examples/speechlm2/conf/salm_asr_decoder.yaml

model:
  # Every name/path here starting with 'pretrained' is used to initialize the model weights.
  pretrained_llm: Qwen/Qwen3-1.7B
  pretrained_asr: nvidia/canary-1b-flash

  pretrained_weights: True  # When False, we use pretrained_name to load the architecture, but with random init

  # Regexp (re.compile) patterns matching parameters to be frozen.
  freeze_params:
    # Frozen LLM
    - "^llm\\..+$"  # LLM
    - "^embed_tokens\\..+$"  # LLM embedding is moved
    # Frozen pretrained ASR (only the modality adapter layers are trainable)
    - "^perception\\.preprocessor\\..+$"
    - "^perception\\.encoder\\..+$"
  prevent_freeze_params: []  # Use to make specific submodules trainable; overrides freeze_params

  prompt_format: qwen
  audio_locator_tag: "<|audioplaceholder|>"  # placeholder token for audio turn is expected

  # Note: Uncomment the block below to enable LoRA on LLM via HuggingFace PEFT library.
  #   It will automatically freeze LLM parameters even if freeze_params was unused,
  #   and prevent freezing any parameter that has the string '.lora_' in its name.
  # lora:
  #   task_type: CAUSAL_LM
  #   r: 128
  #   lora_alpha: 256
  #   lora_dropout: 0.01
  #   # target_modules are only necessary if the `pretrained_llm` is not yet registered in PEFT library
  #   target_modules: ["q_proj", "v_proj"]

  perception:
     target: nemo.collections.speechlm2.modules.perception.AudioPerceptionModule
     output_dim: 2048
     modality_adapter:
       _target_: nemo.collections.speechlm2.modules.perception.IdentityConnector
       d_model: 1024
     # spec_augment:
     #   _target_: nemo.collections.asr.modules.SpectrogramAugmentation
     #   freq_masks: 2 # set to zero to disable it
     #   time_masks: 10 # set to zero to disable it
     #   freq_width: 27
     #   time_width: 5  # 5 frames = 50ms

  optimizer:
    _target_: torch.optim.AdamW
    lr: 5e-4
    betas: [0.9, 0.98]
    weight_decay: 1e-3
    foreach: true # set to false if having issues with tensor-parallelism

  lr_scheduler:
    _target_: nemo.core.optim.lr_scheduler.CosineAnnealing
    warmup_steps: 1000
    min_lr: 1e-6
    max_steps: ${trainer.max_steps}

trainer:
  devices: -1
  accelerator: gpu
  num_nodes: 1
  precision: bf16-true
  logger: False # logger provided by exp_manager
  enable_checkpointing: False
  use_distributed_sampler: False
  max_steps: 100000
  limit_train_batches: 5000  # "epoch" size
  val_check_interval: ${trainer.limit_train_batches}
  limit_val_batches: 10
  log_every_n_steps: 10
  num_sanity_val_steps: 1
  gradient_clip_val: 1.0
  accumulate_grad_batches: 1
  strategy:
    # Replace DDPStrategy with ModelParallelStrategy to enable model parallelism
    _target_: lightning.pytorch.strategies.DDPStrategy
    gradient_as_bucket_view: true
    find_unused_parameters: true
    # _target_: lightning.pytorch.strategies.ModelParallelStrategy
    # tensor_parallel_size: 1
    # data_parallel_size: 8  # This is FSDP2

data:
  train_ds:
    sample_rate: 16000
    prompt_format: ${model.prompt_format}
    token_equivalent_duration: 0.08
    input_cfg:
      - type: lhotse_as_conversation
        cuts_path: ???  # needs to be set
        audio_locator_tag: ${model.audio_locator_tag}
        tags:
          # Uncomment below line to include a system prompt for supported models (e.g. Llama; not Qwen).
          # system_prompt: "some system prompt"
          context: "Repeat after me, typing in lowercase."
    seed: 42
    shuffle: true
    shard_seed: "randomized"
    num_workers: 1
    batch_size: 4
    # Optional bucketing:
    # batch_size: null
    # use_bucketing: true
    # use_multimodal_sampling: true
    # measure_total_length: true
    # Note: `batch_tokens`, `bucket_duration_bins`, and `max_tokens` all represent tokens as
    #  the sum of input audio frames and output text tokens. Number of audio frames is
    #  calculated using `token_equivalent_duration`.
    # batch_tokens: 4000
    # max_tokens: 2048
    # bucket_duration_bins: [64, 128, 256, 384, 512, 768, 1024, 1280, 1536, 2048]
    # num_buckets: 10
    # bucket_buffer_size: 5000

  validation_ds:
    # The entries under 'datasets' are a list of separate dataloaders.
    # The structure is <dataset-name>: {<dataloader-dict-config>}
    # They inherit all settings from validation_ds, but can individually override them.
    prompt_format: ${model.prompt_format}
    token_equivalent_duration: 0.08
    datasets:
      val_set_0:  # rename to your dataset name, add more as needed
        input_cfg:
          - type: lhotse_as_conversation
            cuts_path: ???  # needs to be set
            audio_locator_tag: ${model.audio_locator_tag}
            tags:
              # Uncomment below line to include a system prompt for supported models (e.g. Llama; not Qwen).
              # system_prompt: "some system prompt"
              context: "Repeat after me, typing in lowercase."
    sample_rate: 16000
    batch_size: 1
    seed: 42
    shard_seed: "randomized"

exp_manager:
   exp_dir: null
   explicit_log_dir: salm_results/
   name: salm
   create_tensorboard_logger: false
   create_checkpoint_callback: true
   use_datetime_version: true
   max_time_per_run: 00:03:50:00

   resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
   # you need to set these two to True to continue the training
   resume_if_exists: true
   resume_ignore_no_checkpoint: true

   # You may use this section to create a W&B logger
   create_wandb_logger: false
   wandb_logger_kwargs:
     name: development-run
     project: salm
     resume: true

   checkpoint_callback_params:
     filename: "{step}"
     monitor: val_acc
     mode: max
     every_n_train_steps: null
     every_n_epochs: 1
     save_top_k: 1
     always_save_nemo: false
     save_nemo_on_train_end: false