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bugfix time_speech_total_per_sample=0

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游雁 2024-01-25 20:07:56 +08:00
parent fa56f36921
commit 4091bf66c5
2 changed files with 29 additions and 24 deletions
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10
examples/industrial_data_pretraining/paraformer/infer_after_finetune.sh
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@ -4,9 +4,9 @@ python funasr/bin/inference.py \
--config-path="/Users/zhifu/funasr_github/test_local/funasr_cli_egs" \ --config-path="/Users/zhifu/funasr_github/test_local/funasr_cli_egs" \
--config-name="config.yaml" \ --config-name="config.yaml" \
++init_param="/Users/zhifu/funasr_github/test_local/funasr_cli_egs/model.pt" \ ++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" \ ++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" \ ++frontend_conf.cmvn_file="/Users/zhifu/funasr_github/test_local/funasr_cli_egs/am.mvn" \
+input="data/wav.scp" \ ++input="data/wav.scp" \
+output_dir="./outputs/debug" \ ++output_dir="./outputs/debug" \
+device="cuda" \ ++device="cuda" \

43
funasr/auto/auto_model.py
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@ -260,7 +260,7 @@ class AutoModel:
time_escape_total += time_escape time_escape_total += time_escape
if pbar: if pbar:
pbar.update(1) # pbar.update(1)
pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}") pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
torch.cuda.empty_cache() torch.cuda.empty_cache()
return asr_result_list 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)) key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
results_ret_list = [] results_ret_list = []
time_speech_total_all_samples = 0.0 time_speech_total_all_samples = 1e-6
beg_total = time.time() 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)): for i in range(len(res)):
key = res[i]["key"] key = res[i]["key"]
vadsegments = res[i]["value"] vadsegments = res[i]["value"]
@ -310,14 +310,14 @@ class AutoModel:
batch_size_ms_cum = 0 batch_size_ms_cum = 0
beg_idx = 0 beg_idx = 0
beg_asr_total = time.time() 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 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 = [] all_segments = []
for j, _ in enumerate(range(0, n)): 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]) batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
if j < n - 1 and ( if j < n - 1 and (
batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size 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) segments = sv_chunk(vad_segments)
all_segments.extend(segments) all_segments.extend(segments)
speech_b = [i[2] for i in 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'] results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
beg_idx = end_idx beg_idx = end_idx
if len(results) < 1: if len(results) < 1:
continue continue
results_sorted.extend(results) results_sorted.extend(results)
end_asr_total = time.time() # end_asr_total = time.time()
time_escape_total_per_sample = end_asr_total - beg_asr_total # time_escape_total_per_sample = end_asr_total - beg_asr_total
pbar_sample.update(1) # 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}, " # 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_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}") # f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
restored_data = [0] * n restored_data = [0] * n
for j in range(n): for j in range(n):
@ -386,7 +386,7 @@ class AutoModel:
# step.3 compute punc model # step.3 compute punc model
if self.punc_model is not None: if self.punc_model is not None:
self.punc_kwargs.update(cfg) 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"]) import copy; raw_text = copy.copy(result["text"])
result["text"] = punc_res[0]["text"] result["text"] = punc_res[0]["text"]
@ -418,13 +418,18 @@ class AutoModel:
result["key"] = key result["key"] = key
results_ret_list.append(result) 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}, "
pbar_total.update(1) f"time_speech: {time_speech_total_per_sample: 0.3f}, "
f"time_escape: {time_escape_total_per_sample:0.3f}")
end_total = time.time() end_total = time.time()
time_escape_total_all_samples = end_total - beg_total 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}, " print(f"rtf_avg_all: {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_speech_all: {time_speech_total_all_samples: 0.3f}, "
f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}") f"time_escape_all: {time_escape_total_all_samples:0.3f}")
return results_ret_list return results_ret_list