mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-15 00:03:06 +00:00
Brandon Thomas
125 lines
4.2 KiB
Bash
125 lines
4.2 KiB
Bash
#!/bin/bash
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# default values for optional arguments
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MIN_SCORE=-5
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CUT_PREFIX=0
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SCRIPTS_DIR="scripts"
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OFFSET=0
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LANGUAGE='eng' # 'eng', 'ru', 'other'
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MIN_SEGMENT_LEN=20
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MAX_SEGMENT_LEN=100
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ADDITIONAL_SPLIT_SYMBOLS=''
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AUDIO_FORMAT='.mp3'
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USE_NEMO_NORMALIZATION='True'
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for ARG in "$@"
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do
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key=$(echo $ARG | cut -f1 -d=)
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value=$(echo $ARG | cut -f2 -d=)
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if [[ $key == *"--"* ]]; then
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v="${key/--/}"
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declare $v="${value}"
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fi
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done
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echo "MODEL_NAME_OR_PATH = $MODEL_NAME_OR_PATH"
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echo "DATA_DIR = $DATA_DIR"
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echo "OUTPUT_DIR = $OUTPUT_DIR"
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echo "MIN_SCORE = $MIN_SCORE"
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echo "CUT_PREFIX = $CUT_PREFIX"
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echo "SCRIPTS_DIR = $SCRIPTS_DIR"
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echo "OFFSET = $OFFSET"
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echo "LANGUAGE = $LANGUAGE"
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echo "MIN_SEGMENT_LEN = $MIN_SEGMENT_LEN"
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echo "MAX_SEGMENT_LEN = $MAX_SEGMENT_LEN"
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echo "ADDITIONAL_SPLIT_SYMBOLS = $ADDITIONAL_SPLIT_SYMBOLS"
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echo "AUDIO_FORMAT = $AUDIO_FORMAT"
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echo "USE_NEMO_NORMALIZATION = $USE_NEMO_NORMALIZATION"
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if [[ -z $MODEL_NAME_OR_PATH ]] || [[ -z $DATA_DIR ]] || [[ -z $OUTPUT_DIR ]]; then
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echo "Usage: $(basename "$0")
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--MODEL_NAME_OR_PATH=[model_name_or_path]
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--DATA_DIR=[data_dir]
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--OUTPUT_DIR=[output_dir]
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--LANGUAGE=[language (Optional)]
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--OFFSET=[offset value (Optional)]
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--CUT_PREFIX=[cut prefix in sec (Optional)]
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--SCRIPTS_DIR=[scripts_dir_path (Optional)]
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--MIN_SEGMENT_LEN=[min number of characters of the text segment for alignment (Optional)]
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--MAX_SEGMENT_LEN=[max number of characters of the text segment for alignment (Optional)]
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--ADDITIONAL_SPLIT_SYMBOLS=[Additional symbols to use for
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sentence split if eos sentence split resulted in sequence longer than --max_length.
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Use '|' as a separator between symbols, for example: ';|:|' (Optional)]
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--AUDIO_FORMAT=[choose from ['.mp3', '.wav'], input audio files format
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--USE_NEMO_NORMALIZATION Set to 'True' to use NeMo Normalization tool to convert
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numbers from written to spoken format. By default num2words package will be used. (Optional)"
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exit 1
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fi
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NEMO_NORMALIZATION=""
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if [[ ${USE_NEMO_NORMALIZATION,,} == "true" ]]; then
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NEMO_NORMALIZATION="--use_nemo_normalization "
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fi
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# STEP #1
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# Prepare text and audio data for segmentation
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python $SCRIPTS_DIR/prepare_data.py \
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--in_text=$DATA_DIR/text \
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--audio_dir=$DATA_DIR/audio \
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--audio_format=$AUDIO_FORMAT \
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--output_dir=$OUTPUT_DIR/processed/ \
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--language=$LANGUAGE \
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--cut_prefix=$CUT_PREFIX \
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--model=$MODEL_NAME_OR_PATH \
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--min_length=$MIN_SEGMENT_LEN \
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--max_length=$MAX_SEGMENT_LEN \
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--additional_split_symbols=$ADDITIONAL_SPLIT_SYMBOLS $NEMO_NORMALIZATION || exit
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# STEP #2
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# Run CTC-segmenatation
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# one might want to perform alignment with various window sizes
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# note if the alignment with the initial window size isn't found, the window size will be double to re-attempt
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# alignment
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for WINDOW in 8000 12000
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do
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python $SCRIPTS_DIR/run_ctc_segmentation.py \
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--output_dir=$OUTPUT_DIR \
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--data=$OUTPUT_DIR/processed/ \
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--model=$MODEL_NAME_OR_PATH \
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--window_len $WINDOW || exit
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done
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# STEP #3 (Optional)
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# Verify aligned segments only if multiple WINDOWs used in the Step #2)
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python $SCRIPTS_DIR/verify_segments.py \
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--base_dir=$OUTPUT_DIR || exit
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# STEP #4
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# Cut the original audio files based on the alignments
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# (use --alignment=$OUTPUT_DIR/segments if only 1 WINDOW size was used in the Step #2)
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# Three manifests and corresponding clips folders will be created:
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# - high scored clips
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# - low scored clips
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# - deleted segments
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python $SCRIPTS_DIR/cut_audio.py \
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--output_dir=$OUTPUT_DIR \
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--model=$MODEL_NAME_OR_PATH \
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--alignment=$OUTPUT_DIR/verified_segments \
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--threshold=$MIN_SCORE \
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--offset=$OFFSET || exit
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# STEP #5 (Optional)
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# If multiple audio files were segmented in the step #2, this step will aggregate manifests with high scored segments
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# for all audio files into all_manifest.json
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# Also a separate manifest with samples from across all high scored segments will be credated if --num_samples > 0
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# --num_samples samples will be taken from the beginning, end and the middle of the each audio file high score manifest
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# and will be stored at sample_manifest.json
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python $SCRIPTS_DIR/process_manifests.py \
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--output_dir=$OUTPUT_DIR \
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--manifests_dir=$OUTPUT_DIR/manifests/ \
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--num_samples 0
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exit
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