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82 lines
1.5 KiB
Markdown
82 lines
1.5 KiB
Markdown
# Reflector GPU implementation - Transcription and LLM
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This repository hold an API for the GPU implementation of the Reflector API service,
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and use [Modal.com](https://modal.com)
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- `reflector_llm.py` - LLM API
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- `reflector_transcriber.py` - Transcription API
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## Modal.com deployment
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Create a modal secret, and name it `reflector-gpu`.
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It should contain an `REFLECTOR_APIKEY` environment variable with a value.
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The deployment is done using [Modal.com](https://modal.com) service.
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```
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$ modal deploy reflector_transcriber.py
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...
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└── 🔨 Created web => https://xxxx--reflector-transcriber-web.modal.run
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$ modal deploy reflector_llm.py
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...
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└── 🔨 Created web => https://xxxx--reflector-llm-web.modal.run
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```
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Then in your reflector api configuration `.env`, you can set theses keys:
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```
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TRANSCRIPT_BACKEND=modal
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TRANSCRIPT_URL=https://xxxx--reflector-transcriber-web.modal.run
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TRANSCRIPT_MODAL_API_KEY=REFLECTOR_APIKEY
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LLM_BACKEND=modal
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LLM_URL=https://xxxx--reflector-llm-web.modal.run
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LLM_MODAL_API_KEY=REFLECTOR_APIKEY
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```
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## API
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Authentication must be passed with the `Authorization` header, using the `bearer` scheme.
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```
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Authorization: bearer <REFLECTOR_APIKEY>
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```
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### LLM
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`POST /llm`
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**request**
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```
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{
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"prompt": "xxx"
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}
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```
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**response**
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```
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{
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"text": "xxx completed"
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}
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```
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### Transcription
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`POST /transcribe`
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**request** (multipart/form-data)
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- `file` - audio file
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- `language` - language code (e.g. `en`)
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**response**
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```
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{
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"text": "xxx",
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"words": [
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{"text": "xxx", "start": 0.0, "end": 1.0}
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]
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}
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```
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