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README.md

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+---
+language:
+- sw
+license: cc-by-nc-4.0
+base_model: facebook/mms-tts-swh
+tags:
+- text-to-speech
+- tts
+- vits
+- swahili
+- mms
+datasets:
+- google/WaxalNLP
+pipeline_tag: text-to-speech
+---
+
+# TTSModel — Swahili TTS
+
+A Swahili text-to-speech model, finetuned from Meta's [MMS-TTS Swahili](https://huggingface.co/facebook/mms-tts-swh) checkpoint on the `swa_tts` split of [google/WaxalNLP](https://huggingface.co/datasets/google/WaxalNLP), using the [VITS finetuning recipe](https://github.com/ylacombe/finetune-hf-vits) from `ylacombe/finetune-hf-vits`. Developed by the [Maseno Centre for Applied AI (MCAAI)](https://mcaai.maseno.ac.ke/).
+
+## Model details
+
+- **Base model:** `facebook/mms-tts-swh` (Meta's Massively Multilingual Speech TTS, Swahili)
+- **Architecture:** VITS (single-speaker)
+- **Training data:** [google/WaxalNLP](https://huggingface.co/datasets/google/WaxalNLP), `swa_tts` config — 1,387 train utterances (805 after duration/length filtering), single Swahili speaker, 16kHz audio, sourced via the Loud & Clear initiative.
+- **Training checkpoint:** step 4,500 / 20,200 planned steps (~22 epochs of ~805 examples)
+- **Sample rate:** 16,000 Hz
+- **Language:** Swahili (`swh` / ISO 639-3)
+
+## Training configuration
+
+| Setting | Value |
+|---|---|
+| Learning rate | 2e-5 |
+| Batch size | 8 |
+| Precision | fp16 |
+| Max clip duration | 20s |
+| Min clip duration | 0.5s |
+| Loss weights | mel=35, kl=1.5, disc=3, gen/fmaps/duration=1 |
+
+Training used the `finetune-hf-vits` recipe with `transformers==4.35.1`, `datasets==2.14.7`, `accelerate==0.24.1`, `numpy<2.0`.
+
+## Usage
+
+```python
+import numpy as np
+from transformers import pipeline
+import scipy.io.wavfile
+
+synthesiser = pipeline("text-to-speech", model="MCAA1-MSU/TTSModel")
+speech = synthesiser("Habari yako, karibu Kenya.")
+
+audio = np.squeeze(speech["audio"])
+scipy.io.wavfile.write("output.wav", rate=speech["sampling_rate"], data=audio)
+```
+
+Verified loading cleanly with `transformers` `pipeline("text-to-speech", ...)` — no missing/unexpected weight warnings on load.
+
+## Intended use
+
+Research and experimentation with Swahili TTS. Not yet suitable for production or user-facing applications given the early training stage.
+
+## License
+
+Derived from `facebook/mms-tts-swh` (**CC-BY-NC-4.0**, non-commercial). This finetuned model inherits that license. `swa_tts` training data is CC-BY-SA-4.0.
+
+## Acknowledgements
+
+- [Meta MMS](https://huggingface.co/facebook/mms-tts-swh) for the base checkpoint
+- [WAXAL / google/WaxalNLP](https://huggingface.co/datasets/google/WaxalNLP) for the training data
+- [ylacombe/finetune-hf-vits](https://github.com/ylacombe/finetune-hf-vits) for the finetuning recipe