Lexia's speech recognition models on Hugging Face represent years of research and optimization specifically targeting enterprise use cases. While base models like Whisper provide excellent general-purpose transcription, enterprise requirements demand domain-specific accuracy, multilingual capabilities, and optimized inference performance. Our published models address these needs through careful fine-tuning, architectural improvements, and extensive evaluation on enterprise datasets.
The base architecture leverages transformer-based encoder-decoder models, similar to Whisper but with modifications for enterprise requirements. The encoder processes audio spectrograms using convolutional layers followed by transformer blocks, extracting hierarchical audio features. The decoder generates text tokens using cross-attention over encoder outputs. Our modifications include larger encoder dimensions for better acoustic modeling, modified attention mechanisms that reduce computational cost while maintaining accuracy, and specialized tokenizers optimized for technical terminology.
Fine-tuning strategies are critical for enterprise accuracy. We use a multi-stage approach: first fine-tuning on general enterprise speech data (customer calls, meetings, presentations), then domain-specific fine-tuning for specialized industries (medical, legal, technical). This progressive fine-tuning prevents catastrophic forgetting while improving domain-specific performance. We've found that fine-tuning on as little as 50 hours of domain-specific audio can reduce WER by 3-5 percentage points compared to base models.
Multilingual support is essential for global enterprises. Our models support over 50 languages, with varying accuracy levels. High-resource languages (English, French, Spanish, German) achieve WER below 5%, while lower-resource languages may see WER around 8-12%. The multilingual capability comes from training on diverse datasets including Common Voice, VoxPopuli, and proprietary enterprise recordings. Code-switching detection enables handling conversations that mix multiple languages—critical for international business contexts.
Model optimization for production deployment includes quantization, pruning, and distillation. We provide INT8 quantized versions that reduce model size by 4x with minimal accuracy loss (<0.5% WER increase). Knowledge distillation techniques create smaller student models that maintain most of the teacher model's accuracy while being 2-3x faster. These optimizations enable deployment on resource-constrained environments while maintaining enterprise-grade accuracy.
Evaluation metrics go beyond simple WER to assess enterprise suitability. We report WER on multiple test sets: general conversational speech, technical presentations, customer service calls, and multilingual scenarios. Additionally, we measure entity recognition accuracy, as correctly transcribing names, numbers, and technical terms is often more important than overall WER for enterprise use cases. Our models achieve entity-level accuracy above 92% on common enterprise entities.
Custom pipeline development extends beyond model weights. We provide preprocessing pipelines for audio normalization, noise reduction, and voice activity detection. Post-processing includes punctuation restoration, capitalization, and number formatting. These components are often as important as the model itself for producing polished transcriptions suitable for enterprise documentation. Our pipelines are modular, enabling organizations to swap components based on their specific requirements.
Open-source availability on Hugging Face enables enterprises to evaluate, customize, and deploy models with full transparency. Organizations can download models, test them on proprietary data, fine-tune further for specific use cases, and deploy on their own infrastructure. This transparency builds trust and enables compliance with regulations requiring visibility into AI systems. We actively maintain models, releasing updates as we improve accuracy and efficiency.
Collaboration and community feedback drive continuous improvement. Hugging Face's model hub enables community contributions: users report issues, suggest improvements, and even contribute fine-tuned variants for specific domains. We review community feedback and incorporate improvements into official releases. This collaborative approach has led to specialized variants optimized for specific industries, languages, and use cases beyond what we could develop independently.