Deploying speech-to-speech on an endpoint #2363
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| docker push andito/speech-to-speech:latest | ||
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| With the Docker image built and pushed, it’s ready to be used in the Hugging Face Inference Endpoint. By using this pre-built image, the endpoint can launch faster and run more efficiently, as all dependencies and data are pre-packaged within the image. |
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is it true that the endpoint launches faster? why? it would be good to back up the 'more efficiently' claim if we can.
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If you don't do this, then when you launch the endpoint it has to install the dependencies. That makes start-up take longer. If your instance has a disk, then maybe the cost is mostly on the first run, but still each time you load the startup script it will recheck that it has the dependencies.
| - e.g. `speech-to-speech-demo` | ||
| - Keep it lower-case and short | ||
| - Choose your preferred Cloud and Hardware - We used `AWS` `GPU` `L4` | ||
| - It's only `$0.80` an hour and is big enough to handle the models |
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is there anything we can say in terms of guidance for how to select 'big enough' hardware?
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perhaps something high-levell re: which part of the pipeline drives most of the workload?
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Sure! The LLM is the part that makes you want to look at bigger instances here.
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Make sure to add an entry in _blog.yaml
ref: https://github.com/huggingface/blog?tab=readme-ov-file#how-to-write-an-article-
| 3. Receive the audio responses from the server | ||
| 4. Playback the audio responses | ||
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| The audio is recorded on the `audio_input_callback` method, it simply submits all chunks to a queue. Then, it is sent to the server with the `send_audio` method. Here, if there is no audio to send, we still submit an empty array in order to receive a response from the server. The responses from the server are handled by the `on_message` method we saw earlier in the blog. Then, the playback of the audio responses are handled by the `audio_output_callback` method. Here we only need to ensure that the audio is in the range we expect (We don't want to destroy someone eardrum's because of a faulty package!) and ensure that the size of the output array is what the playback library expects. |
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Maybe a short conclusion and links again to all the useful repos. Could also let users know to open a discussion on a repo if they run into issues or have questions
Done! |
| - audio | ||
| - speech-to-speech | ||
| - inference | ||
| - inference-endpoints |
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@andimarafioti @dmaniloff , are there any other tags you can think of?
Full list here: https://huggingface.slack.com/archives/C01BWJU0YKW/p1724308338706409?thread_ts=1724308331.622869&cid=C01BWJU0YKW
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Those are great, thank you!
Co-authored-by: Diego Maniloff <[email protected]>
Co-authored-by: Derek <[email protected]>
Co-authored-by: Diego Maniloff <[email protected]>
Co-authored-by: Diego Maniloff <[email protected]>
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| title: "Deploying Speech-to-Speech on Hugging Face Inference Endpoints with a Custom Docker Container" | ||
| author: andito | ||
| thumbnail: /blog/assets/s2s_endpoint/thumbnail.png | ||
| date: October 1, 2024 |
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I reckon this should be updated to October 22, 2024 @andimarafioti
How to deploy a complex application on an inference endpoint. We created a custom docker container and custom handler.