zero0303/maya2-mlx-dynamic-8bit

Maya 2 Native for MLX (dynamic 8-bit)

An Apple Silicon (MLX) port of Maya 2 Native by Maya Research: streaming text-to-speech for 11 Indic languages and English, with 20 built-in voices. It runs with no PyTorch and no CUDA, faster than real time on an M5.

• Ported line by line from the official code (commit c6b3768): the backbone, the depth decoder, the Mimi decoder, the sampler with its stop rules and guards, the official random-number stream, text splitting, prompts and the tokenizer.
• Proven against the official model before quantizing: at the official 16-bit precision the port gives the same codes, stops and audio (within one int16 step) as the official code on every test request (strict gates, fp32), and the same accuracy at its own precision (production gates).
• Dynamic 8-bit weights (models/maya2-8bit, in the spirit of Unsloth's dynamic quants): every tensor's sensitivity was measured against the official model; the outliers and the sensitive depth decoder stay 16-bit, the rest is 8-bit. 3.57 GB, with a sampling shift about 1.2× the official 16-bit model's own rounding noise in all 12 languages. (A plain 8-bit set, every weight 8-bit, was measured too and left out: up to 7× the official noise.)

Independent port; not an official Maya Research release. See NOTICE.md.

Quickstart

Requirements: an Apple Silicon Mac, Python 3.11, about 6 GB of free memory.

hf download zero0303/maya2-mlx-dynamic-8bit --local-dir maya2-mlx
cd maya2-mlx
uv venv --python 3.11 .venv
VIRTUAL_ENV=$PWD/.venv uv pip install "mlx==0.32.4" numpy "tokenizers==0.22.2"
.venv/bin/python -I speak.py "A heap keeps its smallest item at the top." --voice Malini --seed 7 -o heap.wav
.venv/bin/python -I speak.py --voices

From Python:

import sys; sys.path.insert(0, "maya2-mlx")
from maya2_mlx import Maya2

tts = Maya2()                                   # models/maya2-8bit
wav = tts.generate("नमस्ते, आप कैसे हैं?", lang="hi", voice="Vaidehi", seed=11)    # float32, 24 kHz, in [-1, 1]
for pcm in tts.stream("Long text ...", lang="en", voice="Anirban"):              # int16 chunks as they are made
    ...

generate / stream follow the official Maya3TTS API: lang, voice (a built-in name, case-insensitive, or None for the plain voice), seed, temperature, top_k, top_p, repetition_penalty (the codebook-0 sampler), max_frames, normalize (the bundled text normaliser: digits, currency, dates, URLs). Long text is split into ~20-second pieces with 0.15 s gaps, as in the official engine. The same seed gives the same audio.

Voices

Voice			Description	Trained languages
Ahana	female	young	bright, youthful	11
Vaidehi	female	adult	calm, gentle	11
Malini	female	mature	steady narrator	11
Charvi	female	young	energetic, upbeat	11
Jahnavi	female	adult	warm, friendly	10 (not pa)
Anirban	male	adult	steady narrator	11
Nakul	male	young	bright, youthful	11
Vasant	male	mature	steady narrator	10 (not pa)
Siddhant	male	adult	clear, professional	11
Satvik	male	young	warm, friendly	11
Abhay, Akshat, Shaunak	male	adult / young	natural conversational	en, hi
Nivedita, Tanushree	female	adult	natural conversational	en, hi
Ishwari, Vrinda	female	adult / young	natural conversational	hi
Ritvik	male	adult	natural conversational	hi
Sreekar	male	adult	natural conversational	en, hi, te
Kayal	female	adult	natural conversational	en, ta

"11" = bn, en, gu, hi, kn, ml, mr, or, pa, ta, te. Other languages work cross-lingually (Assamese for every voice).

Speed and quality

Measured on an M5 (24 GB), the same 23.4 s English paragraph, and the parity gates against the official model (plain 8-bit and 16-bit for comparison; only the dynamic 8-bit is in this repository):

	Dynamic 8-bit (this repository)	Plain 8-bit	16-bit
Download	3.57 GB	2.93 GB	5.24 GB
Peak memory	5.8 GB	4.2 GB	8.2 GB
Time for 23.4 s of speech	18.4 s (1.27× real time)	15.5 s (1.5×)	20.6 s (1.14×)
Sampling shift vs exact, 12 languages (official 16-bit noise = 1×)	~1.2× (0.6–2×)	~3.5× (1.5–7×)	1×
Depth decoder (fine sound detail)	exact, as the official	1.6–2.8% error; decisions change beyond near-ties	exact
Parity gates vs the official model	101/101	86/101	101/101

Files

File (models/maya2-8bit/)	Size	Contents
backbone-main.safetensors	460 MB	token embeddings (8-bit), final norm, cb0 head, RoPE tables
backbone-layers-00/07/14/21.safetensors	4 × 396-407 MB	28 Qwen3-shaped layers, linears 8-bit (layers 2 and 27 down-projection kept 16-bit)
depth.safetensors	1.10 GB (16-bit)	depth decoder
audio_in.safetensors	151 MB	feedback table, 8-bit
mimi.safetensors	230 MB	Kyutai Mimi decoder, fp32
anchor.safetensors	2.3 MB	speaker anchor, bf16
tokenizer.json, voices.json, meta.json, quant.json	16 MB	tokenizer, voices, source checksums, quantization recipe
total	3.57 GB

Code: maya2_mlx/ (the port), maya3/ and configs/ (official files, unchanged), speak.py. The parity tests against the official model, the export and quantization scripts and the 16-bit MLX export are not part of this repository; their results are summarised below.

How it was checked

	Result
Tokenizer	546/546 prompts identical to the official (12 languages × 13 voices, Devanagari edge cases)
Strict gates (fp32, CPU and GPU)	92/92 each: identical sampler decisions and codes on every frame, same free-running requests (37 s / 3 pieces included)
Production gates (16-bit)	101/101: as accurate as the official bf16 (error ratio 1.02); depth flips only at near-ties, as the official's own fp32-vs-fp16
Dynamic 8-bit	101/101 against the official model; sampling shift 1.2× the 16-bit floor
Reviews	two independent code reviews against the official source; one hidden issue found and fixed (the official engine builds RoPE from bf16 frequencies)

Not included

Voice cloning (ECAPA speaker encoder, Mimi encoder), emotion-tag evaluation, multi-request batching and the HTTP server. One request at a time.

Licenses and credit

Maya 2 Native weights and code © Maya Research, Apache-2.0 (LICENSE). Kyutai Mimi © Kyutai, CC BY 4.0 (decoder only, re-laid out for MLX). Qwen3 architecture, Apache-2.0. Details and the original citation: NOTICE.md. Use the voices responsibly: disclose synthetic speech, no impersonation (see the original model card).