NOESIS / AMAImedia

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

• Founder: Ilia Bolotnikov
• Organization: AMAImedia.com
• X (Twitter): @AMAImediacom
• LinkedIn: Ilia Bolotnikov
• Telegram: @djbionicl
• NOESIS version: v16.1
• Release date: 2026-08-26

Language support

This Qwen3-derived model follows the official Qwen3 language coverage (119 languages and dialects):

English, French, Portuguese, German, Romanian, Swedish, Danish, Bulgarian, Russian, Czech, Greek, Ukrainian, Spanish, Dutch, Slovak, Croatian, Polish, Lithuanian, Norwegian Bokmål, Norwegian Nynorsk, Persian, Slovenian, Gujarati, Latvian, Italian, Occitan, Nepali, Marathi, Belarusian, Serbian, Luxembourgish, Venetian, Assamese, Welsh, Silesian, Asturian, Chhattisgarhi, Awadhi, Maithili, Bhojpuri, Sindhi, Irish, Faroese, Hindi, Punjabi, Bengali, Oriya, Tajik, Eastern Yiddish, Lombard, Ligurian, Sicilian, Friulian, Sardinian, Galician, Catalan, Icelandic, Tosk Albanian, Limburgish, Dari, Afrikaans, Macedonian, Sinhala, Urdu, Magahi, Bosnian, Armenian; Chinese (Simplified Chinese, Traditional Chinese, Cantonese), Burmese; Arabic (Standard, Najdi, Levantine, Egyptian, Moroccan, Mesopotamian, Ta’izzi-Adeni, Tunisian), Hebrew, Maltese; Indonesian, Malay, Tagalog, Cebuano, Javanese, Sundanese, Minangkabau, Balinese, Banjar, Pangasinan, Iloko, Waray (Philippines); Tamil, Telugu, Kannada, Malayalam; Turkish, North Azerbaijani, Northern Uzbek, Kazakh, Bashkir, Tatar; Thai, Lao; Finnish, Estonian, Hungarian; Vietnamese, Khmer; Japanese, Korean, Georgian, Basque, Haitian, Papiamento, Kabuverdianu, Tok Pisin, Swahili.

--- license: apache-2.0 license_link: LICENSE language: - en - zh - ja - ko - de - fr - ru - ar - hi - es library_name: transformers tags: - qwen3 - qwen3-119-languages - supports-119-languages - multilingual - language-support - awq - int4 - quantization - text-to-speech - tts - voice-cloning - cross-lingual - noesis - dhcf-fno - qwen3-tts base_model: FINAL-Bench/Darwin-TTS-1.7B-Cross quantized_by: AMAImedia pipeline_tag: text-to-speech

Qwen3-1.7B-TTS-Cross-Darwin-NOESIS-AWQ-INT4

AWQ INT4 quantization of FINAL-Bench/Darwin-TTS-1.7B-Cross optimized for low-VRAM consumer hardware (RTX 3060 6 GB).

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

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⚠️ License notice

This model is derived from FINAL-Bench/Darwin-TTS-1.7B-Cross which is licensed under Apache 2.0. This AWQ quantization retains the same Apache 2.0 license — see the LICENSE file in this repository for the full text.

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Model summary

Property	Value
Base model	FINAL-Bench/Darwin-TTS-1.7B-Cross
Architecture	Qwen3TTSForConditionalGeneration
Model type	qwen3_tts
TTS model size	~2.1B total (talker 28L + code_predictor + codec + encoder/decoder)
TTS model type	base with 3% FFN blend from Qwen3-1.7B LLM
Tokenizer	12 Hz RVQ codec tokenization
Speaker encoder	x-vector based (x_vector_only_mode=True), sample_rate=24 000 Hz
Original precision	BF16 safetensors (~3.4 GB)
Quantized precision	AWQ INT4 (group_size=128, GEMM, zero_point=True) — talker weights only
Languages	10 (ko, en, ja, zh + 6 more)
Disk footprint	~0.9 GB
Inference VRAM	~1.2 GB (fits alongside other specialists)
Quantization library	AutoAWQ 0.2.9
Calibration set	128 TTS prompts (short natural speech samples), max_seq_len=512
RNG seed	1729 (NOESIS reproducibility lock)

Architecture note: Darwin-TTS-1.7B-Cross is a merge model — not a fine-tuned model. It blends 3% of FFN weights from Qwen3-1.7B (LLM) into the talker backbone of Qwen3-TTS-12Hz-1.7B-Base (84 / 976 tensors: gate/up/down_proj × 28 talker layers). This weight-space arithmetic (pure lerp, no training) adds emotional expressiveness while preserving TTS stop-signal stability.

