Sauti Tts v2
This is a Swahili text-to-speech (TTS) model fine-tuned on top of the English Chatterbox Multilingual backbone using a LoRA adapter. It was developed by Msingi-AI as part of the Sauti TTS V2 project.
Model source
- Kind
- LoRA
- File size
- 365 MB
- Version
- vadapter_model
- Updated
- Sep 25, 2026
- Downloads
- 0
- Source
- Hugging Face
Source description
Sauti TTS V2 (Chatterbox Swahili LoRA)
This is a Swahili text-to-speech (TTS) model fine-tuned on top of the English Chatterbox Multilingual backbone using a LoRA adapter. It was developed by Msingi-AI as part of the Sauti TTS V2 project.
Model Details
- Base Model: Chatterbox Multilingual (English backbone + multilingual grapheme tokenizer)
- Training Method: LoRA fine-tuning
- Language: Swahili (
sw) - License: MIT
Training Data
The model was fine-tuned on Google's WaxalNLP swa_tts studio dataset (CC-BY-4.0).
The dataset was filtered using a transcript-agreement check, dropping clips where an Automatic Speech Recognition (ASR) judge detected a Character Error Rate (CER) > 10% against the reference text.
Evaluation Results
The model was evaluated against a held-out evaluation set of 48 Swahili sentences across various stress categories (general, code-switching, named entities, numbers/dates).
Intelligibility is measured by Word Error Rate (WER) and Character Error Rate (CER) under two ASR judges: zero-shot openai/whisper-large-v3 and a Swahili-fine-tuned Whisper (Jacaranda-Health/ASR-STT). Swahili's agglutinative morphology tends to inflate WER, making CER the headline metric. UTMOS is used as a proxy for audio quality.
| Scope | openai/whisper-large-v3 WER / CER | Jacaranda-Health/ASR-STT WER / CER | UTMOS |
|---|---|---|---|
| Overall | 0.388 / 0.088 | 0.139 / 0.038 | 3.887 |
| Overall (Plain Swahili) | 0.384 / 0.086 | 0.092 / 0.022 | 3.871 |
| Code Switch | 0.400 / 0.094 | 0.279 / 0.084 | 3.934 |
| General | 0.320 / 0.069 | 0.148 / 0.036 | 3.872 |
| Named Entities | 0.421 / 0.094 | 0.027 / 0.005 | 3.897 |
| Numbers & Dates | 0.411 / 0.095 | 0.100 / 0.026 | 3.844 |
Usage
The weights provided here are the LoRA adapter (new_lang_adapter directory). They must be loaded on top of the original Chatterbox backbone using the Chatterbox fine-tuning toolkit.
Sources
1 source
Sources
1 sourceVerified Sep 6
Model artifacts
1 artifact
Model artifacts
1 artifactnew_lang_adapter/adapter_model.safetensors
safetensors · 365 MB · SHA-256 19a916c36388…c791 · Hugging Face
Download fileSource excerpts
2 excerpts
Source excerpts
2 excerpts--- language: sw tags: - text-to-speech - tts - swahili - chatterbox - lora license: mit datasets: - google/WaxalNLP --- # Sauti TTS V2 (Chatterbox Swahili LoRA) This is a Swahili text-to-speech (TTS) model fine-tuned on top of the English Chatterbox Multilingual backbone using a LoRA adapter. It was developed by **Msingi-AI** as part of the Sauti TTS V2 project. ## Model Details * **Base Model**: Chatterbox Multilingual (English backbone + multilingual grapheme tokenizer) * **Training Method**: LoRA fine-tuning * **Language**: Swahili (`sw`) * **License**: MIT ## Training Data The model was fine-tuned on Google's [WaxalNLP `swa_tts`](https://huggingface.co/datasets/google/WaxalNLP) studio dataset (CC-BY-4.0). The dataset was filtered using a transcript-agreement check, dropping clips where an Automatic Speech Recognition (ASR) judge detected a Character Error Rate (CER) > 10% against the reference text. ## Evaluation Results The model was evaluated against a held-out evaluation set of 48 Swahili sentences across various stress categories (general, code-switching, named entities, numbers/dates). Intelligibility is measured by Word Error Rate (WER) and Character Error Rate (CER) under two ASR judges: zero-shot `openai/whisper-large-v3` and a Swahili-fine-tuned Whisper (`Jacaranda-Health/ASR-STT`). Swahili's agglutinative morphology tends to inflate WER, making CER the headline metric. UTMOS is used as a proxy for audio quality. | Scope | openai/whisper-large-v3 WER / CER | Jacaranda-Health/ASR-STT WER / CER | UTMOS | |---|---|---|---| | **Overall** | 0.388 / 0.088 | 0.139 / 0.038 | 3.887 | | **Overall (Plain Swahili)** | 0.384 / 0.086 | **0.092 / 0.022** | 3.871 | | **Code Switch** | 0.400 / 0.094 | 0.279 / 0.084 | 3.934 | | **General** | 0.320 / 0.069 | 0.148 / 0.036 | 3.872 | | **Named Entities** | 0.421 / 0.094 | 0.027 / 0.005 | 3.897 | | **Numbers & Dates** | 0.411 / 0.095 | 0.100 / 0.026 | 3.844 | ## Usage The weights provided here are the LoRA adapter (`new_lang_adapter` directory). They must be loaded on top of the original Chatterbox backbone using the [Chatterbox fine-tuning toolkit](https://github.com/gokhaneraslan/chatterbox-finetuning).
Source context: 0 downloads · 2 likes · Pipeline text-to-speech · Library chatterbox · Repo msingiai/sauti_tts_v2
