cer tool

游雁

2023-03-22 efc829e893c41ac5bd596752e7efc05a52efc8e8

cer tool

 funasr/runtime/python/utils/compute_wer.py

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import os
import numpy as np
import sys

def compute_wer(ref_file,
                hyp_file,
                cer_detail_file):
    rst = {
        'Wrd': 0,
        'Corr': 0,
        'Ins': 0,
        'Del': 0,
        'Sub': 0,
        'Snt': 0,
        'Err': 0.0,
        'S.Err': 0.0,
        'wrong_words': 0,
        'wrong_sentences': 0
    }

    hyp_dict = {}
    ref_dict = {}
    with open(hyp_file, 'r') as hyp_reader:
        for line in hyp_reader:
            key = line.strip().split()[0]
            value = line.strip().split()[1:]
            hyp_dict[key] = value
    with open(ref_file, 'r') as ref_reader:
        for line in ref_reader:
            key = line.strip().split()[0]
            value = line.strip().split()[1:]
            ref_dict[key] = value

    cer_detail_writer = open(cer_detail_file, 'w')
    for hyp_key in hyp_dict:
        if hyp_key in ref_dict:
           out_item = compute_wer_by_line(hyp_dict[hyp_key], ref_dict[hyp_key])
           rst['Wrd'] += out_item['nwords']
           rst['Corr'] += out_item['cor']
           rst['wrong_words'] += out_item['wrong']
           rst['Ins'] += out_item['ins']
           rst['Del'] += out_item['del']
           rst['Sub'] += out_item['sub']
           rst['Snt'] += 1
           if out_item['wrong'] > 0:
               rst['wrong_sentences'] += 1
           cer_detail_writer.write(hyp_key + print_cer_detail(out_item) + '\n')
           cer_detail_writer.write("ref:" + '\t' + "".join(ref_dict[hyp_key]) + '\n')
           cer_detail_writer.write("hyp:" + '\t' + "".join(hyp_dict[hyp_key]) + '\n')

    if rst['Wrd'] > 0:
        rst['Err'] = round(rst['wrong_words'] * 100 / rst['Wrd'], 2)
    if rst['Snt'] > 0:
        rst['S.Err'] = round(rst['wrong_sentences'] * 100 / rst['Snt'], 2)

    cer_detail_writer.write('\n')
    cer_detail_writer.write("%WER " + str(rst['Err']) + " [ " + str(rst['wrong_words'])+ " / " + str(rst['Wrd']) +
                            ", " + str(rst['Ins']) + " ins, " + str(rst['Del']) + " del, " + str(rst['Sub']) + " sub ]" + '\n')
    cer_detail_writer.write("%SER " + str(rst['S.Err']) + " [ " + str(rst['wrong_sentences']) + " / " + str(rst['Snt']) + " ]" + '\n')
    cer_detail_writer.write("Scored " + str(len(hyp_dict)) + " sentences, " + str(len(hyp_dict) - rst['Snt']) + " not present in hyp." + '\n')

     
def compute_wer_by_line(hyp,
                        ref):
    hyp = list(map(lambda x: x.lower(), hyp))
    ref = list(map(lambda x: x.lower(), ref))

    len_hyp = len(hyp)
    len_ref = len(ref)

    cost_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int16)

    ops_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int8)

    for i in range(len_hyp + 1):
        cost_matrix[i][0] = i
    for j in range(len_ref + 1):
        cost_matrix[0][j] = j

    for i in range(1, len_hyp + 1):
        for j in range(1, len_ref + 1):
            if hyp[i - 1] == ref[j - 1]:
                cost_matrix[i][j] = cost_matrix[i - 1][j - 1]
            else:
                substitution = cost_matrix[i - 1][j - 1] + 1
                insertion = cost_matrix[i - 1][j] + 1
                deletion = cost_matrix[i][j - 1] + 1

                compare_val = [substitution, insertion, deletion]

                min_val = min(compare_val)
                operation_idx = compare_val.index(min_val) + 1
                cost_matrix[i][j] = min_val
                ops_matrix[i][j] = operation_idx

    match_idx = []
    i = len_hyp
    j = len_ref
    rst = {
        'nwords': len_ref,
        'cor': 0,
        'wrong': 0,
        'ins': 0,
        'del': 0,
        'sub': 0
    }
    while i >= 0 or j >= 0:
        i_idx = max(0, i)
        j_idx = max(0, j)

        if ops_matrix[i_idx][j_idx] == 0:  # correct
            if i - 1 >= 0 and j - 1 >= 0:
                match_idx.append((j - 1, i - 1))
                rst['cor'] += 1

            i -= 1
            j -= 1

        elif ops_matrix[i_idx][j_idx] == 2:  # insert
            i -= 1
            rst['ins'] += 1

        elif ops_matrix[i_idx][j_idx] == 3:  # delete
            j -= 1
            rst['del'] += 1

        elif ops_matrix[i_idx][j_idx] == 1:  # substitute
            i -= 1
            j -= 1
            rst['sub'] += 1

        if i < 0 and j >= 0:
            rst['del'] += 1
        elif j < 0 and i >= 0:
            rst['ins'] += 1

    match_idx.reverse()
    wrong_cnt = cost_matrix[len_hyp][len_ref]
    rst['wrong'] = wrong_cnt

    return rst

def print_cer_detail(rst):
    return ("(" + "nwords=" + str(rst['nwords']) + ",cor=" + str(rst['cor'])
            + ",ins=" + str(rst['ins']) + ",del=" + str(rst['del']) + ",sub="
            + str(rst['sub']) + ") corr:" + '{:.2%}'.format(rst['cor']/rst['nwords'])
            + ",cer:" + '{:.2%}'.format(rst['wrong']/rst['nwords']))

