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 @@ def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **
  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 @@ def inference_with_vad(self, input, input_len=None, **cfg):
 
  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 @@ def inference_with_vad(self, input, input_len=None, **cfg):
  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 @@ def inference_with_vad(self, input, input_len=None, **cfg):
  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 @@ def inference_with_vad(self, input, input_len=None, **cfg):
  # 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 @@ def inference_with_vad(self, input, input_len=None, **cfg):
 
  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