st standalone model

examples/asr/conf/speech_translation/fast-conformer_transformer.yaml

@@ -0,0 +1,218 @@
+# It contains the default values for training an autoregressive FastConformer-Transformer ST model with 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.
+# Here are the recommended configs for different variants of FastConformer-Transformer, other parameters are the same as in this config file.
+# One extra (linear projection) layer is added between FastConformer encoder and Transformer decoder if they have different hidden sizes
+# It is recommended to initialize FastConformer with ASR pre-trained encoder for better accuracy and faster convergence
+
+name: "FastConformer-Transformer-BPE-st"
+
+# Initialize model encoder with pre-trained ASR FastConformer encoder for faster convergence and improved accuracy
+init_from_nemo_model:
+  model0:
+    path: ???
+    include: ["preprocessor", "encoder"]
+
+model:
+  sample_rate: 16000
+  label_smoothing: 0.0
+  log_prediction: true # enables logging sample predictions in the output during training
+
+  train_ds:
+    is_tarred: true
+    tarred_audio_filepaths: ???
+    manifest_filepath: ???
+    sample_rate: 16000
+    shuffle: false
+    trim_silence: false
+    batch_size: 4
+    num_workers: 8
+
+  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: 4
+    pin_memory: true
+    use_start_end_token: true
+
+  test_ds:
+    manifest_filepath: ???
+    sample_rate: ${model.sample_rate}
+    batch_size: 16 # you may increase batch_size if your memory allows
+    shuffle: false
+    num_workers: 4
+    pin_memory: true
+    use_start_end_token: true
+
+  # recommend small vocab size of 128 or 256 when using 4x sub-sampling
+  # 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: 17
+    d_model: 512
+
+    # Sub-sampling params
+    subsampling: dw_striding # vggnet or striding, vggnet may give better results but needs more memory
+    subsampling_factor: 8 # must be power of 2
+    subsampling_conv_channels: 256 # -1 sets it to d_model
+    causal_downsampling: false
+    reduction: null
+    reduction_position: null
+    reduction_factor: 1
+
+    # 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
+    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
+    conv_context_size: null
+
+    ### regularization
+    dropout: 0.1 # The dropout used in most of the Conformer Modules
+    dropout_pre_encoder: 0.1
+    dropout_emb: 0.0 # The dropout used for embeddings
+    dropout_att: 0.1 # The dropout for multi-headed attention modules
+
+  transf_encoder:
+    num_layers: 0
+    hidden_size: 512
+    inner_size: 2048
+    num_attention_heads: 8
+    ffn_dropout: 0.1
+    attn_score_dropout: 0.1
+    attn_layer_dropout: 0.1
+
+  transf_decoder:
+    library: nemo
+    model_name: null
+    pretrained: false
+    max_sequence_length: 512
+    num_token_types: 0
+    embedding_dropout: 0.1
+    learn_positional_encodings: false
+    hidden_size: 512
+    inner_size: 2048
+    num_layers: 6
+    num_attention_heads: 4
+    ffn_dropout: 0.1
+    attn_score_dropout: 0.1
+    attn_layer_dropout: 0.1
+    hidden_act: relu
+    pre_ln: true
+    pre_ln_final_layer_norm: true
+
+  head:
+    num_layers: 1
+    activation: relu
+    log_softmax: true
+    dropout: 0.0
+    use_transformer_init: true
+
+  beam_search:
+    beam_size: 4
+    len_pen: 0.0
+    max_generation_delta: 50
+
+  optim:
+    name: adam
+    lr: 0.0001
+    # 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: InverseSquareRootAnnealing
+      #d_model: ${model.encoder.d_model}
+      # scheduler config override
+      warmup_steps: 1000
+      warmup_ratio: null
+      min_lr: 1e-6
+
+trainer:
+  gpus: -1 # number of GPUs, -1 would use all available GPUs
+  num_nodes: 1
+  max_epochs: 100
+  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: 16 # Should be set to 16 for O1 and O2 to enable the AMP.
+  log_every_n_steps: 100  # 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
+
+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_sacreBLEU"
+    mode: "max"
+    save_top_k: 3
+    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

examples/asr/speech_translation/speech_to_text_transformer.py

@@ -0,0 +1,70 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+# Training the model
+```sh
+python speech_to_text_transformer.py \
+    # (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
+    model.train_ds.audio.tarred_audio_filepaths=<path to tar files with audio> \
+    model.train_ds.audio_manifest_filepath=<path to audio data manifest> \
+    model.validation_ds.manifest_filepath=<path to validation manifest> \
+    model.test_ds.manifest_filepath=<path to test manifest> \
+    model.tokenizer.dir=<path to directory of tokenizer (not full path to the vocab file!)> \
+    model.tokenizer.model_path=<path to speech tokenizer model> \
+    model.tokenizer.type=<either bpe, wpe, or yttm> \
+    trainer.gpus=-1 \
+    trainer.accelerator="ddp" \
+    trainer.max_epochs=100 \
+    model.optim.name="adamw" \
+    model.optim.lr=0.001 \
+    model.optim.betas=[0.9,0.999] \
+    model.optim.weight_decay=0.0001 \
+    model.optim.sched.warmup_steps=2000
+    exp_manager.create_wandb_logger=True \
+    exp_manager.wandb_logger_kwargs.name="<Name of experiment>" \
+    exp_manager.wandb_logger_kwargs.project="<Name of project>"
+```
+
+
+"""
+
+import pytorch_lightning as pl
+from omegaconf import OmegaConf
+
+from nemo.collections.asr.models import EncDecTransfModelBPE
+from nemo.core.config import hydra_runner
+from nemo.utils import logging
+from nemo.utils.exp_manager import exp_manager
+
+
+@hydra_runner(config_path="../conf/speech_translation/", config_name="fast-conformer_transformer")
+def main(cfg):
+    logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
+
+    trainer = pl.Trainer(**cfg.trainer)
+    exp_manager(trainer, cfg.get("exp_manager", None))
+    asr_model = EncDecTransfModelBPE(cfg=cfg.model, trainer=trainer)
+
+    # Initialize the weights of the model from another model, if provided via config
+    asr_model.maybe_init_from_pretrained_checkpoint(cfg)
+    trainer.fit(asr_model)
+
+    if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
+        if asr_model.prepare_test(trainer):
+            trainer.test(asr_model)
+
+
+if __name__ == '__main__':
+    main()