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Merge branch 'main' of https://github.com/SevaSk/ecoute
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@ -75,7 +75,7 @@ Run the main script:
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python main.py
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python main.py
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```
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```
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For a better and faster version, use:
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For a more better and faster version that also works with most languages, use:
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```
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```
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python main.py --api
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python main.py --api
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@ -83,7 +83,7 @@ python main.py --api
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Upon initiation, Ecoute will begin transcribing your microphone input and speaker output in real-time, generating a suggested response based on the conversation. Please note that it might take a few seconds for the system to warm up before the transcription becomes real-time.
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Upon initiation, Ecoute will begin transcribing your microphone input and speaker output in real-time, generating a suggested response based on the conversation. Please note that it might take a few seconds for the system to warm up before the transcription becomes real-time.
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The --api flag will use the whisper api for trnascriptions. This significantly enhances transcription speed and accuracy, and it works in most languages (rather than just English without the flag). It's expected to become the default option in future releases. However, keep in mind that using the Whisper API will consume more OpenAI credits than using the local model. This increased cost is attributed to the advanced features and capabilities that the Whisper API provides. Despite the additional expense, the substantial improvements in speed and transcription accuracy may make it a worthwhile investment for your use case.
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The --api flag will use the whisper api for transcriptions. This significantly enhances transcription speed and accuracy, and it works in most languages (rather than just English without the flag). It's expected to become the default option in future releases. However, keep in mind that using the Whisper API will consume more OpenAI credits than using the local model. This increased cost is attributed to the advanced features and capabilities that the Whisper API provides. Despite the additional expense, the substantial improvements in speed and transcription accuracy may make it a worthwhile investment for your use case.
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### ⚠️ Limitations
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### ⚠️ Limitations
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