Speech to Text
Using
Whisper Model Assistant

  • Category AI & Machine Learning
  • Client Government
  • Start Date January 2024
  • Handover July 2024
What's the

Challenge

A leading government entity sought to enhance its video meeting platform by integrating real-time transcription capabilities. The challenge was to develop a state-of-the-art transcription pipeline capable of handling 8,000 concurrent audio channels in real-time using the Whisper Large V3 model. The goal was to deliver high-accuracy transcriptions, catering to a large user base with diverse accents, languages, and speaking speeds.

Introduction

The client, a major government entity, operates an intranet platform used by officials and ministries countrywide. As part of their continuous improvement efforts, they aimed to enhance their service by providing real-time transcription for video meetings. The project required leveraging the Whisper Large V3 model to deliver high-accuracy transcriptions while ensuring scalability, minimal latency, and seamless integration with the existing infrastructure.

Solution Overview & Methodology

Recognizing the client’s need for advanced transcription capabilities, GrowthGear embarked on a transformative journey to build a robust and scalable transcription pipeline. By harnessing the power of the Whisper Large V3 model and advanced microservices architecture, we ensured high-quality transcriptions and efficient resource management.

Architecture

The architecture was designed to be highly scalable and efficient, leveraging microservices, distributed processing, and advanced load balancing techniques.

The transcription pipeline was built using a microservice architecture to ensure modularity and scalability. Each microservice handled specific tasks such as audio ingestion, preprocessing, transcription, and post-processing.

Audio streams from video meetings were ingested in real-time using a Kafka-based message queue system. This ensured reliable and scalable data streaming.

Audio data was preprocessed to enhance quality and normalize variations in volume and background noise. This step was crucial for improving transcription accuracy.

The Whisper Large V3 model was deployed on a Kubernetes cluster to handle the transcription process. Each instance of the model processed audio segments in parallel, allowing for high concurrency and efficient utilization of resources.

Transcribed text was post-processed to correct common errors, punctuate sentences, and format the output according to client specifications.

- After post-processing, the transcribed text went through a summarization engine where the Llama3 model, based on its context-based learning, summarized the transcribed text. This provided users with a crisp summary of long meetings.
 
- Additionally, an action points generator was created to keep the events in the meeting on track. Based on the context of the meeting, it generated action points and minutes of the meeting, helping users keep their agendas on track.
Advanced load balancing techniques were implemented using Kubernetes' horizontal pod autoscale to dynamically allocate resources based on the current load, ensuring smooth scaling during peak times.

The Implementation Journey

The implementation of the Speech to Text Using Whisper Model Assistant for a Major Government Entity involved a meticulous approach to ensure seamless integration and high accuracy. Our team at GrowthGear focused on fine-tuning the Whisper Large V3 model to accommodate diverse accents and languages, integrating various meeting platforms, and implementing advanced load balancing techniques using Kubernetes. This structured methodology ensured a robust and scalable transcription platform that met the client's requirements. The process was divided into critical phases, each focusing on specific aspects to deliver a successful outcome.

The Whisper Large V3 model was fine-tuned with a diverse dataset that included various accents, languages, and speaking styles. This improved the model's accuracy and robustness.

A RESTful API was developed to facilitate easy integration with the client's existing infrastructure. The API provided endpoints for audio stream submission, transcription retrieval, and real-time updates.

Audio streams were segmented into smaller chunks to enable real-time processing. Each chunk was transcribed independently, and the results were merged to form the final transcript.

Comprehensive monitoring and logging systems were implemented using Prometheus and Grafana. This allowed real-time tracking of system performance, resource utilization, and error rates.

See the Impact:

Results That Speak

Our transcription pipeline delivered exceptional results, empowering the client with unprecedented insights and capabilities. By accurately transcribing audio in real-time and providing dynamic summaries and action points, the organization gained a competitive edge in decision-making.

  • Scalability: The transcription pipeline successfully handled 8,000 concurrent audio channels with minimal latency, demonstrating excellent scalability.
  • Improved Accuracy: The fine-tuned Whisper Large V3 model achieved an accuracy rate of over 95%, surpassing the client's expectations.
  • Reduced Latency: The average transcription latency was reduced to less than 2 seconds, ensuring a seamless real-time experience for users.
  • Resource Efficiency: The Kubernetes-based deployment ensured optimal resource utilization, reducing operational costs by 35%.
  • Enhanced User Satisfaction: The enhanced transcription feature received positive feedback from users, with a significant increase in engagement and satisfaction.

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