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游雁 2024-02-29 14:33:10 +08:00
parent d0f3532052
commit b5ea9c7a6a
2 changed files with 34 additions and 11 deletions
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42
funasr/metrics/compute_wer.py → funasr/metrics/wer.py
View File

@ -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 @@ def compute_wer(ref_file,
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 @@ def compute_wer(ref_file,
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 @@ def compute_wer(ref_file,
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 @@ def print_cer_detail(rst):
+ 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()

3
funasr/models/llm_asr_nar/model.py
View File

@ -315,7 +315,8 @@ class LLMASRNAR(nn.Module):
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