AI Research & Model Optimization • 6-week • 4 Specialists

Automatic Speech Recognition (ASR)

Model Fine-Tuning & Optimization (NVIDIA NeMo)

We designed and fine-tuned a production-grade Automatic Speech Recognition (ASR) system using NVIDIA NeMo, implementing both CTC and Transducer (RNNT) architectures for high-accuracy and low-latency streaming speech recognition. The solution includes a complete end-to-end training pipeline from data preprocessing to optimized model export.

3.44% WER

dev-clean (RNNT)

3.61% WER

dev-other (RNNT)

~6.1% WER

dev-other (CTC – robust noisy speech)

AI Features Delivered

End-to-End ASR Pipeline

Complete data preparation, preprocessing, manifest generation, and model training workflow.

Dual Architecture Implementation

Implemented both FastConformer-CTC and FastConformer-Transducer (RNNT).

Dual Architecture Implementation

Implemented both FastConformer-CTC and FastConformer-Transducer (RNNT).

GPU-Optimized Training

bf16 mixed precision training with gradient accumulation for efficient large-model training.

Robust Noise Evaluation

Focused evaluation on dev-other dataset to ensure real-world speech robustness.

Export & Production Readiness

Checkpointing, resume-training support, and export to .nemo format for deployment.

Measurable Results

Before vs After Automatic Speech Recognition (ASR)

Impact Analysis

Metric	Before	After Automatic Speech Recognition (ASR)
Word Error Rate (dev-clean)	Higher baseline WER	3.44% WER
Word Error Rate (dev-other)	Reduced robustness	3.61% WER
Noisy Speech Handling	Limited	Strong performance on dev-other
Streaming Capability	Partial	Optimized RNNT streaming support
Streaming Capability	Partial	Optimized RNNT streaming support

Key Outcomes

Achieved sub-4% WER on clean speech

Strong robustness on noisy real-world speech

Streaming-ready RNNT architecture

Optimized GPU training pipeline

Custom tokenizer adaptation for domain flexibility

Export-ready ASR model for production environments

Full-Service Development

Delivered by Agent Architects

Real transformation across all key business metrics

Complete Development Package

Python for ML pipeline orchestration

PyTorch for deep learning model training

NVIDIA NeMo for ASR architecture implementation

FastConformer (CTC & RNNT) models

SentencePiece for BPE tokenizer training

HuggingFace Datasets for LibriSpeech handling

LibriSpeech (train-clean-100, dev-clean, dev-other)

TensorBoard for experiment tracking & evaluation

Mixed precision training (bf16)

Gradient accumulation & checkpoint optimization

The ASR system delivered accuracy beyond our expectations. The fine-tuning and optimization work by Agents Architects made the model production-ready and robust for real-world speech. We truly appreciate their deep ML expertise.

Head of AI Research,

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