Add finetuning scripts

Jenkinsfile

@@ -134,7 +134,7 @@ pipeline {
           }
         }
 
-        stage('L2: Speech to Text WPE - CitriNet') {
+        stage('Speech to Text WPE - CitriNet') {
           steps {
             sh 'python examples/asr/asr_ctc/speech_to_text_ctc_bpe.py \
             --config-path="../conf/citrinet/" --config-name="config_bpe" \
@@ -150,7 +150,7 @@ pipeline {
           }
         }
 
-        stage('L2: Speech Pre-training - CitriNet') {
+        stage('Speech Pre-training - CitriNet') {
           steps {
             sh 'python examples/asr/speech_pretraining/speech_pre_training.py \
             --config-path="../conf/ssl/citrinet/" --config-name="citrinet_ssl_ci" \
@@ -164,6 +164,22 @@ pipeline {
           }
         }
 
+        stage('Speech To Text Finetuning') {
+          steps {
+            sh 'python examples/asr/speech_to_text_finetune.py \
+            --config-path="conf" --config-name="speech_to_text_finetune" \
+            model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
+            model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
+            init_from_nemo_model=/home/TestData/asr/stt_en_fastconformer_transducer_large.nemo \
+            model.tokenizer.update_tokenizer=False \
+            trainer.devices=[1] \
+            trainer.accelerator="gpu" \
+            +trainer.fast_dev_run=True \
+            exp_manager.exp_dir=examples/asr/speech_finetuning_results'
+            sh 'rm -rf examples/asr/speech_finetuning_results'
+          }
+        }
+
         // TODO: Please Fix Me
         // Error locating target 'nemo.collections.asr.modules.wav2vec_modules.ConvFeatureEncoder', see chained exception above.
         // stage('L2: Speech Pre-training - Wav2Vec') {

docs/source/asr/configs.rst

@@ -984,8 +984,8 @@ Main parts of the config:
       batch_size: 16 # you may increase batch_size if your memory allows
       # other params
 
-Finetuning
-~~~~~~~~~~~
+Finetuning with Text-Only Data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 To finetune existing ASR model using text-only data use ``<NeMo_git_root>/examples/asr/asr_with_tts/speech_to_text_bpe_with_text_finetune.py`` script with the corresponding config ``<NeMo_git_root>/examples/asr/conf/asr_tts/hybrid_asr_tts.yaml``.
 
@@ -1030,47 +1030,53 @@ Fine-tuning Configurations
 
 All ASR scripts support easy fine-tuning by partially/fully loading the pretrained weights from a checkpoint into the **currently instantiated model**. Note that the currently instantiated model should have parameters that match the pre-trained checkpoint (such that weights may load properly). In order to directly fine-tune a pre-existing checkpoint, please follow the tutorial  `ASR Language Fine-tuning. <https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb>`_
 
-Pre-trained weights can be provided in multiple ways -
+Models can be fine-tuned in two ways: 
+* By updating or retaining current tokenizer alone
+* By updating model architecture and tokenizer
+
+Fine-tuning by updating or retaining current tokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In this case, the model architecture is not updated. The model is initialized with the pre-trained weights by 
+two ways:
 
 1) Providing a path to a NeMo model (via ``init_from_nemo_model``)
 2) Providing a name of a pretrained NeMo model (which will be downloaded via the cloud) (via ``init_from_pretrained_model``)
-3) Providing a path to a Pytorch Lightning checkpoint file (via ``init_from_ptl_ckpt``)
 
-There are multiple ASR subtasks inside the ``examples/asr/`` directory, you can substitute the ``<subtask>`` tag below.
+Then users can use existing tokenizer or update the tokenizer with new vocabulary. This is useful when users don't want to update the model architecture 
+but want to update the tokenizer with new vocabulary.
 
-Fine-tuning via a NeMo model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The same script can be used to finetune CTC, RNNT or Hybrid models as well.
+
+<NeMo_repo>/examples/asr/speech_to_text_finetune.py script supports this type of fine-tuning with the following arguments:
 
