Benchmarking and Performance Evaluation for Speech-to-Text Pipeline

Description

Implement a comprehensive benchmarking system to evaluate the performance and accuracy of the speech-to-text pipeline across different transcription models and configurations.

The benchmarking module should measure and display key ASR metrics such as:

• Processing Time
• Real-Time Factor (RTFx)
• Word Error Rate (WER)
• Character Error Rate (CER)
• Model Inference Time
• Memory/CPU Usage
• Language Detection Accuracy
• Speaker Detection Count (if diarization is enabled)

Objectives

• Compare performance between multiple ASR models.
• Track transcription latency and accuracy.
• Identify bottlenecks in the transcription pipeline.
• Provide reproducible benchmark reports for testing and optimization.

Expected Features

• Benchmark execution for uploaded or recorded audio samples.
• Metrics visualization in UI/dashboard.
• Export benchmark results in JSON/CSV format.
• Support benchmarking for different model sizes/configurations.
• Consistent testing workflow for future model evaluations.

Acceptance Criteria

• Benchmarking module successfully records all required metrics.
• Results are displayed clearly in the application UI.
• Benchmark reports can be exported.
• Benchmarking works for both offline and realtime transcription flows.
• No impact on existing transcription functionality.