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Model: maya-research/Veena Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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- hi
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library_name: transformers
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tags:
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- text-to-speech
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- tts
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- hindi
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- english
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- llama
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- audio
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- speech
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- india
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datasets:
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- proprietary
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pipeline_tag: text-to-speech
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co2_eq_emissions:
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emissions: 0
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source: "Not specified"
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training_type: "unknown"
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geographical_location: "unknown"
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---
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# Veena - Text to Speech for Indian Languages
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Veena is a state-of-the-art neural text-to-speech (TTS) model developed by Maya Research, designed for English and Indian languages. Built on a Llama architecture backbone, Veena generates natural, expressive speech with emotional tone, remarkable quality, and ultra-low latency. It represents the foundation of our voice intelligence work, bringing human-like voice to the two most spoken languages in the world.
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## Model Overview
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**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.
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* **Model type:** Autoregressive Transformer
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* **Base Architecture:** Llama (3B parameters)
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* **Languages:** Hindi, English
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* **Audio Codec:** SNAC @ 24kHz
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* **License:** Apache 2.0
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* **Developed by:** Maya Research
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* **Model URL:** [https://huggingface.co/maya-research/veena](https://huggingface.co/maya-research/veena)
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## Key Features
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* **4 Distinct Voices:** `kavya`, `agastya`, `maitri`, and `vinaya` - each with unique vocal characteristics.
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* **Multilingual Support:** Native Hindi and English capabilities with code-mixed support.
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* **Ultra-Fast Inference:** Sub-80ms latency on H100-80GB GPUs.
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* **High-Quality Audio:** 24kHz output with the SNAC neural codec.
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* **Production-Ready:** Optimized for real-world deployment with 4-bit quantization support.
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## How to Get Started with the Model
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### Installation
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To use Veena, you need to install the `transformers`, `torch`, `torchaudio`, `snac`, and `bitsandbytes` libraries.
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```bash
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pip install transformers torch torchaudio
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pip install snac bitsandbytes # For audio decoding and quantization
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```
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### Basic Usage
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The following Python code demonstrates how to generate speech from text using Veena with 4-bit quantization for efficient inference.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from snac import SNAC
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import soundfile as sf
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# Model configuration for 4-bit inference
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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)
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# Load model and tokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"maya-research/veena-tts",
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quantization_config=quantization_config,
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device_map="auto",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)
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# Initialize SNAC decoder
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snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()
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# Control token IDs (fixed for Veena)
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START_OF_SPEECH_TOKEN = 128257
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END_OF_SPEECH_TOKEN = 128258
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START_OF_HUMAN_TOKEN = 128259
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END_OF_HUMAN_TOKEN = 128260
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START_OF_AI_TOKEN = 128261
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END_OF_AI_TOKEN = 128262
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AUDIO_CODE_BASE_OFFSET = 128266
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# Available speakers
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speakers = ["kavya", "agastya", "maitri", "vinaya"]
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def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
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"""Generate speech from text using specified speaker voice"""
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# Prepare input with speaker token
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prompt = f"<spk_{speaker}> {text}"
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prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)
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# Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
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input_tokens = [
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START_OF_HUMAN_TOKEN,
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*prompt_tokens,
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END_OF_HUMAN_TOKEN,
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START_OF_AI_TOKEN,
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START_OF_SPEECH_TOKEN
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]
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input_ids = torch.tensor([input_tokens], device=model.device)
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# Calculate max tokens based on text length
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max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)
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# Generate audio tokens
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with torch.no_grad():
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output = model.generate(
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input_ids,
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max_new_tokens=max_tokens,
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do_sample=True,
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temperature=temperature,
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top_p=top_p,
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repetition_penalty=1.05,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
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)
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# Extract SNAC tokens
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generated_ids = output[0][len(input_tokens):].tolist()
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snac_tokens = [
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token_id for token_id in generated_ids
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if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
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]
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if not snac_tokens:
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raise ValueError("No audio tokens generated")
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# Decode audio
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audio = decode_snac_tokens(snac_tokens, snac_model)
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return audio
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def decode_snac_tokens(snac_tokens, snac_model):
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"""De-interleave and decode SNAC tokens to audio"""
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if not snac_tokens or len(snac_tokens) % 7 != 0:
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return None
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# Get the device of the SNAC model. Fixed by Shresth to run on colab notebook :)
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snac_device = next(snac_model.parameters()).device
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# De-interleave tokens into 3 hierarchical levels
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codes_lvl = [[] for _ in range(3)]
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llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]
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for i in range(0, len(snac_tokens), 7):
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# Level 0: Coarse (1 token)
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codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
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# Level 1: Medium (2 tokens)
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codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
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codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
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# Level 2: Fine (4 tokens)
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codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
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codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
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codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
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codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])
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# Convert to tensors for SNAC decoder
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hierarchical_codes = []
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for lvl_codes in codes_lvl:
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tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_device).unsqueeze(0)
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if torch.any((tensor < 0) | (tensor > 4095)):
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raise ValueError("Invalid SNAC token values")
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hierarchical_codes.append(tensor)
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# Decode with SNAC
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with torch.no_grad():
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audio_hat = snac_model.decode(hierarchical_codes)
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return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()
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# --- Example Usage ---
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# Hindi
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text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
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audio = generate_speech(text_hindi, speaker="kavya")
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sf.write("output_hindi_kavya.wav", audio, 24000)
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# English
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text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
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audio = generate_speech(text_english, speaker="agastya")
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sf.write("output_english_agastya.wav", audio, 24000)
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# Code-mixed
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text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
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audio = generate_speech(text_mixed, speaker="maitri")
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sf.write("output_mixed_maitri.wav", audio, 24000)
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```
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## Uses
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Veena is ideal for a wide range of applications requiring high-quality, low-latency speech synthesis for Indian languages, including:
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* **Accessibility:** Screen readers and voice-enabled assistance for visually impaired users.
