Awesome

nemo-experimentation

Training Steps

Prepare Data

cd scripts/data

Modify folder and name paths in create_manifest.sh.

folder : path to folder containing audio and text files data : will contain the json file required for training

bash create_manifest.sh

Similarly, you can create manifests for validation and test sets as well. These will be required to start the finetuning.

Tokenize Data

We tokenize the text using a unigram sentencepiece tokenizer with a vocab size of 128. In tokenize.sh modify the manifest and destination path to point to train manifest and save folder respectively.

bash tokenize.sh

Start finetuning

cd scripts/finetuning

Modify start_train_ctc.sh

config_path='../..//conf/finetuning/char'
config_name='conformer_ctc_bpe_large.yaml'
train_manifest_path= < path to train manifest > 
valid_manifest_path= < path to valid manifest > 
test_manifest_path= < path to test manifest >  
tokenizer_dir= < path to tokenizer directory > 
tokenizer_type=bpe
wandb_project_name= < project name in your wandb account >
wandb_run_name= < run name >
pretrained_model= < path to pretrained checkpoint >

bash start_train_ctc.sh

Inference

cd scripts/inference

Modify infer.sh

model_path= < finetuned model path >
dataset_manifest= < path to .json manifest >
output_filename= < file name to save results >

Infer with language model

First we need to train a language model to infer with it.

cd scripts/lm
bash install_beamsearch_decoders.sh 

Modify train_lm.sh

train_file='path to tokenizer tokenizer/text_corpus/document.txt'
model='< finetuned checkpoint >'
output='<output file path .bin'

bash train_lm.sh

Once the language model is trained go to cd scripts/inference. Modify infer_with_lm.sh

model_path="< finstuned model path > "
dataset_manifest='manifest .json file path'
kenlm_model='language model path .bin'
alpha=1.0 # asr model weight
beta=1.0. # lm weight
beam=128  # beam size

bash infer_with_lm.sh