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LICENSE

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+NVIDIA OPEN MODEL LICENSE
+=========================
+
+This model, "Magpie-TTS 13-Language (357M)", is a derivative of NVIDIA
+Magpie-TTS-Multilingual (nvidia/magpie_tts_multilingual_357m) and is distributed
+under the NVIDIA Open Model License Agreement.
+
+The full and authoritative text of the NVIDIA Open Model License is available at:
+
+    https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+
+Required attribution (see NOTICE):
+
+    "Licensed by NVIDIA Corporation under the NVIDIA Open Model License."
+
+Summary of key permissions and conditions (the linked text governs):
+  - Redistribution and commercial use are permitted.
+  - Derivative models are permitted (this model is one such derivative).
+  - You must retain this attribution/notice and comply with the license terms,
+    including its use restrictions and trustworthy-AI provisions.
+
+The Swahili language extension in this derivative was produced by New Emerging
+Technologies (a subsidiary of Infinia Technologies) and is not provided,
+supported, or endorsed by NVIDIA Corporation.
+
+Training data are licensed CC-BY-4.0 (Google FLEURS; Bateesa Kiswahili TTS) and
+are credited in the model card and NOTICE.

NOTICE

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+Magpie-TTS 13-Language (357M)
+Copyright (c) 2026 New Emerging Technologies (a subsidiary of Infinia Technologies).
+
+This model is a derivative of NVIDIA Magpie-TTS-Multilingual
+(nvidia/magpie_tts_multilingual_357m), extended with Swahili support.
+
+Licensed by NVIDIA Corporation under the NVIDIA Open Model License.
+See the LICENSE file for the full terms.
+
+The Swahili language extension was added by the community and is not provided,
+supported, or endorsed by NVIDIA Corporation.
+
+Training data:
+  - FLEURS (Conneau et al., 2022), CC-BY-4.0
+  - Bateesa Kiswahili TTS dataset, CC-BY-4.0
+
+Audio generated by this model carries a watermark and a synthetic-speech
+disclosure inherited from the base model.

