cloud0day3/antalia-1-foundation

Antalia 1 Foundation

The speaker-agnostic Turkish acoustic model behind Antalia 1, trained from scratch on public data only.

Türkçe: README.tr.md

Development is discontinued. Released as-is so that others can fine-tune their own consented Turkish voice with the code in https://github.com/0daycloud/antalia. Contact: sezgin@patientdesk.ai or GitHub issues.

At a glance

What it is	The base model of Antalia 1: a ~300M-parameter character-conditioned rectified-flow model over 24 kHz, 100-band log-mel spectrograms.
Speaker	None. There is no speaker conditioning; the voice of any output is an unspecified average of Common Voice contributors and changes with the sampling seed.
Size	299.6M parameters, fp32 safetensors, 1.20 GB.
Training data	Common Voice 26.0 Turkish (CC0) and FLEURS Turkish (CC-BY-4.0), 61,469 filtered clips, 67.55 h. Nothing else.
Intelligibility	CER 0.1835 single seed on the 120-prompt turkish-v2 suite. An intelligibility baseline, not a product voice.
Use it for	Fine-tuning a new consented Turkish voice with the reference lineage below.
License	Weights: Antalia OpenRAIL-M (LICENSE.md), which requires credit to the authors in anything that uses or redistributes the model. Code: Apache-2.0.

Listen

Both clips are AI-generated with speaker id 0, the unconditioned path. Each is paired with the same sentence from the fine-tuned Antalia 1 so you can hear what the fine-tuning stages add. More on the sample site.

Prompt	Takes
Sabah güneşi sessiz sokağın taşlarına yavaşça vuruyordu. General	Foundation, unconditioned Antalia 1, best of 8 · CER 0.00 · Sim 0.972
Size yardımcı olabilmem için randevu tarihini paylaşır mısınız? Voice agent	Foundation, unconditioned Antalia 1, best of 8 · CER 0.00 · Sim 0.959

Quick start

git clone https://github.com/0daycloud/antalia
cd antalia
uv sync            # or: pip install -e .

# Vocoder: BigVGAN is not redistributed. Pinned commit + one-line hub patch.
git clone https://github.com/NVIDIA/BigVGAN /opt/bigvgan
git -C /opt/bigvgan checkout 7d2b454564a6c7d014227f635b7423881f14bdac
patch -d /opt/bigvgan -p4 < scripts/patches/bigvgan-huggingface-hub-1.patch
export PYTHONPATH=/opt/bigvgan

python scripts/synthesize-crossflow.py \
  --checkpoint cloud0day3/antalia-1-foundation \
  --vocoder nvidia/bigvgan_v2_24khz_100band_256x \
  --sway -0.8 --steps 32 --mel-clamp 5.0 \
  --text "Bugün hava çok güzel, dışarıda yürüyüş yapmak istiyorum." \
  --output foundation.wav

To fine-tune a new voice, follow the reference lineage in configs/crossflow/: foundation-speaker-v2.json → candidate-b-speaker-v1.json → cfg-foundation-v1.json → candidate-consistency-v1.json → candidate-b-timbre-adapter-v2.json. The voice corpus that lineage was run on is public: antalia-voice-corpus (5.008 h, CC-BY-4.0).

Training data

Source	Clips used	License
Common Voice 26.0 Turkish (validated, filtered)	59,593	CC0-1.0
FLEURS Turkish	1,876	CC-BY-4.0

Total 61,469 clips / 67.55 h. Filtering: forced-Turkish Whisper-large-v3 transcript check (accepted median CER 0, p90 0.111), active-audio, level, clipping, and SNR gates, per-speaker caps, speaker-disjoint validation split. Rejections from 120,407 validated CV clips: 7,098 insufficient active audio, 1,449 transcript mismatch, 356 too short, 351 low level, 5 low SNR, 4 clipping. Filter manifests with clip ids and checksums ship in the code repository; the audio itself is not re-hosted.

Training

Clean initialization; 100,000 updates on one A100-80GB; AdamW, LR 1e-4 with 2,000 warmup updates decaying to 1e-5, weight decay 0.01, gradient clip 1.0, EMA 0.9999; 6,000 mel frames per batch (≤32 samples, ≤24 s per clip); duration-loss weight 0.1; per-band mel mean/std normalization measured on 256 training records (89,966 frames), clipped at ±5. Two earlier foundation runs without per-band normalization diverged and were abandoned.

Quality

On the 120-prompt turkish-v2 suite, single seed, 32 Euler steps, no guidance:

Metric	Foundation	Antalia 1 (single seed)
CER mean	0.1835	0.0528
CER p90	0.4049	0.1348
WER mean	0.3952	0.1297

This is an intelligibility baseline for a speaker-agnostic model with a sampled voice; it is not usable as a product voice on its own.

Files

File	Purpose
model.safetensors	EMA weights, fp32, 299,623,013 parameters. SHA-256 89ecf310a14333c6bd5a754360cee2207027dd23b2a0171984a32c423afcfcd1
config.json	Release format antalia-crossflow-release-v1: architecture, mel statistics, character vocabulary, vocoder pointer, provenance
LICENSE.md	Antalia OpenRAIL-M

Vocoder: nvidia/bigvgan_v2_24khz_100band_256x (MIT), downloaded by the loader.

Architecture

16 Transformer blocks (dim 768, 12 heads, SwiGLU FFN 3072) with self-attention over mel frames, cross-attention over characters, and zero-initialized adaLN modulation from the flow timestep; 4-block ConvNeXt-style character encoder; a scalar log-total-duration head. Deterministic Turkish text normalizer; grapheme input.

Limitations

• Unconditioned voice identity is arbitrary and unstable across seeds.
• Long inputs are under-budgeted by the duration head; use clause chunking.
• Numbers, foreign names, and abbreviations have the highest error rates.
• Turkish only. No memorization audit was run.

License and use

Weights: Antalia Open RAIL-M (see LICENSE.md): no impersonation, no undisclosed synthetic speech, no fraud or robocalls. Any distribution of the model or its derivatives, and any product, service or publication that uses them, must credit "Antalia 1" by Sezgin Saygili, Emre Kaplaner, Oncel Ozgul and Fikri San Koktas (Patientdesk.ai) with a link to this repository or the code repository. Code: Apache-2.0.

Citation

@misc{antalia1_2026,
  title  = {Antalia 1: An Open Turkish Text-to-Speech Model from a Rights-Clean Pipeline},
  author = {Saygili, Sezgin and Kaplaner, Emre and Ozgul, Oncel and Koktas, Fikri San},
  year   = {2026},
  note   = {Technical report},
  url    = {https://huggingface.co/cloud0day3/antalia-1-foundation}
}