### Prepare training data manifest ``` $ python l2_label.py /path/to/waves --dest /manifest/path ``` ### Fine-tune a pre-trained model Edit the run.sh ```bash #!/usr/python/bin/ export CUDA_VISIBLE_DEVICES=1 # GPU device ID DATASET=/manifest/path FAIRSEQ_PATH=/path/to/fairseq valid_subset=valid model_path=/path/to/pretrain_model.pt # do not use finetuned model config_dir=/path/to/config config_name=base_finetune # made by reffering https://github.com/pytorch/fairseq/blob/master/examples/wav2vec/config/finetuning/base_10m.yaml labels=phn python3 $FAIRSEQ_PATH/fairseq_cli/hydra_train.py \ distributed_training.distributed_port=0 \ task.labels=$labels \ task.data=$DATASET \ dataset.valid_subset=$valid_subset \ distributed_training.distributed_world_size=1 \ model.w2v_path=$model_path \ --config-dir $config_dir \ --config-name $config_name ``` and ```bash $ sh run.sh ``` ### Evaluating a CTC model clone the respository to local ``` git clone https://github.com/cageyoko/CTC-Attention-Mispronunciation ``` Edit the evaluate.sh ```bash #!/usr/python/bin/ # Evaluating the CTC model export CUDA_VISIBLE_DEVICES=0 DATASET=/manifest/path FAIRSEQ_PATH=/path/to/fairseq python3 $FAIRSEQ_PATH/examples/speech_recognition/infer.py $DATASET --task audio_pretraining \ --nbest 1 --path /path/to/checkpoints/checkpoint_best.pt --gen-subset test --results-path $DATASET --w2l-decoder viterbi \ --lm-weight 0 --word-score -1 --sil-weight 0 --criterion ctc --labels phn --max-tokens 640000 # Env export KALDI_ROOT=/path/to/kaldi [ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$KALDI_ROOT/tools/irstlm/bin/:$PWD:$PATH [ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1 . $KALDI_ROOT/tools/config/common_path.sh export LC_ALL=C # calculate the result of MDD python3 result.py align-text ark:ref.txt ark:annotation.txt ark,t:- | wer_per_utt_details.pl > ref_human_detail align-text ark:annotation.txt ark:hypo.txt ark,t:- | wer_per_utt_details.pl > human_our_detail align-text ark:ref.txt ark:hypo.txt ark,t:- | wer_per_utt_details.pl > ref_our_detail python3 ins_del_sub_cor_analysis.py rm ref_human_detail human_our_detail ref_our_detail ``` and ```bash $ sh evaluate.sh >> result ``` # What's more we are going to make wav2vec2-based model to provide diagnose information in near future, Please stay tuned.