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- Câu hỏi: So I have trained my own voice model using tacotron 2 but now that I have it I can’t seem to find a way to actually use it in my ai’s voice
- Thời gian hỏi: Hỏi lúc 139 Giờ Trước
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So I have trained my own voice model using tacotron 2 but now that I have it I can’t seem to find a way to actually use it in my ai’s voice
Congratulations on training your own voice model using Tacotron 2! To use the model to generate voice output for your AI, you’ll need to integrate it into your AI’s codebase or architecture.
The specific steps for doing this will depend on the architecture and programming language you are using for your AI, as well as the format in which you saved your trained model. Here are some general steps that you can follow to get started:
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Load your trained model: You’ll need to load your trained Tacotron 2 model into your AI’s codebase. This will involve loading the saved model weights and any other necessary files.
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Convert text input to phonemes: Tacotron 2 operates on a sequence of phonemes (i.e., units of sound) rather than raw text. So, you’ll need to convert any text input you receive into phonemes. You can use a pre-existing library like the CMU Pronouncing Dictionary for this task.
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Generate mel spectrograms: Tacotron 2 takes a sequence of phonemes as input and generates a sequence of mel spectrograms as output. You’ll need to feed the phoneme sequence into your Tacotron 2 model to generate the mel spectrograms.
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Synthesize audio: Finally, you’ll need to use a vocoder (such as WaveGlow or Griffin-Lim) to synthesize audio from the mel spectrograms.
Overall, integrating a voice model into an AI can be a complex task that requires a solid understanding of both machine learning and software engineering. If you’re new to these topics, it may be helpful to work through some tutorials or consult with experienced engineers to help you get started.
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