bugfix time_speech_total_per_sample=0

examples/industrial_data_pretraining/paraformer/infer_after_finetune.sh

@@ -4,9 +4,9 @@ python funasr/bin/inference.py \
 --config-path="/Users/zhifu/funasr_github/test_local/funasr_cli_egs" \
 --config-name="config.yaml" \
 ++init_param="/Users/zhifu/funasr_github/test_local/funasr_cli_egs/model.pt" \
-+tokenizer_conf.token_list="/Users/zhifu/funasr_github/test_local/funasr_cli_egs/tokens.txt" \
-+frontend_conf.cmvn_file="/Users/zhifu/funasr_github/test_local/funasr_cli_egs/am.mvn" \
-+input="data/wav.scp" \
-+output_dir="./outputs/debug" \
-+device="cuda" \
+++tokenizer_conf.token_list="/Users/zhifu/funasr_github/test_local/funasr_cli_egs/tokens.txt" \
+++frontend_conf.cmvn_file="/Users/zhifu/funasr_github/test_local/funasr_cli_egs/am.mvn" \
+++input="data/wav.scp" \
+++output_dir="./outputs/debug" \
+++device="cuda" \
 

funasr/auto/auto_model.py

@@ -260,7 +260,7 @@ class AutoModel:
             time_escape_total += time_escape
 
         if pbar:
-            pbar.update(1)
+            # pbar.update(1)
             pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
         torch.cuda.empty_cache()
         return asr_result_list
@@ -285,10 +285,10 @@ class AutoModel:
         
         key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
         results_ret_list = []
-        time_speech_total_all_samples = 0.0
+        time_speech_total_all_samples = 1e-6
 
         beg_total = time.time()
-        pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
+        pbar_total = tqdm(colour="red", total=len(res), dynamic_ncols=True)
         for i in range(len(res)):
             key = res[i]["key"]
             vadsegments = res[i]["value"]
@@ -310,14 +310,14 @@ class AutoModel:
             batch_size_ms_cum = 0
             beg_idx = 0
             beg_asr_total = time.time()
-            time_speech_total_per_sample = speech_lengths/16000 + 1e-6
+            time_speech_total_per_sample = speech_lengths/16000
             time_speech_total_all_samples += time_speech_total_per_sample
 
-            pbar_sample = tqdm(colour="blue", total=n + 1, dynamic_ncols=True)
+            # pbar_sample = tqdm(colour="blue", total=n, dynamic_ncols=True)
 
             all_segments = []
             for j, _ in enumerate(range(0, n)):
-                pbar_sample.update(1)
+                # pbar_sample.update(1)
                 batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
                 if j < n - 1 and (
                     batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
@@ -336,19 +336,19 @@ class AutoModel:
                         segments = sv_chunk(vad_segments)
                         all_segments.extend(segments)
                         speech_b = [i[2] for i in segments]
-                        spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
+                        spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, disable_pbar=True, **cfg)
                         results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
                 beg_idx = end_idx
                 if len(results) < 1:
                     continue
                 results_sorted.extend(results)
             
-            end_asr_total = time.time()
-            time_escape_total_per_sample = end_asr_total - beg_asr_total
-            pbar_sample.update(1)
-            pbar_sample.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
-                                 f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
-                                 f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
+            # end_asr_total = time.time()
+            # time_escape_total_per_sample = end_asr_total - beg_asr_total
+            # pbar_sample.update(1)
+            # pbar_sample.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
+            #                      f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
+            #                      f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
             
             restored_data = [0] * n
             for j in range(n):
@@ -386,7 +386,7 @@ class AutoModel:
             # step.3 compute punc model
             if self.punc_model is not None:
                 self.punc_kwargs.update(cfg)
-                punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
+                punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, disable_pbar=True, **cfg)
                 import copy; raw_text = copy.copy(result["text"])
                 result["text"] = punc_res[0]["text"]
                 
@@ -418,13 +418,18 @@ class AutoModel:
                     
             result["key"] = key
             results_ret_list.append(result)
+            end_asr_total = time.time()
+            time_escape_total_per_sample = end_asr_total - beg_asr_total
             pbar_total.update(1)
-            
-        pbar_total.update(1)
+            pbar_total.set_description(f"rtf_avg: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
+                                 f"time_speech: {time_speech_total_per_sample: 0.3f}, "
+                                 f"time_escape: {time_escape_total_per_sample:0.3f}")
+
+
         end_total = time.time()
         time_escape_total_all_samples = end_total - beg_total
-        pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
-                             f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
-                             f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
+        print(f"rtf_avg_all: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
+                             f"time_speech_all: {time_speech_total_all_samples: 0.3f}, "
+                             f"time_escape_all: {time_escape_total_all_samples:0.3f}")
         return results_ret_list