NVIDIA Speech AI: Customized, Low-Latency Models for Enterprise Level Conversational AI

Join Travis, Yang Jong, and Jan as they discuss NVIDIA's approach to developing models for speech AI, focusing on customization, variety, and deployment at enterprise levels.

1. Travis, Yang Jong, and Jan are from NVIDIA Repo, focusing on enterprise-level speech AI model deployment.
2. They cover speech translation, text to speech development, speech recognition, and speech translation.
3. The focus is on low latency, highly efficient models for embedded devices.
4. Four categories considered in model development: robustness, coverage, personalization, and deployment cases.
5. Robustness includes noisy environments, sound quality, telephony, and environmental contamination factors.
6. Coverage considers domains like medical, entertainment, call centers, monolingual vs multilingual development, dialects, and code switching.
7. Personalization focuses on target speaker AI, uncommon vocabulary, text normalization FST models, and exact output requirements.
8. Deployment cases involve speed-accuracy trade-offs, high variety vs efficiency, and streaming or offline use cases.
9. CTC (Connectionist Temporal Classification) models are used for non-auto regression decoding in streaming environments.
10. RNN-T (Recurrent Neural Network Transducer) or TDT (Transducer with Deep Transformer) models are used when non-auto regression is not ideal.
11. Attention encoder-decoder setups are used for high accuracy, less alignment focus, and accommodating various tasks in a single model.
12. Fast Conformer models are the backbone of NVIDIA's decoding offerings, allowing subsampling for smaller audio inputs and efficient training.
13. Model offerings are divided into Reva Parakeet (streaming speech recognition) and Rea Canary (accuracy and multitask modeling).
14. Customization is crucial for NVIDIA's speech AI models, focusing on variety and coverage over a unified model.
15. The parakeet ASR model can be extended to multi-speaker and target speaker scenarios with the integration of diarization models.
16. Additional models are offered to improve accuracy, customization, and readability, including voice activity detection, external language model resources, engram based language models, text normali
17. NVIDIA dominates the Hugging Face Open ASR leaderboard with top models due to a focus on customization and variety.
18. Training focuses on data development fundamentals like robustness, multilingual coverage, ear for dialect sensitivity, open-source, and proprietary data.
19. The Nemo research toolkit is used for model training, offering tools for GPU maximization, data bucketing, high-speed data loading, and more.
20. Models undergo thorough bias and domain testing to ensure robustness before deployment.
21. NVIDIA Reva provides low latency and high throughput inference with Tensor optimizations and the NVIDIA Triton inference server.
22. Customization features are available at every stage, including fine-tuning acoustic models, external language models, punctuation models, inverse text normalization models, and word boosting for b
23. NVIDIA Reva is containerized, scalable, and supports various applications, including contact centers, consumer applications, and video conferencing.
24. Customers can visit the NVIDIA website to explore available Reva models, find quick starter guides, developer forums, fine-tuning guides, and pre-built container industry standard API support for

Source: AI Engineer via YouTube

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