Transcribe

Transcribe is a real time transcription, conversation, Language learning platform. Transcribe provides live transcripts from microphone and speaker. It generates a suggested conversation response using OpenAI's GPT API. It will read out the responses, simulating a real live conversation in English or another language.

Compatible with Windows.

Features

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Multilingual support

Transcribe supports Multi-Lingual transcription in all languages supported by OpenAI. See the complete list of all languages here.
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Text editing

Transcribe allows users to edit the transcribed text.
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Restore all LLM responses

Users can view earlier LLM responses.
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Response customization

Transcribe users can add contextual information to customize responses, making responses specific to a field or conversation topic.
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Audio customization

Transcribe allows users to selectively enable / disable microphone input or speaker output for Transcription.
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Speech mode

Transcribe users can  get Audio responses in addition to text responses using the Suggest Response and Read button.
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Save content

Transcribe provides options to save content. These options include save all text in transcription window, Save all responses from LLM, copy to clipboard, clear all content manually and clear all content periodically.
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Model selection

Transcribe architecture has two primary components that require model selection, speech to Text and LLM Responses.
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Application configuration

Transcribe is customizable in many different ways. A number of configuration options are specified in parameters.yaml file.
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OpenAI API Compatible Provider Support

Transcribe supports all Providers who are API compatible with OpenAI API specification. Examples are Azure, Perplexity and Together.
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Support for latest GPT models

Transcribe offers support for all of the latest GPT models, including GPT-4o, the newest one.
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Security

Transcribe uses secret scanning, static code analysis, secure transmission and dependency security to ensure security during the development process.

Created by Abhinav Uppal in collaboration with Vivek Uppal

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