From b5ea9c7a6a7cf0816fd59d7b3377752390d3a775 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 29 二月 2024 14:33:10 +0800
Subject: [PATCH] cer
---
funasr/models/llm_asr_nar/model.py | 3 ++-
funasr/metrics/wer.py | 42 ++++++++++++++++++++++++++++++++----------
2 files changed, 34 insertions(+), 11 deletions(-)
diff --git a/funasr/metrics/compute_wer.py b/funasr/metrics/wer.py
similarity index 81%
rename from funasr/metrics/compute_wer.py
rename to funasr/metrics/wer.py
index 26a9f49..77abbbd 100755
--- a/funasr/metrics/compute_wer.py
+++ b/funasr/metrics/wer.py
@@ -1,10 +1,13 @@
import os
import numpy as np
import sys
+import hydra
def compute_wer(ref_file,
hyp_file,
- cer_detail_file):
+ cer_file,
+ cn_postprocess=False,
+ ):
rst = {
'Wrd': 0,
'Corr': 0,
@@ -24,14 +27,22 @@
for line in hyp_reader:
key = line.strip().split()[0]
value = line.strip().split()[1:]
+ if cn_postprocess:
+ value = value.replace(" ", "")
+ value = [x for x in value]
+ value = " ".join(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 = value.replace(" ", "")
+ value = [x for x in value]
+ value = " ".join(value)
ref_dict[key] = value
- cer_detail_writer = open(cer_detail_file, 'w')
+ 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])
@@ -47,6 +58,7 @@
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)
@@ -59,6 +71,7 @@
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):
@@ -146,12 +159,21 @@
+ 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)
+@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..f170349 100644
--- a/funasr/models/llm_asr_nar/model.py
+++ b/funasr/models/llm_asr_nar/model.py
@@ -315,7 +315,8 @@
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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