fast conformer configs and doc (#5970) (#5997)

* fast conformer configs and doc



* feedback



* adding fast conformer to main README



* path changes



* rewording



* further doc changes



* naming

---------

Signed-off-by: Dima Rekesh <[email protected]>
Co-authored-by: Dima Rekesh <[email protected]>
Co-authored-by: Dima Rekesh <[email protected]>

README.rst

@@ -63,7 +63,7 @@ Key Features
 * Speech processing
     * `HuggingFace Space for Audio Transcription (File, Microphone and YouTube) <https://huggingface.co/spaces/smajumdar/nemo_multilingual_language_id>`_
     * `Automatic Speech Recognition (ASR) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/intro.html>`_
-        * Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC, ...
+        * Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC, FastConformer-CTC, FastConformer-Transducer...
         * Supports CTC and Transducer/RNNT losses/decoders
             * NeMo Original `Multi-blank Transducers <https://arxiv.org/abs/2211.03541>`_
         * Beam Search decoding

docs/source/asr/models.rst

@@ -128,6 +128,26 @@ You may find the example config files of Conformer-Transducer model with charact
 ``<NeMo_git_root>/examples/asr/conf/conformer/conformer_transducer_char.yaml`` and
 with sub-word encoding at ``<NeMo_git_root>/examples/asr/conf/conformer/conformer_transducer_bpe.yaml``.
 
+Fast-Conformer
+--------------
+
+The Fast Conformer (CTC and RNNT) models have a faster version of the Conformer encoder and differ from it as follows:
+
+* 8x depthwise convolutional subsampling with 256 channels
+* Reduced convolutional kernel size of 9 in the conformer blocks
+
+The Fast Conformer encoder is about 2.4x faster than the regular Conformer encoder without a significant model quality degradation.
+128 subsampling channels yield a 2.7x speedup vs baseline but model quality starts to degrade.
+With local attention, inference is possible on audios >1 hrs (256 subsampling channels) / >2 hrs (128 channels).
+
+Fast Conformer models were trained using CosineAnnealing (instead of Noam) as the scheduler.
+
+You may find the example CTC config at 
+``<NeMo_git_root>/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml`` and
+the transducer config at ``<NeMo_git_root>/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml``
+
+Note that both configs are subword-based (BPE).
+
 Cache-aware Streaming Conformer
 -------------------------------
 

examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml

@@ -0,0 +1,187 @@
+# It contains the default values for training a Fast Conformer-CTC ASR model, large size (~120M) with CTC loss and sub-word encoding.
+
+# Architecture and training config:
+# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective
+# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches.
+
+# You may find more info about Fast Conformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer
+
+name: "FastConformer-CTC-BPE"
+
+model:
+  sample_rate: 16000
+  log_prediction: true # enables logging sample predictions in the output during training
+  ctc_reduction: 'mean_volume'
+  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
+    use_start_end_token: false
+    trim_silence: false
+    max_duration: 16.7 # it is set for LibriSpeech, 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: "fully_randomized"
+    bucketing_batch_size: null
+
+  validation_ds:
+    manifest_filepath: ???
+    sample_rate: ${model.sample_rate}
+    batch_size: 16 # you may increase batch_size if your memory allows
+    shuffle: false
+    num_workers: 8
+    pin_memory: true
+    use_start_end_token: false
+
+  test_ds:
+    manifest_filepath: null
+    sample_rate: ${model.sample_rate}
+    batch_size: 16 # you may increase batch_size if your memory allows
+    shuffle: false
+    num_workers: 8
+    pin_memory: true
+    use_start_end_token: false
+
+  # recommend vocab size of 128 or 256 when training on ~1k hr datasets and 1k vocab size on 10+k hr datasets
+  # you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
+  tokenizer:
+    dir: ???  # path to directory which contains either tokenizer.model (bpe) or vocab.txt (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: "per_feature"
+    window_size: 0.025
+    window_stride: 0.01
+    window: "hann"
+    features: 80
+    n_fft: 512
+    log: true
+    frame_splicing: 1
+    dither: 0.00001
+    pad_to: 0
+    pad_value: 0.0
+
+  spec_augment:
+    _target_: nemo.collections.asr.modules.SpectrogramAugmentation
+    freq_masks: 2 # set to zero to disable it
+    # you may use lower time_masks for smaller models to have a faster convergence
+    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: 18
+    d_model: 512
+
+    # Sub-sampling params
+    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 # -1 sets it to d_model
+    causal_downsampling: false
+
+    # 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
+    att_context_size: [-1, -1] # -1 means unlimited context
+    att_context_style: regular # regular or chunked_limited
+    xscaling: true # scales up the input embeddings by sqrt(d_model)
+    untie_biases: true # unties the biases of the TransformerXL layers
+    pos_emb_max_len: 5000
+
+    # Convolution module's params
+    conv_kernel_size: 9
+    conv_norm_type: 'batch_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]
+    conv_context_size: null
+
+    ### 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
+
+  decoder:
+    _target_: nemo.collections.asr.modules.ConvASRDecoder
+    feat_in: null
+    num_classes: -1
+    vocabulary: []
+
+  optim:
+    name: adamw
+    lr: 1e-3
+    # optimizer arguments
+    betas: [0.9, 0.98]
+    # less necessity for weight_decay as we already have large augmentations with SpecAug
+    # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
+    # weight decay of 0.0 with lr of 2.0 also works fine
+    weight_decay: 1e-3
+
+    # scheduler setup
+    sched:
+      name: CosineAnnealing
+      # scheduler config override
+      warmup_steps: 15000
+      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: 0.0
+  precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
+  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
+
+  # you need to set these two to True to continue the training
+  resume_if_exists: false
+  resume_ignore_no_checkpoint: false
+
+  # You may use this section to create a W&B logger
+  create_wandb_logger: false
+  wandb_logger_kwargs:
+    name: null
+    project: null