 .. code-block:: sh
 
-    python examples/asr/<subtask>/script_to_<script_name>.py \
+    python examples/asr/speech_to_text_finetune.py \
         --config-path=<path to dir of configs> \
         --config-name=<name of config without .yaml>) \
         model.train_ds.manifest_filepath="<path to manifest file>" \
         model.validation_ds.manifest_filepath="<path to manifest file>" \
+        model.tokenizer.update_tokenizer=<True/False> \ # True to update tokenizer, False to retain existing tokenizer
+        model.tokenizer.dir=<path to tokenizer dir> \ # Path to tokenizer dir when update_tokenizer=True
+        model.tokenizer.type=<tokenizer type> \ # tokenizer type when update_tokenizer=True
         trainer.devices=-1 \
         trainer.accelerator='gpu' \
         trainer.max_epochs=50 \
-        +init_from_nemo_model="<path to .nemo model file>"
+        +init_from_nemo_model="<path to .nemo model file>" (or +init_from_pretrained_model="<name of pretrained checkpoint>")
 
+Fine-tuning by changing model architecture and tokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Fine-tuning via a NeMo pretrained model name
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+If users want to update the model architecture as well they can use the following script:
 
-.. code-block:: sh
+For providing pretrained model, users can provide Pre-trained weights in multiple ways -
 
-    python examples/asr/<subtask>/script_to_<script_name>.py \
-        --config-path=<path to dir of configs> \
-        --config-name=<name of config without .yaml>) \
-        model.train_ds.manifest_filepath="<path to manifest file>" \
-        model.validation_ds.manifest_filepath="<path to manifest file>" \
-        trainer.devices=-1 \
-        trainer.accelerator='gpu' \
-        trainer.max_epochs=50 \
-        +init_from_pretrained_model="<name of pretrained checkpoint>"
+1) Providing a path to a NeMo model (via ``init_from_nemo_model``)
+2) Providing a name of a pretrained NeMo model (which will be downloaded via the cloud) (via ``init_from_pretrained_model``)
+3) Providing a path to a Pytorch Lightning checkpoint file (via ``init_from_ptl_ckpt``)
 
-Fine-tuning via a Pytorch Lightning checkpoint
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+There are multiple ASR subtasks inside the ``examples/asr/`` directory, you can substitute the ``<subtask>`` tag below.
 
 .. code-block:: sh
 
@@ -1082,7 +1088,16 @@ Fine-tuning via a Pytorch Lightning checkpoint
         trainer.devices=-1 \
         trainer.accelerator='gpu' \
         trainer.max_epochs=50 \
-        +init_from_ptl_ckpt="<name of pytorch lightning checkpoint>"
+        +init_from_nemo_model="<path to .nemo model file>" # (or +init_from_pretrained_model, +init_from_ptl_ckpt )
+
+To reinitialize part of the model, to make it different from the pretrained model, users can mention them through config:
+
+.. code-block:: yaml
+
+    init_from_nemo_model: "<path to .nemo model file>"
+        asr_model:
+            include: ["preprocessor","encoder"]
+            exclude: ["decoder"]
 
 Fine-tuning Execution Flow Diagram
 ----------------------------------

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

@@ -254,7 +254,6 @@ trainer:
   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

examples/asr/conf/speech_to_text_finetune.yaml

@@ -0,0 +1,118 @@
+name: "Speech_To_Text_Finetuning"
+
+# use `init_from_nemo_model` or `init_from_pretrained_model` to initialize the model
+# We do not currently support `init_from_ptl_ckpt` to create a single script for all types of models.
+init_from_nemo_model: null # path to nemo model
+
+model:
+  sample_rate: 16000
+  compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag.
+  log_prediction: true # enables logging sample predictions in the output during training
+  rnnt_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
+    max_duration: 20
+    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
+    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
+
+  char_labels: # use for char based models
+    update_labels: false
+    labels: null # example list config: \[' ', 'a', 'b', 'c'\]
+
+  tokenizer: # use for spe/bpe based tokenizer models
+    update_tokenizer: false
+    dir: null  # 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)
+
+  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
+
+  optim:
+    name: adamw
+    lr: 1e-4
+    # optimizer arguments
+    betas: [0.9, 0.98]
+    weight_decay: 1e-3
+
+    # scheduler setup
+    sched:
+      name: CosineAnnealing
+      # scheduler config override
+      warmup_steps: 5000
+      warmup_ratio: null
+      min_lr: 5e-6
+
+trainer:
+  devices: -1 # number of GPUs, -1 would use all available GPUs
+  num_nodes: 1
+  max_epochs: 50
+  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 # 16, 32, or bf16
+  log_every_n_steps: 10  # Interval of logging.
+  enable_progress_bar: True
+  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 along with PTL checkpoints
+  resume_if_exists: false
+  resume_ignore_no_checkpoint: false
+
+  create_wandb_logger: false
+  wandb_logger_kwargs:
+    name: null
+    project: null