77fb63372a
* fix: incorrectly changed the base type in my last pull request for LocalAudioTransport * update examples to use the new LocalTransportParams * add local device select example
89 lines
2.3 KiB
Markdown
89 lines
2.3 KiB
Markdown
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# Pipecat Audio Transcription Example 🚀🎙️
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Welcome to the **Pipecat Audio Transcription Example**!
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This project showcases how to integrate the awesome [pipecat](https://github.com/pipecat-ai/pipecat) library with a neat textual interface (powered by [Textual](https://github.com/Textualize/textual)) to select audio devices, perform real-time speech-to-text (STT) transcription using [Whisper](https://github.com/openai/whisper).
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> **Note:** Although the script allows you to select both input and output audio devices, this example only utilizes the audio **input** for transcription.
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---
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## 🎉 Features
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- **Interactive Audio Device Selection:**
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Choose your preferred audio input device using a cool, textual UI.
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- **State-of-the-Art Transcription:**
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Leverage Whisper's large model (running on CUDA) for high-quality, real-time STT.
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- **Live Transcription Logging:**
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Watch your spoken words transform into text on your console instantly.
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- **Easy Setup:**
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Everything you need is in the [`requirements.txt`](./requirements.txt).
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---
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## 🎥 Demo
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Get a quick glimpse of the app in action!
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*(Don't worry – I'll be adding a GIF demo here soon!)*
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---
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## 🔧 Installation
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Install Dependencies:
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```bash
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pip install -r requirements.txt
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```
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---
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## 🚀 Usage
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Run the main script:
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```bash
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python bot.py
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```
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When the app launches, you'll see a textual interface that lets you select your audio input device. Once selected, the app will begin capturing audio, transcribing it using Whisper.
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---
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## ⚙️ How It Works
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1. **LocalAudioTransport:**
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Captures audio from your chosen input device.
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2. **WhisperSTTService:**
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Processes the audio stream using Whisper's large model for speech-to-text conversion.
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3. **TranscriptionLogger:**
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Logs the transcribed text to the console as soon as it's processed.
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---
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## 📦 Dependencies
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The project relies on:
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- [pipecat](https://github.com/yourusername/pipecat) – For building the audio processing pipeline.
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- [Textual](https://github.com/Textualize/textual) – For the interactive terminal UI.
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- [Whisper](https://github.com/openai/whisper) – For state-of-the-art STT transcription.
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---
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## Example improvements:
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I plan to improve this example with local LLM calls and audio output.
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