PaddleSpeech/examples/callcenter/s1/local/data.sh

#! /usr/bin/env bash

stage=-1
stop_stage=100

source ${MAIN_ROOT}/utils/parse_options.sh

mkdir -p data

if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
    for dataset in train dev test; do
        mv data/manifest.${dataset} data/manifest.${dataset}.raw
    done
fi

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
    # compute mean and stddev for normalizer
    num_workers=$(nproc)
    python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
    --manifest_path="data/manifest.train.raw" \
    --spectrum_type="fbank" \
    --feat_dim=80 \
    --delta_delta=false \
    --stride_ms=10.0 \
    --window_ms=25.0 \
    --sample_rate=8000 \
    --use_dB_normalization=False \
    --num_samples=-1 \
    --num_workers=${num_workers} \
    --output_path="data/mean_std.json"

    if [ $? -ne 0 ]; then
        echo "Compute mean and stddev failed. Terminated."
        exit 1
    fi
fi

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
    # download data, generate manifests
    # build vocabulary
    python3 ${MAIN_ROOT}/utils/build_vocab.py \
    --unit_type="char" \
    --count_threshold=0 \
    --vocab_path="data/vocab.txt" \
    --manifest_paths "data/manifest.train.raw"

    if [ $? -ne 0 ]; then
        echo "Build vocabulary failed. Terminated."
        exit 1
    fi
fi

if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
    # format manifest with tokenids, vocab size
    for dataset in train dev test; do
    {
        python3 ${MAIN_ROOT}/utils/format_data.py \
            --feat_type "raw" \
            --cmvn_path "data/mean_std.json" \
            --unit_type "char" \
            --vocab_path="data/vocab.txt" \
            --manifest_path="data/manifest.${dataset}.raw" \
            --output_path="data/manifest.${dataset}"

        if [ $? -ne 0 ]; then
            echo "Formt mnaifest failed. Terminated."
            exit 1
        fi
    } &
    done
    wait
fi

echo "data preparation done."
exit 0