compute wer

scripts/compute_wer.sh

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+#cd /root/SLAM-LLM
+
+trans="/Users/zhifu/Downloads/decode_log_test_clean_gt.txt"
+preds="/Users/zhifu/Downloads/decode_log_test_clean_pred.txt"
+
+python src/llama_recipes/utils/preprocess_text.py ${preds} ${preds}.proc
+python src/llama_recipes/utils/num2word.py ${preds}.proc ${preds}.proc.words
+
+python src/llama_recipes/utils/compute_wer.py ${trans} ${preds}.proc ${preds}.proc.wer
+
+tail -3 ${preds}.proc.wer
+
+echo "-------num2word------"
+
+python src/llama_recipes/utils/compute_wer.py ${trans} ${preds}.proc.words ${preds}.proc.wer.words
+
+tail -3 ${preds}.proc.wer.words

src/llama_recipes/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(list(map(lambda x: x.lower(), ref_dict[hyp_key]))) + '\n')
+           cer_detail_writer.write("hyp:" + '\t' + " ".join(list(map(lambda x: x.lower(), 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)

src/llama_recipes/utils/num2word.py

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+
+import sys
+from num2words import num2words
+
+file = sys.argv[1]
+out_file = sys.argv[2]
+
+with open(file) as f:
+    lines = f.readlines()
+
+with open(out_file, "w") as fw:
+    for line in lines:
+        key, content = line.strip().split(maxsplit=1)
+        new_content = ""
+        for ct in content.split():
+            if ct.isdigit():
+                ct = num2words(ct)
+            new_content += ct + " "
+        fw.write(key + " " + new_content + "\n")

src/llama_recipes/utils/preprocess_text.py

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+
+import sys
+import re
+import string
+
+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("\t", 1)
+    if len(outs) == 2:
+      idx, text = outs
+      text = re.sub("<|", "", text)
+      text = re.sub("|>", "", text)
+      text = re.sub("—", "", 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()
+      translator = str.maketrans('', '', string.punctuation.replace("'", ""))
+      result = text.translate(translator)
+      text = result.upper()
+    else:
+      idx = outs[0]
+      text = " "
+
+    # text = [x for x in text]
+    # text = " ".join(text)
+    out = "{} {}\n".format(idx, text)
+    f.write(out)