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* fix: refactor modal API key configuration for better separation of concerns - Split generic MODAL_API_KEY into service-specific keys: - TRANSCRIPT_API_KEY for transcription service - DIARIZATION_API_KEY for diarization service - TRANSLATE_API_KEY for translation service - Remove deprecated *_MODAL_API_KEY settings - Add proper validation to ensure URLs are set when using modal processors - Update README with new configuration format BREAKING CHANGE: Configuration keys have changed. Update your .env file: - TRANSCRIPT_MODAL_API_KEY → TRANSCRIPT_API_KEY - LLM_MODAL_API_KEY → (removed, use TRANSCRIPT_API_KEY) - Add DIARIZATION_API_KEY and TRANSLATE_API_KEY if using those services * fix: update Modal backend configuration to use service-specific API keys - Changed from generic MODAL_API_KEY to service-specific keys: - TRANSCRIPT_MODAL_API_KEY for transcription - DIARIZATION_MODAL_API_KEY for diarization - TRANSLATION_MODAL_API_KEY for translation - Updated audio_transcript_modal.py and audio_diarization_modal.py to use modal_api_key parameter - Updated documentation in README.md, CLAUDE.md, and env.example * feat: implement auto/modal pattern for translation processor - Created TranscriptTranslatorAutoProcessor following the same pattern as transcript/diarization - Created TranscriptTranslatorModalProcessor with TRANSLATION_MODAL_API_KEY support - Added TRANSLATION_BACKEND setting (defaults to "modal") - Updated all imports to use TranscriptTranslatorAutoProcessor instead of TranscriptTranslatorProcessor - Updated env.example with TRANSLATION_BACKEND and TRANSLATION_MODAL_API_KEY - Updated test to expect TranscriptTranslatorModalProcessor name - All tests passing * refactor: simplify transcript_translator base class to match other processors - Moved all implementation from base class to modal processor - Base class now only defines abstract _translate method - Follows the same minimal pattern as audio_diarization and audio_transcript base classes - Updated test mock to use _translate instead of get_translation - All tests passing * chore: clean up settings and improve type annotations - Remove deprecated generic API key variables from settings - Add comments to group Modal-specific settings - Improve type annotations for modal_api_key parameters * fix: typing * fix: passing key to openai * test: fix rtc test failing due to change on transcript It also correctly setup database from sqlite, in case our configuration is setup to postgres. * ci: deactivate translation backend by default * test: fix modal->mock * refactor: implementing igor review, mock to passthrough
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Reflector GPU implementation - Transcription and LLM
This repository hold an API for the GPU implementation of the Reflector API service, and use Modal.com
reflector_diarizer.py- Diarization APIreflector_transcriber.py- Transcription APIreflector_translator.py- Translation API
Modal.com deployment
Create a modal secret, and name it reflector-gpu.
It should contain an REFLECTOR_APIKEY environment variable with a value.
The deployment is done using Modal.com service.
$ modal deploy reflector_transcriber.py
...
└── 🔨 Created web => https://xxxx--reflector-transcriber-web.modal.run
$ modal deploy reflector_llm.py
...
└── 🔨 Created web => https://xxxx--reflector-llm-web.modal.run
Then in your reflector api configuration .env, you can set these keys:
TRANSCRIPT_BACKEND=modal
TRANSCRIPT_URL=https://xxxx--reflector-transcriber-web.modal.run
TRANSCRIPT_MODAL_API_KEY=REFLECTOR_APIKEY
DIARIZATION_BACKEND=modal
DIARIZATION_URL=https://xxxx--reflector-diarizer-web.modal.run
DIARIZATION_MODAL_API_KEY=REFLECTOR_APIKEY
TRANSLATION_BACKEND=modal
TRANSLATION_URL=https://xxxx--reflector-translator-web.modal.run
TRANSLATION_MODAL_API_KEY=REFLECTOR_APIKEY
API
Authentication must be passed with the Authorization header, using the bearer scheme.
Authorization: bearer <REFLECTOR_APIKEY>
LLM
POST /llm
request
{
"prompt": "xxx"
}
response
{
"text": "xxx completed"
}
Transcription
POST /transcribe
request (multipart/form-data)
file- audio filelanguage- language code (e.g.en)
response
{
"text": "xxx",
"words": [
{"text": "xxx", "start": 0.0, "end": 1.0}
]
}