4-module structure:

• talker — 28-layer Qwen3 LLM backbone (AWQ-quantized)
• code_predictor — 5-layer token head, hidden_size=1024 (BF16, untouched)
• speech_tokenizer — 12 Hz RVQ codec (BF16, untouched)
• encoder/decoder — audio waveform pipeline (BF16, untouched)

The "Cross" refers to cross-lingual voice cloning using x-vector speaker embedding (x_vector_only_mode=True). At α≥10% the LLM's generation pattern overrides the TTS stop signal — hence the conservative 3% blend ratio.

⚠️ AWQ compatibility: AutoAWQ quantizes the talker (language model head) component. Non-standard modules (code_predictor, speaker_encoder) are kept in BF16 within the checkpoint. Verify output with validate_awq_quality.py before using as a KD teacher.

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Why this quantization

The original Darwin-TTS-1.7B-Cross weights are in BF16 (~3.4 GB). While this fits VRAM in principle, AWQ INT4 reduces VRAM to ~1.2 GB, enabling parallel loading alongside other NOESIS specialists in the sequential swapping pipeline.

This AWQ build:

1. Reduces VRAM from 3.4 GB to **1.2 GB** — freeing headroom for KV cache
2. Uses GEMM kernel — compatible with device_map={"":0} (no CPU offload)
3. Provenance-tracked (noesis_provenance.json ships alongside the model)
4. Calibrated on natural TTS prompts matching cross-lingual synthesis distribution

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Quantization methodology

This checkpoint was produced using a proprietary quantization pipeline developed by AMAImedia as part of the NOESIS DHCF-FNO framework (v14.7). The Qwen3TTSForConditionalGeneration architecture is not present in the transformers main branch and has no upstream AutoAWQ support; quantization required original engineering work developed internally at AMAImedia.

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How to use

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
import torch

model_id = "amaimedia/Qwen3-1.7B-TTS-Cross-Darwin-NOESIS-AWQ-INT4"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoAWQForCausalLM.from_quantized(
    model_id,
    device_map={"": 0},
    torch_dtype=torch.float16,
    fuse_layers=False,
    trust_remote_code=True,
)

# TTS inference requires additional processing — see base model card for full pipeline
prompt = "Hello, how are you today?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)

Note: Full TTS synthesis (audio waveform output) requires the complete NOESIS TTS pipeline with NanoCodec vocoder. The AWQ checkpoint above is suitable for logit extraction (KD) and text-side token generation.

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NOESIS context

In NOESIS this model serves as the TTS stream-A teacher for Specialist M3-TTS during knowledge distillation. It provides cross-lingual text-side logits (vocab space) used in build_ensemble_labels.py with proposed weight w=0.20 (secondary, combined with codec-side teachers via NanoCodec).

NOESIS TTS knowledge distillation uses two parallel streams:

Stream	Vocab	Teachers
Stream A (text-side)	151 936 (Qwen3)	Darwin-TTS-1.7B-Cross + others
Stream B (codec-side)	65 536 (NanoCodec)	nanocodec_distill.py

NOESIS specialists overview:

ID	Role	Size
M1	ASR (150+ langs)	10B/3B
M2	Dubbing LM (30 langs full)	10B/3B
M3	TTS + voice cloning	10B/3B
M4	Chat + creative writing	10B/3B
M5	Code + math	10B/3B
M6	Deep research (1M ctx)	10B/3B
M7	Prompt engineering	4B/0.8B
M8	Quality control (PRM)	4B/0.8B
M9	Orchestrator + routing	4B/0.8B

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Acknowledgements & citation

Base model: Darwin-TTS-1.7B-Cross by FINAL-Bench (Qwen3 TTS architecture, cross-lingual synthesis).

@misc{darwin_tts_cross,
  title     = {Darwin-TTS-1.7B-Cross},
  author    = {FINAL-Bench},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/FINAL-Bench/Darwin-TTS-1.7B-Cross}
}

Quantization & NOESIS integration:

@misc{noesis_v14,
  title     = {NOESIS v14.7: DHCF-FNO Multilingual Dubbing Platform},
  author    = {Bolotnikov, Ilia},
  year      = {2026},
  publisher = {AMAImedia},
  url       = {https://amaimedia.com}
}