updated batch sizes and added fastconformer ctc streaming configs.

Signed-off-by: Vahid <[email protected]>

examples/asr/conf/conformer/conformer_transducer_bpe.yaml

@@ -26,7 +26,7 @@
 # The checkpoint of the large model trained on NeMo ASRSET with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large
 
 # We suggest to use trainer.precision=bf16 for GPUs which support it otherwise trainer.precision=16 is recommended.
-# Using bf16 or 16 would makes it possible to double the batch size and speedup training/inference. If fp16 is not stable and model diverges after some epochs, you may use fp32.
+# Using bf16 or 16 would make it possible to double the batch size and speedup training/inference. If fp16 is not stable and model diverges after some epochs, you may use fp32.
 # Here are the suggested batch size per GPU for each precision and memory sizes:
 #  +-----------+------------+------------+
 #  | Precision | GPU Memory | Batch Size |

examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_ctc_bpe_streaming.yaml

@@ -0,0 +1,198 @@
+# It contains the default values for training a cache-aware streaming FastConformer-CTC ASR model, large size (~115M) with sub-word encoding.
+
+# You may find more detail:
+# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer
+# Cache-aware Conformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer
+# FastConformer-CTC's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml
+
+name: "FastConformer-CTC-BPE-Streaming"
+
+model:
+  sample_rate: 16000
+  log_prediction: true # enables logging sample predictions in the output during training
+  ctc_reduction: 'mean_batch'
+  skip_nan_grad: false
+
+  train_ds:
+    manifest_filepath: ???
+    sample_rate: ${model.sample_rate}
+    batch_size: 16 # you may increase batch_size if your memory allows
+    shuffle: true
+    num_workers: 8
+    pin_memory: true
+    max_duration: 20 # you may need to update it for your dataset
+    min_duration: 0.1
+    # tarred datasets
+    is_tarred: false
+    tarred_audio_filepaths: null
+    shuffle_n: 2048
+    # bucketing params
+    bucketing_strategy: "synced_randomized"
+    bucketing_batch_size: null
+
+  validation_ds:
+    manifest_filepath: ???
+    sample_rate: ${model.sample_rate}
+    batch_size: 16
+    shuffle: false
+    use_start_end_token: false
+    num_workers: 8
+    pin_memory: true
+
+  test_ds:
+    manifest_filepath: null
+    sample_rate: ${model.sample_rate}
+    batch_size: 16
+    shuffle: false
+    use_start_end_token: false
+    num_workers: 8
+    pin_memory: true
+
+  # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
+  # We recommend to use vocab size of 1024 with SPE Unigram for most languages
+  tokenizer:
+    dir: ???  # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
+    type: bpe  # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
+
+  preprocessor:
+    _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
+    sample_rate: ${model.sample_rate}
+    normalize: "NA" # No normalization for mel-spectogram makes streaming easier
+    window_size: 0.025
+    window_stride: 0.01
+    window: "hann"
+    features: 80
+    n_fft: 512
+    frame_splicing: 1
+    dither: 0.00001
+    pad_to: 0
+
+  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: 0.05
+
+  encoder:
+    _target_: nemo.collections.asr.modules.ConformerEncoder
+    feat_in: ${model.preprocessor.features}
+    feat_out: -1 # you may set it if you need different output size other than the default d_model
+    n_layers: 17
+    d_model: 512
+
+    # Sub-sampling parameters
+    subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding
+    subsampling_factor: 8 # must be power of 2 for striding and vggnet
+    subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model
+    causal_downsampling: true
+
+    # Feed forward module's params
+    ff_expansion_factor: 4
+
+    # Multi-headed Attention Module's params
+    self_attention_model: rel_pos # rel_pos or abs_pos
+    n_heads: 8 # may need to be lower for smaller d_models
+
+    # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
+    # for att_context_style=regular, the right context is recommended to be a small number around 0 to 3 as multiple-layers may increase the effective right context too large
+    # for att_context_style=chunked_limited, the left context need to be dividable by the right context plus one
+    # for chunked_limited you may calculate the look-ahead or right context by the following formula:
+    # look-ahead(secs) = att_context_size[1]*subsampling_factor*window_stride, example: 13*8*0.01=1.04s
+    att_context_size: [70, 13] # -1 means unlimited context
+    att_context_style: chunked_limited # regular or chunked_limited
+
+    xscaling: true # scales up the input embeddings by sqrt(d_model)
+    pos_emb_max_len: 5000
+
+    # Convolution module's params
+    conv_kernel_size: 9
+    conv_norm_type: 'layer_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
+
+    # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size
+    # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0]
+    # Recommend to use causal convolutions as it would increase the effective right context and therefore the look-ahead significantly
+    conv_context_size: causal
+
+    ### regularization
+    dropout: 0.1 # The dropout used in most of the Conformer Modules
+    dropout_pre_encoder: 0.1 # The dropout used before the encoder
+    dropout_emb: 0.0 # The dropout used for embeddings
+    dropout_att: 0.1 # The dropout for multi-headed attention modules
+
+    # set to non-zero to enable stochastic depth
+    stochastic_depth_drop_prob: 0.0
+    stochastic_depth_mode: linear  # linear or uniform
+    stochastic_depth_start_layer: 1
+
+  decoder:
+    _target_: nemo.collections.asr.modules.ConvASRDecoder
+    feat_in: null
+    num_classes: -1
+    vocabulary: []
+
+  # config for InterCTC loss: https://arxiv.org/abs/2102.03216
+  # specify loss weights and which layers to use for InterCTC
+  # e.g., to reproduce the paper results, set loss_weights: [0.3]
+  # and apply_at_layers: [8] (assuming 18 layers). Note that final
+  # layer loss coefficient is automatically adjusted (to 0.7 in above example)
+  interctc:
+    loss_weights: []
+    apply_at_layers: []
+
+  optim:
+    name: adamw
+    lr: 2.0
+    # optimizer arguments
+    betas: [0.9, 0.98]
+    weight_decay: 1e-3
+
+    # scheduler setup
+    sched:
+      name: NoamAnnealing
+      d_model: ${model.encoder.d_model}
+      # scheduler config override
+      warmup_steps: 10000
+      warmup_ratio: null
+      min_lr: 1e-6
+
+trainer:
+  devices: -1 # number of GPUs, -1 would use all available GPUs
+  num_nodes: 1
+  max_epochs: 1000
+  max_steps: -1 # computed at runtime if not set
+  val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
+  accelerator: auto
+  strategy: ddp
+  accumulate_grad_batches: 1
+  gradient_clip_val: 1.0
+  precision: 32 # 16, 32, or bf16
+  log_every_n_steps: 10  # Interval of logging.
+  enable_progress_bar: True
+  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.
+  num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
+  check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
+  sync_batchnorm: true
+  enable_checkpointing: False  # Provided by exp_manager
+  logger: false  # Provided by exp_manager
+  benchmark: false # needs to be false for models with variable-length speech input as it slows down training
+
+
+exp_manager:
+  exp_dir: null
+  name: ${name}
+  create_tensorboard_logger: true
+  create_checkpoint_callback: true
+  checkpoint_callback_params:
+    # in case of multiple validation sets, first one is used
+    monitor: "val_wer"
+    mode: "min"
+    save_top_k: 5
+    always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
+  resume_if_exists: false
+  resume_ignore_no_checkpoint: false
+
+  create_wandb_logger: false
+  wandb_logger_kwargs:
+    name: null
+    project: null