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* **Customer Service:** IVR systems, voice bots, and automated announcements.
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* **Content Creation:** Dubbing for videos, e-learning materials, and audiobooks.
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* **Automotive:** In-car navigation and infotainment systems.
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* **Edge Devices:** Voice-enabled smart devices and IoT applications.
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## Technical Specifications
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### Architecture
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Veena leverages a 3B parameter transformer-based architecture with several key innovations:
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* **Base Architecture:** Llama-style autoregressive transformer (3B parameters)
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* **Audio Codec:** SNAC (24kHz) for high-quality audio token generation
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* **Speaker Conditioning:** Special speaker tokens (`<spk_kavya>`, `<spk_agastya>`, `<spk_maitri>`, `<spk_vinaya>`)
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* **Parameter-Efficient Training:** LoRA adaptation with differentiated ranks for attention and FFN modules.
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* **Context Length:** 2048 tokens
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### Training
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#### Training Infrastructure
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* **Hardware:** 8× NVIDIA H100 80GB GPUs
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* **Distributed Training:** DDP with optimized communication
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* **Precision:** BF16 mixed precision training with gradient checkpointing
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* **Memory Optimization:** 4-bit quantization with NF4 + double quantization
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#### Training Configuration
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* **LoRA Configuration:**
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* `lora_rank_attention`: 192
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* `lora_rank_ffn`: 96
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* `lora_alpha`: 2× rank (384 for attention, 192 for FFN)
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* `lora_dropout`: 0.05
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* `target_modules`: `["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]`
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* `modules_to_save`: `["embed_tokens"]`
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* **Optimizer Configuration:**
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* `optimizer`: AdamW (8-bit)
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* `optimizer_betas`: (0.9, 0.98)
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* `optimizer_eps`: 1e-5
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* `learning_rate_peak`: 1e-4
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* `lr_scheduler`: cosine
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* `warmup_ratio`: 0.02
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* **Batch Configuration:**
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* `micro_batch_size`: 8
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* `gradient_accumulation_steps`: 4
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* `effective_batch_size`: 256
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#### Training Data
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Veena was trained on **proprietary, high-quality datasets** specifically curated for Indian language TTS.
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* **Data Volume:** 15,000+ utterances per speaker (60,000+ total)
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* **Languages:** Native Hindi and English utterances with code-mixed support
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* **Speaker Diversity:** 4 professional voice artists with distinct characteristics
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* **Audio Quality:** Studio-grade recordings at 24kHz sampling rate
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* **Content Diversity:** Conversational, narrative, expressive, and informational styles
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**Note:** The training datasets are proprietary and not publicly available.
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## Performance Benchmarks
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| Metric | Value |
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| --------------------- | ------------------------- |
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| Latency (H100-80GB) | \<80ms |
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| Latency (A100-40GB) | \~120ms |
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| Latency (RTX 4090) | \~200ms |
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| Real-time Factor | 0.05x |
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| Throughput | \~170k tokens/s (8×H100) |
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| Audio Quality (MOS) | 4.2/5.0 |
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| Speaker Similarity | 92% |
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| Intelligibility | 98% |
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## Risks, Limitations and Biases
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* **Language Support:** Currently supports only Hindi and English. Performance on other Indian languages is not guaranteed.
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* **Speaker Diversity:** Limited to 4 speaker voices, which may not represent the full diversity of Indian accents and dialects.