if __name__ == '__main__':
    if len(sys.argv) != 4:
        print("usage : python compute-wer.py test.ref test.hyp test.wer")
        sys.exit(0)

    ref_file = sys.argv[1]
    hyp_file = sys.argv[2]
    cer_detail_file = sys.argv[3]
    compute_wer(ref_file, hyp_file, cer_detail_file)

 funasr/runtime/python/utils/infer.py

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import os
import time
import sys
import librosa
from funasr.utils.types import str2bool

import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--model_dir', type=str, required=True)
parser.add_argument('--backend', type=str, default='onnx', help='["onnx", "torch"]')
parser.add_argument('--wav_file', type=str, default=None, help='amp fallback number')
parser.add_argument('--quantize', type=str2bool, default=False, help='quantized model')
parser.add_argument('--intra_op_num_threads', type=int, default=1, help='intra_op_num_threads for onnx')
parser.add_argument('--output_dir', type=str, default=None, help='amp fallback number')
args = parser.parse_args()


from funasr.runtime.python.libtorch.torch_paraformer import Paraformer
if args.backend == "onnx":
    from funasr.runtime.python.onnxruntime.rapid_paraformer import Paraformer
	
model = Paraformer(args.model_dir, batch_size=1, quantize=args.quantize, intra_op_num_threads=args.intra_op_num_threads)

wav_file_f = open(args.wav_file, 'r')
wav_files = wav_file_f.readlines()

output_dir = args.output_dir
if not os.path.exists(output_dir):
    os.makedirs(output_dir)
if os.name == 'nt':   # Windows
    newline = '\r\n'
else:   # Linux Mac
    newline = '\n'
text_f = open(os.path.join(output_dir, "text"), "w", newline=newline)
token_f = open(os.path.join(output_dir, "token"), "w", newline=newline)

for i, wav_path_i in enumerate(wav_files):
    wav_name, wav_path = wav_path_i.strip().split()
    result = model(wav_path)
    text_i = "{} {}\n".format(wav_name, result[0])
    token_i = "{} {}\n".format(wav_name, result[1])
    text_f.write(text_i)
    text_f.flush()
    token_f.write(token_i)
    token_f.flush()
text_f.close()
token_f.close()
	

 funasr/runtime/python/utils/infer.sh

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nj=32
stage=0
stop_stage=2

scp="/nfs/haoneng.lhn/funasr_data/aishell-1/data/test/wav.scp"
label_text="/nfs/haoneng.lhn/funasr_data/aishell-1/data/test/text"
export_root="/nfs/zhifu.gzf/export"
split_scps_tool=split_scp.pl
inference_tool=infer.py
proce_text_tool=proce_text.py
compute_wer_tool=compute_wer.py

#:<<!
model_name="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
backend="onnx" # "torch"
quantize='true' # 'False'
tag=${model_name}/${backend}_quantize_${quantize}
!

output_dir=${export_root}/logs/${tag}/split$nj
mkdir -p ${output_dir}
echo ${output_dir}


if [ $stage -le 0 ] && [ $stop_stage -ge 0 ];then

    python -m funasr.export.export_model --model-name ${model_name} --export-dir ${export_root} --type ${backend} --quantize ${quantize} --audio_in ${scp}

fi


if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then

  model_dir=${export_root}/${model_name}
  split_scps=""
  for JOB in $(seq ${nj}); do
      split_scps="$split_scps $output_dir/wav.$JOB.scp"
  done

  perl ${split_scps_tool} $scp ${split_scps}


  for JOB in $(seq ${nj}); do
    {
      core_id=`expr $JOB - 1`
      taskset -c ${core_id} python ${rtf_tool} --backend ${backend} --model_dir ${model_dir} --wav_file ${output_dir}/wav.$JOB.scp --quantize ${quantize} --output_dir ${output_dir}/${JOB} &> ${output_dir}/log.$JOB.txt
    }&

  done
  wait

  mkdir -p ${output_dir}/1best_recog
  for f in token text; do
      if [ -f "${output_dir}/1/${f}" ]; then
        for JOB in $(seq "${nj}"); do
            cat "${output_dir}/${JOB}/1best_recog/${f}"
        done | sort -k1 >"${output_dir}/1best_recog/${f}"
      fi
  done

fi

if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
    echo "Computing WER ..."
    python ${proce_text_tool} ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc
    python ${proce_text_tool} ${label_text} ${output_dir}/1best_recog/text.ref
    python ${compute_wer_tool} ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer
    tail -n 3 ${output_dir}/1best_recog/text.cer
fi


 funasr/runtime/python/utils/proce_text.py

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import sys
import re

in_f = sys.argv[1]
out_f = sys.argv[2]


with open(in_f, "r", encoding="utf-8") as f:
  lines = f.readlines()

with open(out_f, "w", encoding="utf-8") as f:
  for line in lines:
    outs = line.strip().split(" ", 1)
    if len(outs) == 2:
      idx, text = outs
      text = re.sub("</s>", "", text)
      text = re.sub("<s>", "", text)
      text = re.sub("@@", "", text)
      text = re.sub("@", "", text)
      text = re.sub("<unk>", "", text)
      text = re.sub(" ", "", text)
      text = text.lower()
    else:
      idx = outs[0]
      text = " "

    text = [x for x in text]
    text = " ".join(text)
    out = "{} {}\n".format(idx, text)
    f.write(out)