onnx-community/Veena-Onnx

Veena - Text to Speech for Indian Languages

Veena is a state-of-the-art neural text-to-speech (TTS) model specifically designed for Indian languages, developed by Maya Research. Built on a Llama architecture backbone, Veena generates natural, expressive speech in Hindi and English with remarkable quality and ultra-low latency.

Model Overview

Veena is a 3B parameter autoregressive transformer model based on the Llama architecture. It is designed to synthesize high-quality speech from text in Hindi and English, including code-mixed scenarios. The model outputs audio at a 24kHz sampling rate using the SNAC neural codec.

• Model type: Autoregressive Transformer
• Base Architecture: Llama (3B parameters)
• Languages: Hindi, English
• Audio Codec: SNAC @ 24kHz
• License: Apache 2.0
• Developed by: Maya Research
• Model URL: https://huggingface.co/maya-research/veena

Key Features

• 4 Distinct Voices: kavya, agastya, maitri, and vinaya - each with unique vocal characteristics.
• Multilingual Support: Native Hindi and English capabilities with code-mixed support.
• Ultra-Fast Inference: Sub-80ms latency on H100-80GB GPUs.
• High-Quality Audio: 24kHz output with the SNAC neural codec.
• Production-Ready: Optimized for real-world deployment with 4-bit quantization support.

How to Get Started with the Model

Installation

To use Veena, you need to install the transformers, torch, torchaudio, snac, and bitsandbytes libraries.

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

Basic Usage

The following Python code demonstrates how to generate speech from text using Veena with 4-bit quantization for efficient inference.

<!-- ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from snac import SNAC import soundfile as sf

Model configuration for 4-bit inference

quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, )

Load model and tokenizer

model = AutoModelForCausalLM.from_pretrained( "maya-research/veena-tts", quantization_config=quantization_config, device_map="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

Initialize SNAC decoder

snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

Control token IDs (fixed for Veena)

START_OF_SPEECH_TOKEN = 128257 END_OF_SPEECH_TOKEN = 128258 START_OF_HUMAN_TOKEN = 128259 END_OF_HUMAN_TOKEN = 128260 START_OF_AI_TOKEN = 128261 END_OF_AI_TOKEN = 128262 AUDIO_CODE_BASE_OFFSET = 128266 INFO: Setting token_id of bos to 128000 INFO: Setting token_id of eos to 128009 INFO: Setting token_id of pad to 128263

INFO: Setting max_context_limit to 4096

Available speakers

speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9): """Generate speech from text using specified speaker voice"""

# Prepare input with speaker token
prompt = f"<spk_{speaker}> {text}"
prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

# Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
input_tokens = [
    START_OF_HUMAN_TOKEN,
    *prompt_tokens,
    END_OF_HUMAN_TOKEN,
    START_OF_AI_TOKEN,
    START_OF_SPEECH_TOKEN
]

input_ids = torch.tensor([input_tokens], device=model.device)

# Calculate max tokens based on text length
max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

# Generate audio tokens
with torch.no_grad():
    output = model.generate(
        input_ids,
        max_new_tokens=max_tokens,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        repetition_penalty=1.05,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
    )

# Extract SNAC tokens
generated_ids = output[0][len(input_tokens):].tolist()
snac_tokens = [
    token_id for token_id in generated_ids
    if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
]

if not snac_tokens:
    raise ValueError("No audio tokens generated")

# Decode audio
audio = decode_snac_tokens(snac_tokens, snac_model)
return audio

def decode_snac_tokens(snac_tokens, snac_model): """De-interleave and decode SNAC tokens to audio""" if not snac_tokens or len(snac_tokens) % 7 != 0: return None

# De-interleave tokens into 3 hierarchical levels
codes_lvl = [[] for _ in range(3)]
llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

for i in range(0, len(snac_tokens), 7):
    # Level 0: Coarse (1 token)
    codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
    # Level 1: Medium (2 tokens)
    codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
    codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
    # Level 2: Fine (4 tokens)
    codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
    codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
    codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
    codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

# Convert to tensors for SNAC decoder
hierarchical_codes = []
for lvl_codes in codes_lvl:
    tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
    if torch.any((tensor < 0) | (tensor > 4095)):
        raise ValueError("Invalid SNAC token values")
    hierarchical_codes.append(tensor)

# Decode with SNAC
with torch.no_grad():
    audio_hat = snac_model.decode(hierarchical_codes)

return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

--- Example Usage ---

Hindi

text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।" audio = generate_speech(text_hindi, speaker="kavya") sf.write("output_hindi_kavya.wav", audio, 24000)

English

text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices." audio = generate_speech(text_english, speaker="agastya") sf.write("output_english_agastya.wav", audio, 24000)

Code-mixed

text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।" audio = generate_speech(text_mixed, speaker="maitri") sf.write("output_mixed_maitri.wav", audio, 24000)

## Uses

Veena is ideal for a wide range of applications requiring high-quality, low-latency speech synthesis for Indian languages, including:

  * **Accessibility:** Screen readers and voice-enabled assistance for visually impaired users.
  * **Customer Service:** IVR systems, voice bots, and automated announcements.
  * **Content Creation:** Dubbing for videos, e-learning materials, and audiobooks.
  * **Automotive:** In-car navigation and infotainment systems.
  * **Edge Devices:** Voice-enabled smart devices and IoT applications.

