Deploying speech-to-text models in production requires a nuanced understanding of the trade-offs between accuracy, latency, and computational resources. Our production deployments at Lexia have taught us that achieving sub-5% WER while maintaining real-time processing capabilities demands careful architectural choices and optimization strategies.
Model quantization is perhaps the most impactful optimization technique for production deployments. By converting floating-point weights to INT8 or even INT4 representations, we can achieve 2-4x reduction in model size and inference time with minimal accuracy loss. However, this requires careful calibration using representative datasets to minimize quantization errors. We've developed a proprietary quantization-aware training pipeline that maintains 99.7% of original model accuracy after INT8 quantization.
Dynamic batching is another critical optimization. Traditional inference processes one audio stream at a time, leading to GPU underutilization. By implementing dynamic batching with padding strategies that handle variable-length audio inputs, we've achieved 40-60% improvement in throughput. Our implementation uses adaptive padding that considers both audio length and batch size to minimize wasted computation while maintaining real-time latency guarantees.
The architecture of the inference pipeline itself requires careful design. We've moved away from synchronous processing to asynchronous pipelines where audio preprocessing, model inference, and post-processing happen in parallel. This pipeline architecture, combined with message queuing systems like RabbitMQ, enables us to handle burst traffic while maintaining consistent latency distributions. Our P99 latency (99th percentile) remains under 300ms even during 10x traffic spikes.
Caching strategies play an underappreciated role in production optimization. For repetitive audio content—common in call center environments where greeting messages and standard responses are frequent—we maintain an LRU cache of transcriptions. This reduces redundant computation by 15-25% in typical enterprise scenarios. The cache key incorporates audio fingerprinting using perceptual hashing algorithms to handle slight variations in audio quality.
Model ensembling, while computationally expensive, can provide significant accuracy improvements in production. We deploy lightweight ensemble methods that combine predictions from base models with domain-specific fine-tuned variants. The ensemble weights are learned through validation data, and we've found that even simple weighted averaging can reduce WER by 0.5-1 percentage point. However, this comes with increased computational cost, so we reserve ensembling for high-value use cases where accuracy is paramount.
Monitoring and observability are non-negotiable for production deployments. We instrument our inference pipelines with detailed metrics: per-model latency distributions, accuracy metrics calculated on a held-out validation set updated weekly, GPU utilization, memory consumption, and error rates. Anomaly detection algorithms flag performance degradation automatically, enabling proactive model retraining or fallback strategies.
A/B testing infrastructure allows us to safely deploy model improvements. We route a small percentage of traffic to new model versions, comparing WER, latency, and downstream metrics (like user satisfaction scores) against production models. Statistical significance testing ensures we only promote improvements that provide genuine value.