Hi TensorSpeech team! I propose building on top of the existing integration with 馃 Hub to enable the Inference API and widgets
Current Status
- With the same PR, users now have access to a code snippet that shows how to load the model. You can use
text-to-mel and mel-to-wav so the code snippet is different.
What can we do next?
Our next step is to integrate it to the Inference API. This would make the widget in the model repos work and allow anyone to make requests to the API, which is very exciting!
The main challenge is that TTS is a two-step process: converting text to mel, and mel to speech. At the moment, every single repo only has one of the two components, so there is no way to make inference work end-to-end.
What do you think of bundling the two models? That is, in a single repo we would have Fastspeech2 and Melgan for example. We would only do this for the repos for which the Inference API is wanted, so it wouldn't be for all of them.
Happy to hear your thoughts,
Omar
cc @patrickvonplaten @julien-c
Hi TensorSpeech team! I propose building on top of the existing integration with 馃 Hub to enable the Inference API and widgets
Current Status
text-to-melandmel-to-wavso the code snippet is different.What can we do next?
Our next step is to integrate it to the Inference API. This would make the widget in the model repos work and allow anyone to make requests to the API, which is very exciting!
The main challenge is that TTS is a two-step process: converting text to mel, and mel to speech. At the moment, every single repo only has one of the two components, so there is no way to make inference work end-to-end.
What do you think of bundling the two models? That is, in a single repo we would have Fastspeech2 and Melgan for example. We would only do this for the repos for which the Inference API is wanted, so it wouldn't be for all of them.
Happy to hear your thoughts,
Omar
cc @patrickvonplaten @julien-c