speech-to-text-llm

Speech to Text for Transcription Services

This project demonstrates a speech-to-text pipeline using machine learning and audio processing libraries to transcribe spoken language into written text. It is designed for transcription services that require accurate and efficient audio-to-text conversion.

📁 Project Structure

• project_1_Speech_to_Text_for_transcription_services.ipynb: Main notebook containing code for preprocessing, model loading, and transcription.
• README.md: Project overview and instructions.

🧠 Features

• Audio preprocessing using standard techniques (resampling, noise reduction, etc.)
• Speech recognition using state-of-the-art models (e.g., Whisper, Wav2Vec, etc.)
• Visualizations of audio waveforms and spectrograms
• Transcription output in text format

🛠️ Requirements

You can install the dependencies via pip:

pip install torchaudio librosa numpy matplotlib transformers

(Adjust based on exact libraries used in the notebook.)

🚀 Usage

1. Open the notebook in Jupyter:

jupyter notebook project_1_Speech_to_Text_for_transcription_services.ipynb

1. Run the cells in sequence:
   • Load and preprocess audio
   • Transcribe using selected model
   • View results and export transcriptions

🔍 Example Output

Input Audio: sample_audio.wav
Transcribed Text: "Welcome to the meeting. Today we'll be discussing..."

📌 Notes

• Model performance depends on audio quality.
• Future improvements may include multi-language support and speaker diarization.

📄 License

This project is licensed under the MIT License.