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  base_model:
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  - facebook/mms-tts-div
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  ---
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- # 🎙️ Divehi TTS – Male Voice (VITS-based)
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  This is a fine-tuned VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) model for Divehi speech synthesis. The model produces Male voice audio from Thaana-scripted Divehi text. Fine-tuned from Meta’s MMS-TTS architecture using a curated dataset of synthetic Divehi speech.
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- ## 🧠 Model Details
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  | Field | Value |
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  |----------------------|-------------------------------------------------|
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  | **Inference Engine** | Transformers (🤗 Hugging Face) |
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- ## 🚀 Usage
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  ```python
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  from transformers import VitsModel, VitsTokenizer
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  torchaudio.save("output.wav", waveform.unsqueeze(0), 16000)
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  ```
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- ## 📊 Evaluation Summary
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  - **Model**: `alakxender/mms-tts-div-ft-spk01-m01`
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  - **Evaluated Samples**: 3
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- ## 🙏 Acknowledgements
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  - [Meta MMS-TTS](https://github.com/facebookresearch/fairseq/tree/main/examples/mms)
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  - [Tarepan's SpeechMOS](https://github.com/Tarepan/SpeechMOS)
 
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  base_model:
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  - facebook/mms-tts-div
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  ---
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+ # Divehi TTS – Male Voice (VITS-based)
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  This is a fine-tuned VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) model for Divehi speech synthesis. The model produces Male voice audio from Thaana-scripted Divehi text. Fine-tuned from Meta’s MMS-TTS architecture using a curated dataset of synthetic Divehi speech.
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+ ## Model Details
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  | Field | Value |
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  |----------------------|-------------------------------------------------|
 
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  | **Inference Engine** | Transformers (🤗 Hugging Face) |
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+ ## Usage
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  ```python
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  from transformers import VitsModel, VitsTokenizer
 
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  torchaudio.save("output.wav", waveform.unsqueeze(0), 16000)
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  ```
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+ ## Evaluation Summary
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  - **Model**: `alakxender/mms-tts-div-ft-spk01-m01`
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  - **Evaluated Samples**: 3
 
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+ ## Acknowledgements
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  - [Meta MMS-TTS](https://github.com/facebookresearch/fairseq/tree/main/examples/mms)
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  - [Tarepan's SpeechMOS](https://github.com/Tarepan/SpeechMOS)