In this contribution, we present two approaches to efficiently enhance end-to-end (E2E) automatic speech recognition (ASR) models for the Norwegian language. Both utilize multilingual models. First, we demonstrate that model performance can be significantly improved if trained with encoder parameters initialized from models created for other languages. This is true not only for closely related languages, like Swedish or Danish, but also for more or less distant ones, like German, English, Italian, or even Ukrainian. This type of model enhancement is achieved without any additional training data, so it requires no extra computation time. Second, having multiple and differently initialized models for Norwegian offers another advantage. They can be used in data harvesting as multiple checkers to validate correct annotations in parallel. This allows us to acquire a large amount of additional training data automatically from various public sources, such as YouTube, parliament, or government archives. We evaluate our final Norwegian model (trained on 2,599 h) on a diverse 18-h test set and compare its performance to major ASR service providers (Google, Microsoft, Speechmatics) and two fine-tuned Whisper models.