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* **Hardware Requirements:** Requires a GPU for real-time or near-real-time inference. CPU performance will be significantly slower.
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* **Input Length:** The model is limited to a maximum input length of 2048 tokens.
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* **Bias:** The model's performance and voice characteristics are a reflection of the proprietary training data. It may exhibit biases present in the data.
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## Future Updates
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We are actively working on expanding Veena's capabilities:
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* Support for Tamil, Telugu, Bengali, Marathi, and other Indian languages.
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* Additional speaker voices with regional accents.
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* Emotion and prosody control tokens.
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* Streaming inference support.
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* CPU optimization for edge deployment.
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## Citing
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If you use Veena in your research or applications, please cite:
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```bibtex
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@misc{veena2025,
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title={Veena: Open Source Text-to-Speech for Indian Languages},
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author={Maya Research Team},
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year={2025},
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publisher={HuggingFace},
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url={[https://huggingface.co/maya-research/veena-tts](https://huggingface.co/maya-research/veena-tts)}
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}
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```
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## Acknowledgments
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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.
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93
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
|
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{%- endif %}
|
||||
{%- if not tools_in_user_message is defined %}
|
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{%- set tools_in_user_message = true %}
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{%- endif %}
|
||||
{%- if not date_string is defined %}
|
||||
{%- if strftime_now is defined %}
|
||||
{%- set date_string = strftime_now("%d %b %Y") %}
|
||||
{%- else %}
|
||||
{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if not tools is defined %}
|
||||
{%- set tools = none %}
|
||||
{%- endif %}
|
||||
|
||||
{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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||||
{%- set messages = messages[1:] %}
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||||
{%- else %}
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||||
{%- set system_message = "" %}
|
||||
{%- endif %}
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||||
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||||
{#- System message #}
|
||||
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
|
||||
{%- if tools is not none %}
|
||||
{{- "Environment: ipython\n" }}
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||||
{%- endif %}
|
||||
{{- "Cutting Knowledge Date: December 2023\n" }}
|
||||
{{- "Today Date: " + date_string + "\n\n" }}
|
||||
{%- if tools is not none and not tools_in_user_message %}
|
||||
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- system_message }}
|
||||
{{- "<|eot_id|>" }}
|
||||
|
||||
{#- Custom tools are passed in a user message with some extra guidance #}
|
||||
{%- if tools_in_user_message and not tools is none %}
|
||||
{#- Extract the first user message so we can plug it in here #}
|
||||
{%- if messages | length != 0 %}
|
||||
{%- set first_user_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
|
||||
{%- endif %}
|
||||
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
||||
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
||||
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{{- first_user_message + "<|eot_id|>"}}
|
||||
{%- endif %}
|
||||
|
||||
{%- for message in messages %}
|
||||
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
|
||||
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
|
||||
{%- elif 'tool_calls' in message %}
|
||||
{%- if not message.tool_calls|length == 1 %}
|
||||
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
||||
{%- endif %}
|
||||
{%- set tool_call = message.tool_calls[0].function %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
||||
{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- "}" }}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- elif message.role == "tool" or message.role == "ipython" %}
|
||||
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
||||
{%- if message.content is mapping or message.content is iterable %}
|
||||
{{- message.content | tojson }}
|
||||
{%- else %}
|
||||
{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
{%- endif %}
|
||||
35
config.json
Normal file
35
config.json
Normal file
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 128000,
|
||||
"eos_token_id": 128001,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3072,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 8192,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 24,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 8,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": {
|
||||
"factor": 32.0,
|
||||
"high_freq_factor": 4.0,
|
||||
"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_type": "llama3"
|
||||
},
|
||||
"rope_theta": 500000.0,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.52.4",
|
||||
"use_cache": true,
|
||||
"vocab_size": 156951
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-to-speech", "allow_remote": true}
|
||||
9
generation_config.json
Normal file
9
generation_config.json
Normal file
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 128000,
|
||||
"do_sample": true,
|
||||
"eos_token_id": 128001,
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.52.4"
|
||||
}
|
||||
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 4991105552
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3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 2575032728
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262
model.safetensors.index.json
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262
model.safetensors.index.json
Normal file
@@ -0,0 +1,262 @@
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{
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36
special_tokens_map.json
Normal file
36
special_tokens_map.json
Normal file
@@ -0,0 +1,36 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
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}
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
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version https://git-lfs.github.com/spec/v1
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size 22851621
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3
tokenizer_config.json
Normal file
3
tokenizer_config.json
Normal file
@@ -0,0 +1,3 @@
|
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version https://git-lfs.github.com/spec/v1
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size 5405560
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||||
Reference in New Issue
Block a user