## Technical Specifications

### Architecture

Veena leverages a 3B parameter transformer-based architecture with several key innovations:

  * **Base Architecture:** Llama-style autoregressive transformer (3B parameters)
  * **Audio Codec:** SNAC (24kHz) for high-quality audio token generation
  * **Speaker Conditioning:** Special speaker tokens (`<spk_kavya>`, `<spk_agastya>`, `<spk_maitri>`, `<spk_vinaya>`)
  * **Parameter-Efficient Training:** LoRA adaptation with differentiated ranks for attention and FFN modules.
  * **Context Length:** 2048 tokens

### Training

#### Training Infrastructure

  * **Hardware:** 8× NVIDIA H100 80GB GPUs
  * **Distributed Training:** DDP with optimized communication
  * **Precision:** BF16 mixed precision training with gradient checkpointing
  * **Memory Optimization:** 4-bit quantization with NF4 + double quantization

#### Training Configuration

  * **LoRA Configuration:**
      * `lora_rank_attention`: 192
      * `lora_rank_ffn`: 96
      * `lora_alpha`: 2× rank (384 for attention, 192 for FFN)
      * `lora_dropout`: 0.05
      * `target_modules`: `["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]`
      * `modules_to_save`: `["embed_tokens"]`
  * **Optimizer Configuration:**
      * `optimizer`: AdamW (8-bit)
      * `optimizer_betas`: (0.9, 0.98)
      * `optimizer_eps`: 1e-5
      * `learning_rate_peak`: 1e-4
      * `lr_scheduler`: cosine
      * `warmup_ratio`: 0.02
  * **Batch Configuration:**
      * `micro_batch_size`: 8
      * `gradient_accumulation_steps`: 4
      * `effective_batch_size`: 256

#### Training Data

Veena was trained on **proprietary, high-quality datasets** specifically curated for Indian language TTS.

  * **Data Volume:** 15,000+ utterances per speaker (60,000+ total)
  * **Languages:** Native Hindi and English utterances with code-mixed support
  * **Speaker Diversity:** 4 professional voice artists with distinct characteristics
  * **Audio Quality:** Studio-grade recordings at 24kHz sampling rate
  * **Content Diversity:** Conversational, narrative, expressive, and informational styles

**Note:** The training datasets are proprietary and not publicly available.

## Performance Benchmarks

| Metric                | Value                     |
| --------------------- | ------------------------- |
| Latency (H100-80GB)   | \<80ms                     |
| Latency (A100-40GB)   | \~120ms                    |
| Latency (RTX 4090)    | \~200ms                    |
| Real-time Factor      | 0.05x                     |
| Throughput            | \~170k tokens/s (8×H100)   |
| Audio Quality (MOS)   | 4.2/5.0                   |
| Speaker Similarity    | 92%                       |
| Intelligibility       | 98%                       |

## Risks, Limitations and Biases

  * **Language Support:** Currently supports only Hindi and English. Performance on other Indian languages is not guaranteed.
  * **Speaker Diversity:** Limited to 4 speaker voices, which may not represent the full diversity of Indian accents and dialects.
  * **Hardware Requirements:** Requires a GPU for real-time or near-real-time inference. CPU performance will be significantly slower.
  * **Input Length:** The model is limited to a maximum input length of 2048 tokens.
  * **Bias:** The model's performance and voice characteristics are a reflection of the proprietary training data. It may exhibit biases present in the data.

## Future Updates

We are actively working on expanding Veena's capabilities:

  * Support for Tamil, Telugu, Bengali, Marathi, and other Indian languages.
  * Additional speaker voices with regional accents.
  * Emotion and prosody control tokens.
  * Streaming inference support.
  * CPU optimization for edge deployment.

## Citing

If you use Veena in your research or applications, please cite:

```bibtex
@misc{veena2025,
  title={Veena: Open Source Text-to-Speech for Indian Languages},
  author={Maya Research Team},
  year={2025},
  publisher={HuggingFace},
  url={[https://huggingface.co/maya-research/veena-tts](https://huggingface.co/maya-research/veena-tts)}
}

Acknowledgments

We thank the open-source community and all contributors who made this project possible. Special thanks to the voice artists who provided high-quality recordings for training.