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ThisSpeechBrain recipe includes scripts to train end-to-end transducer-based target speaker automaticspeech recognition (TS-ASR) systems as proposed inStreaming Target-Speaker ASR with Neural Transducer.
Generate the LibriSpeechMix data in<path-to-data-folder> following theofficial readme.
Clone the repository, navigate to<path-to-repository>, open a terminal and run:
pip install -e vendor/speechbrainpip install -r requirements.txt
Navigate to<path-to-repository>, open a terminal and run:
python train_<dataset>_<variant>.py hparams/<dataset>/<config>.yaml --data_folder<path-to-data-folder>
To use multiple GPUs on the same node, run:
python -m torch.distributed.launch --nproc_per_node=<num-gpus> \train_<dataset>_<variant>.py hparams/<dataset>/<config>.yaml --data_folder<path-to-data-folder> --distributed_launch
To use multiple GPUs on multiple nodes, for each node with rank0, ..., <num-nodes> - 1 run:
python -m torch.distributed.launch --nproc_per_node=<num-gpus-per-node> \--nnodes=<num-nodes> --node_rank=<node-rank> --master_addr<rank-0-ip-addr> --master_port 5555 \train_<dataset>_<variant>.py hparams/<dataset>/<config>.yaml --data_folder<path-to-data-folder> --distributed_launch
Helper functions and scripts for plotting and analyzing the results can be found inutils.py andtools.
NOTE: the vendored version of SpeechBrain inside this repository includes several hotfixes (e.g. distributed training,gradient clipping, gradient accumulation, causality, etc.) and additional features (e.g. distributed evaluation).
nohup python -m torch.distributed.launch --nproc_per_node=8 \train_librispeechmix_scratch.py hparams/LibriSpeechMix/conformer-t_scratch.yaml \--data_folder datasets/LibriSpeechMix --num_epochs 100 \--distributed_launch&About
Target speaker automatic speech recognition (TS-ASR)
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