From c456abaf33023038be686f18df6a1178367d3894 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 29 二月 2024 16:23:52 +0800
Subject: [PATCH] Dev gzf (#1405)
---
/dev/null | 157 --------------------------
funasr/models/llm_asr_nar/model.py | 5
funasr/metrics/wer.py | 190 +++++++++++++++++++++++++++++++
3 files changed, 194 insertions(+), 158 deletions(-)
diff --git a/funasr/metrics/compute_wer.py b/funasr/metrics/compute_wer.py
deleted file mode 100755
index 26a9f49..0000000
--- a/funasr/metrics/compute_wer.py
+++ /dev/null
@@ -1,157 +0,0 @@
-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)
diff --git a/funasr/metrics/wer.py b/funasr/metrics/wer.py
new file mode 100755
index 0000000..b58daab
--- /dev/null
+++ b/funasr/metrics/wer.py
@@ -0,0 +1,190 @@
+import os
+import numpy as np
+import sys
+import hydra
+from omegaconf import DictConfig, OmegaConf, ListConfig
+
+
+def compute_wer(ref_file,
+ hyp_file,
+ cer_file,
+ cn_postprocess=False,
+ ):
+ 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:]
+ if cn_postprocess:
+ value = " ".join(value)
+ value = value.replace(" ", "")
+ if value[0] == "璇�":
+ value = value[1:]
+ value = [x for x in value]
+ 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:]
+ if cn_postprocess:
+ value = " ".join(value)
+ value = value.replace(" ", "")
+ value = [x for x in value]
+ ref_dict[key] = value
+
+ cer_detail_writer = open(cer_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')
+ cer_detail_writer.flush()
+
+ 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')
+
+ cer_detail_writer.close()
+
+
+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']))
+
+
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(cfg: DictConfig):
+ ref_file = cfg.get("ref_file", None)
+ hyp_file = cfg.get("hyp_file", None)
+ cer_file = cfg.get("cer_file", None)
+ cn_postprocess = cfg.get("cn_postprocess", False)
+ if ref_file is None or hyp_file is None or cer_file is None:
+ print(
+ "usage : python -m funasr.metrics.wer ++ref_file=test.ref ++hyp_file=test.hyp ++cer_file=test.wer ++cn_postprocess=false")
+ sys.exit(0)
+
+ compute_wer(ref_file, hyp_file, cer_file, cn_postprocess)
+
+
+if __name__ == '__main__':
+ main_hydra()
+
+
+
diff --git a/funasr/models/llm_asr_nar/model.py b/funasr/models/llm_asr_nar/model.py
index 6a4ecce..db81c47 100644
--- a/funasr/models/llm_asr_nar/model.py
+++ b/funasr/models/llm_asr_nar/model.py
@@ -315,7 +315,10 @@
model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=None)
preds = torch.argmax(model_outputs.logits, -1)
text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
- text = text[0].split(': \n')[-1]
+
+ text = text[0].split(': ')[-1]
+ text = text.strip()
+
# preds = torch.argmax(model_outputs.logits, -1)
ibest_writer = None
--
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