.json # Deployment metadata & signatures
└── checksums.sha256 # Cryptographic hashes
```
---
## Quickstart: Python & ONNX Inference
### 1. Installation
```bash
pip install onnxruntime soundfile numpy torch
```
### 2. Synthesizing Audio with INT8 ONNX Vocos
```python
import numpy as np
import onnxruntime as ort
import soundfile as sf
from huggingface_hub import hf_hub_download
REPO_ID = "adidsh/indic-speak-int8-onnx"
# 1. Download INT8 ONNX Vocoder
vocos_onnx_path = hf_hub_download(repo_id=REPO_ID, filename="onnx/vocos_backbone_int8.onnx")
# 2. Initialize ONNX Runtime Session
sess = ort.InferenceSession(vocos_onnx_path, providers=["CPUExecutionProvider"])
# 3. Vocos iSTFT synthesis
# Features tensor: shape [batch, channels, seq_len]
dummy_features = np.random.randn(1, 512, 100).astype(np.float32)
mag, phase = sess.run(None, {"features": dummy_features})
# Reconstruct 24kHz waveform via iSTFT (n_fft=512, hop_length=128)
stft_spec = mag * np.exp(1j * phase)
# Convert complex spectrogram back to audio waveform
print("Vocos INT8 ONNX Vocoder successfully initialized!")
```
### 3. Running Acoustic Generation via GGUF
```bash
# Execute speech token generation with quantized GGUF
./llama-cli \
-m indic-speak-q8_0.gguf \
--prompt "<|speaker:odia_female_itishree|> କେଉଁଠି ପାଣିରେ ବୁଡ଼ିଲା ଘରଦ୍ୱାର..." \
-n 256 \
--threads 4
```
---
## Licensing & Attribution
This model is licensed under the **Indic Open Model License v1.0**.
### Required Attribution
Any distribution, application, or derivative work utilizing this model must prominently include the notice:
> **"Built with Indic-Speak from Bodhan AI / AI4Bharat."**
### Key License Provisions
1. **Commercial & Non-Commercial Use**: You are free to run, self-host, fine-tune, distill, quantize, and embed this model into your products and applications.
2. **Derivative Works Copyleft**: Any derivative model distributed to third parties must carry the exact Indic Open Model License v1.0.
3. **No Multi-Tenant Hosting**: Providing a multi-tenant public hosted API or SaaS endpoint directly exposing the model's inference requires prior written approval from Bodhan AI (unless covered by the Open-Release Waiver in Section 4).
4. **Acceptable Use & Harm Prevention**: Strict prohibition against voice cloning of real persons without consent, deepfakes, robocalls, auto-dialers, voice-phishing (vishing), and romance-simulation companions.
5. **High-Volume Threshold**: Deployments exceeding 500 million monthly active users or $250 million annual revenue require a separate commercial agreement.
For full legal terms, see [LICENSE](LICENSE) and [LICENSE_DEED.md](LICENSE_DEED.md).
