From 4091bf66c509224c8f8c1ed28f9411ce48e34ab4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 25 一月 2024 20:07:56 +0800
Subject: [PATCH] bugfix time_speech_total_per_sample=0
---
funasr/auto/auto_model.py | 43 ++++++++++++++++++++++++-------------------
examples/industrial_data_pretraining/paraformer/infer_after_finetune.sh | 10 +++++-----
2 files changed, 29 insertions(+), 24 deletions(-)
diff --git a/examples/industrial_data_pretraining/paraformer/infer_after_finetune.sh b/examples/industrial_data_pretraining/paraformer/infer_after_finetune.sh
index df1e54a..94797bf 100644
--- a/examples/industrial_data_pretraining/paraformer/infer_after_finetune.sh
+++ b/examples/industrial_data_pretraining/paraformer/infer_after_finetune.sh
@@ -4,9 +4,9 @@
--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" \
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 4d0f302..9d957a3 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -260,7 +260,7 @@
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 @@
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 @@
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 @@
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 @@
# 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 @@
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
--
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