Speech Language Model

This template contains two drivers for different SpeechLM training interfaces:

Driver	Python entry point	Use
speechlm.sh	espnet2.bin.speechlm_train	Existing staged recipes, such as LibriTTS and mini_an4, with the traditional ESPnet environment and job launcher
train.sh	espnet2.speechlm.bin.train	Bagpiper and Bagpiper-TTS training with TorchTitan, prepared dataset manifests, and length statistics

The drivers have different configuration and data interfaces. Keep using speechlm.sh for recipes built around it; train.sh provides the entry point for the current SpeechLM trainer. The existing setup.sh scaffolds the traditional speechlm.sh layout.

Training-only recipes

Bagpiper and Bagpiper-TTS contain run.sh, conf/, a README, and symlinks to the shared train.sh and utils. Their inputs are already prepared, so the data-preparation and cluster-launch files are unnecessary:

• Environment: there is no path.sh to activate tools/venv. Activate the environment from the SpeechLM installation guide first, or pass --python /path/to/env/bin/python. train.sh adds the repository root to PYTHONPATH and uses that Python interpreter.
• Launch: there is no cmd.sh, ${cuda_cmd}, run.pl, or slurm.pl dispatch. train.sh invokes torchrun directly. For multiple nodes, start the command on every allocated node with the same --num-nodes, --master-addr, and --master-port, and a distinct --node-rank. --ngpu means GPUs per node; allocate nodes with your scheduler before launching.
• Data: there is no local/data.sh, db.sh, or --stage pipeline. Supply prepared manifests and length statistics directly. The local/, scripts, pyscripts, steps, and cluster configuration files used by the traditional pipeline are therefore omitted.

The default configuration is conf/train.yaml; additional stages live in conf/tuning/. See each recipe's README for stage selection and checkpoint usage.