  Multi-instrument music transcription aims to convert polyphonic music
recordings into musical scores assigned to each instrument. This task is
challenging for modeling as it requires simultaneously identifying multiple
instruments and transcribing their pitch and precise timing, and the lack of
fully annotated data adds to the training difficulties. This paper introduces
YourMT3+, a suite of models for enhanced multi-instrument music transcription
based on the recent language token decoding approach of MT3. We enhance its
encoder by adopting a hierarchical attention transformer in the time-frequency
domain and integrating a mixture of experts. To address data limitations, we
introduce a new multi-channel decoding method for training with incomplete
annotations and propose intra- and cross-stem augmentation for dataset mixing.
Our experiments demonstrate direct vocal transcription capabilities,
eliminating the need for voice separation pre-processors. Benchmarks across ten
public datasets show our models' competitiveness with, or superiority to,
existing transcription models. Further testing on pop music recordings
highlights the limitations of current models. Fully reproducible code and
datasets are available with demos at \url{https://github.com/mimbres/YourMT3}.