README.md

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+---
+license: other
+license_name: nvidia-open-model-license
+license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+library_name: nemo
+pipeline_tag: text-to-speech
+base_model: nvidia/magpie_tts_multilingual_357m
+language:
+  - ar
+  - de
+  - en
+  - es
+  - fr
+  - hi
+  - it
+  - ja
+  - ko
+  - pt
+  - sw
+  - vi
+  - zh
+datasets:
+  - google/fleurs
+  - Bateesa/kiswahili-tts-dataset
+tags:
+  - text-to-speech
+  - speech-synthesis
+  - multilingual
+  - swahili
+  - continual-learning
+  - codec-language-model
+  - nemo
+---
+
+# Magpie-TTS 13-Language (357M) — with Swahili
+
+A single **13-language** text-to-speech checkpoint: the twelve languages of NVIDIA's
+[`magpie_tts_multilingual_357m`](https://huggingface.co/nvidia/magpie_tts_multilingual_357m)
+(Arabic, German, English, Spanish, French, Hindi, Italian, Japanese, Korean,
+Portuguese, Vietnamese, Chinese) **plus Swahili (Kiswahili)** — added by a
+community team **without regressing any of the original twelve**.
+
+Swahili was grafted onto the frozen base model with a dedicated byte-level
+tokenizer, a **warm-started** input-embedding surgery, and multilingual rehearsal.
+On held-out Swahili the model reaches **8.8% character error rate** (median 0.0%,
+MMS-Swahili ASR), and the twelve base languages show **no measurable regression**
+versus the untouched base. Full method: see the accompanying paper *"Cross-Lingual
+Grafting: Extending Frozen Codec Speech Language Models to Unseen Low-Resource
+Languages"* (New Emerging Technologies, a subsidiary of Infinia Technologies).
+
+> ⚠️ **Community model.** Swahili support was added by the community and is **not**
+> provided or endorsed by NVIDIA. The twelve base languages follow the base model.
+
+## Highlights
+
+| | |
+|---|---|
+| Base | Magpie-TTS-Multilingual 357M (Koel-TTS family) |
+| Audio codec | Low Frame-rate Speech Codec (22.05 kHz, 1.89 kbps, 21.5 fps) |
+| Languages | 13 (12 base + Swahili) |
+| Swahili quality | 8.8% mean / 0.0% median CER (MMS-sw) |
+| Base regression | ~0 (mean recognizer CER identical to base) |
+| Voices | 5 baked speakers (2 female, 3 male), selected by index |
+| Trained on | 1× 128 GB unified-memory GPU |
+
+## Usage (NVIDIA NeMo)
+
+```python
+from nemo.collections.tts.models import MagpieTTSModel
+import soundfile as sf, numpy as np, torch, random
+
+m = MagpieTTSModel.from_pretrained("infiniatechnologies/magpie-tts-13lang-357m").eval().cuda()
+
+# Deterministic inference: pin the seed so identical inputs give identical audio
+def seed(s=1234):
+    random.seed(s); np.random.seed(s); torch.manual_seed(s); torch.cuda.manual_seed_all(s)
+
+seed(1234)
+audio, alen = m.do_tts(
+    "Habari ya asubuhi. Karibu kwenye jaribio la sauti ya Kiswahili.",
+    language="sw",            # native Swahili code; also en, de, es, fr, it, vi,
+                              # zh, hi, ja, pt-BR, ko, ar-MSA, ar-SA, ar-AE
+    speaker_index=0,          # 0 Aria(F) 1 Jason(M) 2 John(M) 3 Leo(M) 4 Sofia(F)
+    apply_TN=False, use_cfg=True,
+)
+w = np.squeeze(audio.detach().float().cpu().numpy())
+sf.write("out.wav", (w[0] if w.ndim > 1 else w).astype(np.float32), 22050)
+```
+
+**Determinism.** The released tokenizers use `phoneme_probability=1.0` (deterministic
+phonemization) and the sampler defaults (temperature 0.7, top-k 80, CFG 2.5) match
+the base model. With a fixed RNG seed, identical (text, language, voice, CFG, seed)
+inputs produce byte-identical audio; change the seed for a different rendering.
+For the occasional degenerate silent generation, resample at a new seed
+(retry-on-silence).
+
+## Language codes
+`en, de, es, fr, it, vi, zh, hi, ja, pt-BR, ko, ar-MSA, ar-SA, ar-AE, sw`.
+
+## Training data
+- **Swahili**: [Bateesa/kiswahili-tts-dataset](https://huggingface.co/datasets/Bateesa/kiswahili-tts-dataset) (CC-BY) + [FLEURS](https://huggingface.co/datasets/google/fleurs) `sw_ke` (CC-BY), ~27.8 h, resampled to 22.05 kHz.
+- **Rehearsal (12 base languages)**: FLEURS, ~500 utterances/language, each routed to its native tokenizer.
+
+Please cite FLEURS (Conneau et al., *arXiv:2205.12446*) and the Koel-TTS
+(*arXiv:2502.05236*) and Low Frame-rate Speech Codec (*arXiv:2409.12117*) papers.
+
+## Intended use & limitations
+Research and product speech synthesis for the 13 supported languages. **Out of
+scope:** impersonation of real individuals, deceptive or harmful synthetic media.
+The model synthesizes a **fixed set of 5 baked voices**; it does **not** support
+zero-shot voice cloning (the base model's context encoder is not included).
+Swahili data skews toward read/literary speech and one dominant speaker; FLEURS
+adds multi-speaker breadth at upsampled 16 kHz. 357M parameters bound absolute
+quality.
+
+## Responsible use
+Generated audio carries the base model's watermark and synthetic-speech
+disclosure. Do not use to deceive. Disclose that audio is AI-generated.
+
+## License & attribution
+Released under the **NVIDIA Open Model License** (see `LICENSE` and `NOTICE`).
+Derived from `nvidia/magpie_tts_multilingual_357m`. *"Licensed by NVIDIA
+Corporation under the NVIDIA Open Model License."* Training data: FLEURS (CC-BY),
+Bateesa Kiswahili TTS (CC-BY).

magpie_tts_13lang_357m.nemo

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