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  # KhanomTan TTS v1.0
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- KhanomTan TTS (ขนมตาล) is a open-source Thai text-to-speech model that supports multilingual speakers. It supports Thai, English, and others.
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- KhanomTan TTS is a YourTTS model that trained with supports Thai. We add the Thai speech corpus from TSync 1* and TSync 2* to [mbarnig/lb-de-fr-en-pt-12800-TTS-CORPUS](https://huggingface.co/datasets/mbarnig/lb-de-fr-en-pt-12800-TTS-CORPUS) that train the model with YourTTS model.
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- ## Config
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- We have Thai characters to the graphemes config for training the model and use the Speaker Encoder model from the speaker encoder model from [🐸 Coqui-TTS](https://github.com/coqui-ai/TTS/releases/tag/speaker_encoder_model).
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- ## Dataset
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- We add Tsync 1 corpus and Tsync 2 corpus, which are not complete datasets, and then add those to [mbarnig/lb-de-fr-en-pt-12800-TTS-CORPUS](https://huggingface.co/datasets/mbarnig/lb-de-fr-en-pt-12800-TTS-CORPUS) dataset.
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- ## Trained the model
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  We use the 🐸 Coqui-TTS multilingual VITS-model recipe (version 0.7.1 or the commit id is d46fbc240ccf21797d42ac26cb27eb0b9f8d31c4) for training the model, and we use the speaker encoder model from [🐸 Coqui-TTS](https://github.com/coqui-ai/TTS/releases/tag/speaker_encoder_model) then we release the best model to public access.
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  - Model cards: [https://github.com/wannaphong/KhanomTan-TTS-v1.0](https://github.com/wannaphong/KhanomTan-TTS-v1.0)
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  # KhanomTan TTS v1.0
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+ KhanomTan TTS (ขนมตาล) is an open-source Thai text-to-speech model that supports multilingual speakers such as Thai, English, and others.
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+ KhanomTan TTS is a YourTTS model trained on multilingual languages that supports Thai. We use Thai speech corpora, TSync 1* and TSync 2* [mbarnig/lb-de-fr-en-pt-12800-TTS-CORPUS](https://huggingface.co/datasets/mbarnig/lb-de-fr-en-pt-12800-TTS-CORPUS) to train the YourTTS model by using code from [the 🐸 Coqui-TTS](https://github.com/coqui-ai/TTS).
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+ ### Config
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+ We use Thai characters to the graphemes config to training the model and use the Speaker Encoder model from [🐸 Coqui-TTS](https://github.com/coqui-ai/TTS/releases/tag/speaker_encoder_model).
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+ ### Dataset
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+ We use Tsync 1 and Tsync 2 corpora, which are not complete datasets, and then add these to [mbarnig/lb-de-fr-en-pt-12800-TTS-CORPUS](https://huggingface.co/datasets/mbarnig/lb-de-fr-en-pt-12800-TTS-CORPUS) dataset.
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+ ### Trained the model
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  We use the 🐸 Coqui-TTS multilingual VITS-model recipe (version 0.7.1 or the commit id is d46fbc240ccf21797d42ac26cb27eb0b9f8d31c4) for training the model, and we use the speaker encoder model from [🐸 Coqui-TTS](https://github.com/coqui-ai/TTS/releases/tag/speaker_encoder_model) then we release the best model to public access.
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  - Model cards: [https://github.com/wannaphong/KhanomTan-TTS-v1.0](https://github.com/wannaphong/KhanomTan-TTS-v1.0)