Merge pull request #83 from alibaba-damo-academy/dev_lzr

remove useless vars and fix bug in predictor tail_process_fn

funasr/bin/asr_inference.py

@@ -464,16 +464,6 @@ def inference_modelscope(
     
     return _forward
 
-def set_parameters(language: str = None,
-                   sample_rate: Union[int, Dict[Any, int]] = None):
-    if language is not None:
-        global global_asr_language
-        global_asr_language = language
-    if sample_rate is not None:
-        global global_sample_rate
-        global_sample_rate = sample_rate
-
-
 def get_parser():
     parser = config_argparse.ArgumentParser(
         description="ASR Decoding",

funasr/models/predictor/cif.py

@@ -68,7 +68,8 @@ class CifPredictor(nn.Module):
             mask_2 = torch.cat([ones_t, mask], dim=1)
             mask = mask_2 - mask_1
             tail_threshold = mask * tail_threshold
-            alphas = torch.cat([alphas, tail_threshold], dim=1)
+            alphas = torch.cat([alphas, zeros_t], dim=1)
+            alphas = torch.add(alphas, tail_threshold)
         else:
             tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
             tail_threshold = torch.reshape(tail_threshold, (1, 1))
@@ -597,7 +598,8 @@ class CifPredictorV3(nn.Module):
             mask_2 = torch.cat([ones_t, mask], dim=1)
             mask = mask_2 - mask_1
             tail_threshold = mask * tail_threshold
-            alphas = torch.cat([alphas, tail_threshold], dim=1)
+            alphas = torch.cat([alphas, zeros_t], dim=1)
+            alphas = torch.add(alphas, tail_threshold)
         else:
             tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
             tail_threshold = torch.reshape(tail_threshold, (1, 1))

funasr/utils/asr_utils.py

@@ -186,23 +186,12 @@ def recursion_dir_all_wav(wav_list, dir_path: str) -> List[str]:
 
     return wav_list
 
-
-def set_parameters(language: str = None):
-    if language is not None:
-        global global_asr_language
-        global_asr_language = language
-
-
 def compute_wer(hyp_list: List[Any],
                 ref_list: List[Any],
                 lang: str = None) -> Dict[str, Any]:
     assert len(hyp_list) > 0, 'hyp list is empty'
     assert len(ref_list) > 0, 'ref list is empty'
 
-    if lang is not None:
-        global global_asr_language
-        global_asr_language = lang
-
     rst = {
         'Wrd': 0,
         'Corr': 0,
@@ -216,12 +205,15 @@ def compute_wer(hyp_list: List[Any],
         'wrong_sentences': 0
     }
 
+    if lang is None:
+        lang = global_asr_language
+
     for h_item in hyp_list:
         for r_item in ref_list:
             if h_item['key'] == r_item['key']:
                 out_item = compute_wer_by_line(h_item['value'],
                                                r_item['value'],
-                                               global_asr_language)
+                                               lang)
                 rst['Wrd'] += out_item['nwords']
                 rst['Corr'] += out_item['cor']
                 rst['wrong_words'] += out_item['wrong']