Real time web based Speech-to-Text app with Streamlit

This repository contains a Streamlit-based application that leverages Azure Speech Services for both Speech-to-Text (STT) and Text-to-Speech (TTS) functionalities. With an intuitive user interface, users can easily transcribe real-time audio or generate speech from entered text using Azure's powerful AI capabilities.

A gui interface for tts (text-to-speech) and stt (speech-to-text) interfaces using the openai api developed by Streamlit, with a history function一个使用Streamlit开发的openai的api接口的tts（文字转语音）和stt（语音转文字）接口的gui界面，带有历史记录功能

In this 4 in 1 project, I have create a 4 features like chatbot, image generation, text-to-speech, speech-to-text using the openai api keys to use the models like whisper, chatgpt, DALL-E-2.

Voicebot - Whisper API(STT), GPT-3.5-turbo/GPT-4, gTTS(TTS), and Streamlit

Using RealtimeSTT to encode speech to text and push results to streamlit

Real Time web based ASR(STT) app with streamlit

🎙️ Voice-enabled agent using Streamlit, Google STT, and ElevenLabs TTS. Built with uv.

This Project Leverages Whisper (STT) & Groq(LLM) to automatically transcribe and summarize meeting audio (live/upload). Delivers AI-generated transcripts & key takeaways via Streamlit front-end.

A prototype for a production-grade Arabic AI Voice Agent for a restaurant. Features real-time SIP call handling, Whisper STT, ElevenLabs TTS, custom NLP, and a Streamlit testing UI.

Whisper-STT-Microservice is a Speech-to-Text service leveraging OpenAI's Whisper model for accurate transcriptions. Featuring a user-friendly Streamlit UI, it allows for seamless audio uploads and browser-based recordings.

This is an innovative application built using Python and Streamlit, which endeavours to bring about Speech To Text Conversion(STT) ,while providing an interactive UI. This application integrates pure Machine Learning code into Web Development.

An AI-powered voice-enabled chatbot built using OpenAI’s GPT, LangChain, Streamlit, and ChromaDB. This application enables real-time conversation using speech-to-text (STT) and text-to-speech (TTS), allowing users to interact naturally with an AI assistant.

I developed a program within the Streamlit environment that leverages Azure’s Text-to-Speech (TTS) and Speech-to-Text (STT) services. This application provides a user-friendly interface for real-time audio transcription and speech synthesis, utilizing Azure’s powerful AI capabilities.

I built a multi-agent finance assistant that delivers spoken market briefs via a Streamlit app. It integrates real-time data APIs, web scraping, and RAG with FAISS. Agents are orchestrated using FastAPI, with Whisper for STT and TTS for responses. The system is modular, open-source, and fully deployed.

Harry-Swarm is a Streamlit app that drives OpenAI’s Responses API with built-in tools: Web Search, File Search, Code Interpreter, and optional Computer Use. It includes a model picker (including GPT-5), STT/TTS audio, a vector-store upload CLI, .env configuration, and a usage/debug panel for cost tracking.

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Formation made at @Sicara on 03/05/2021

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Just a try using Streamlit.

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Free speech-to-text web app built with Streamlit and Hugging Face faster-whisper. Works on Mac (CPU) and Windows (GPU).

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This project uses the Whisper model for audio transcription with a Streamlit-based web interface.

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stt 저장용 새싹 파이널프로젝트