maya1

Maya-1 is a state-of-the-art speech model for expressive voice generation, designed to capture human emotion and enable precise voice design.

Maya-1 was developed at Maya Research by Dheemanth Reddy Bhumireddy Singa Reddy (Co-founder & CEO) and Bharath Kumar Kakumani (Co-founder & CTO).

try it: Playground

What it does:

• Create any voice you can imagine — a 20s British girl, an American guy, or a full-blown demon.
• Make it feel real with emotion tags: laugh, cry, whisper, rage, sigh, gasp.
• It streams instantly, sounds alive, 3B parameters, runs on single GPU
• Outperforms top proprietary models. and Developed by Maya Research.

Demos

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Example 1: Energetic Female Event Host

Voice Description:

Female, in her 30s with an American accent and is an event host, energetic, clear diction

Text:

Wow. This place looks even better than I imagined. How did they set all this up so perfectly? The lights, the music, everything feels magical. I can't stop smiling right now.

Audio Output:

Example 2: Dark Villain with Anger

Voice Description:

Dark villain character, Male voice in their 40s with a British accent. low pitch, gravelly timbre, slow pacing, angry tone at high intensity.

Text:

Welcome back to another episode of our podcast!  Today we are diving into an absolutely fascinating topic

Audio Output:

Example 3: Demon Character (Screaming Emotion)

Voice Description:

Demon character, Male voice in their 30s with a Middle Eastern accent. screaming tone at high intensity.

Text:

You dare challenge me, mortal  how amusing. Your kind always thinks they can win

Audio Output:

Example 4: Mythical Goddess with Crying Emotion

Voice Description:

Mythical godlike magical character, Female voice in their 30s slow pacing, curious tone at medium intensity.

Text:

After all we went through to pull him out of that mess  I can't believe he was the traitor

Audio Output:

Why Maya1 is Different: Voice Design Features That Matter

1. Natural Language Voice Control

Describe voices like you would brief a voice actor:

No complex parameters. No training data. Just describe and generate.

2. Inline Emotion Tags for Expressive Speech

Add emotions exactly where they belong in your text:

Our new update  finally ships with the feature you asked for.

Supported Emotions: and 12+ more.

3. Streaming Audio Generation

Real-time voice synthesis with SNAC neural codec (~0.98 kbps). Perfect for:

• Voice assistants
• Interactive AI agents
• Live content generation
• Game characters
• Podcasts and audiobooks

4. Production-Ready Infrastructure

• Runs on single GPU
• vLLM integration for scale
• Automatic prefix caching for efficiency
• 24 kHz audio output
• WebAudio compatible for browser playback

How to Use maya1: Download and Run in Minutes

Quick Start: Generate Voice with Emotions

#!/usr/bin/env python3

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from snac import SNAC
import soundfile as sf
import numpy as np

CODE_START_TOKEN_ID = 128257
CODE_END_TOKEN_ID = 128258
CODE_TOKEN_OFFSET = 128266
SNAC_MIN_ID = 128266
SNAC_MAX_ID = 156937
SNAC_TOKENS_PER_FRAME = 7

SOH_ID = 128259
EOH_ID = 128260
SOA_ID = 128261
BOS_ID = 128000
TEXT_EOT_ID = 128009

def build_prompt(tokenizer, description: str, text: str) -> str:
    """Build formatted prompt for Maya1."""
    soh_token = tokenizer.decode([SOH_ID])
    eoh_token = tokenizer.decode([EOH_ID])
    soa_token = tokenizer.decode([SOA_ID])
    sos_token = tokenizer.decode([CODE_START_TOKEN_ID])
    eot_token = tokenizer.decode([TEXT_EOT_ID])
    bos_token = tokenizer.bos_token

    formatted_text = f' {text}'

    prompt = (
        soh_token + bos_token + formatted_text + eot_token +
        eoh_token + soa_token + sos_token
    )

    return prompt

def extract_snac_codes(token_ids: list) -> list:
    """Extract SNAC codes from generated tokens."""
    try:
        eos_idx = token_ids.index(CODE_END_TOKEN_ID)
    except ValueError:
        eos_idx = len(token_ids)

    snac_codes = [
        token_id for token_id in token_ids[:eos_idx]
        if SNAC_MIN_ID <= token_id <= SNAC_MAX_ID
    ]

    return snac_codes

def unpack_snac_from_7(snac_tokens: list) -> list:
    """Unpack 7-token SNAC frames to 3 hierarchical levels."""
    if snac_tokens and snac_tokens[-1] == CODE_END_TOKEN_ID:
        snac_tokens = snac_tokens[:-1]

    frames = len(snac_tokens) // SNAC_TOKENS_PER_FRAME
    snac_tokens = snac_tokens[:frames * SNAC_TOKENS_PER_FRAME]

    if frames == 0:
        return [[], [], []]

    l1, l2, l3 = [], [], []

    for i in range(frames):
        slots = snac_tokens[i*7:(i+1)*7]
        l1.append((slots[0] - CODE_TOKEN_OFFSET) % 4096)
        l2.extend([
            (slots[1] - CODE_TOKEN_OFFSET) % 4096,
            (slots[4] - CODE_TOKEN_OFFSET) % 4096,
        ])
        l3.extend([
            (slots[2] - CODE_TOKEN_OFFSET) % 4096,
            (slots[3] - CODE_TOKEN_OFFSET) % 4096,
            (slots[5] - CODE_TOKEN_OFFSET) % 4096,
            (slots[6] - CODE_TOKEN_OFFSET) % 4096,
        ])

    return [l1, l2, l3]

def main():

    # Load the best open source voice AI model
    print("\n[1/3] Loading Maya1 model...")
    model = AutoModelForCausalLM.from_pretrained(
        "maya-research/maya1",
        torch_dtype=torch.bfloat16,
        device_map="auto",
        trust_remote_code=True
    )
    tokenizer = AutoTokenizer.from_pretrained(
        "maya-research/maya1",
        trust_remote_code=True
    )
    print(f"Model loaded: {len(tokenizer)} tokens in vocabulary")

    # Load SNAC audio decoder (24kHz)
    print("\n[2/3] Loading SNAC audio decoder...")
    snac_model = SNAC.from_pretrained("