From 7759ab5febbccdeb929c34a5d5dd4b5387bfd83c Mon Sep 17 00:00:00 2001
From: Yuming Zhang <736099538@qq.com>
Date: 星期五, 23 二月 2024 18:28:26 +0800
Subject: [PATCH] fix bug: 模型初始化可传入参数disable_pbar=True (#1387)
---
funasr/auto/auto_model.py | 47 +++++++++++++++++++++++------------------------
1 files changed, 23 insertions(+), 24 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index e5faa2a..66c0750 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -219,7 +219,7 @@
speed_stats = {}
asr_result_list = []
num_samples = len(data_list)
- disable_pbar = kwargs.get("disable_pbar", False)
+ disable_pbar = self.kwargs.get("disable_pbar", False)
pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None
time_speech_total = 0.0
time_escape_total = 0.0
@@ -231,12 +231,12 @@
if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank
batch["data_in"] = data_batch[0]
batch["data_lengths"] = input_len
-
+
time1 = time.perf_counter()
with torch.no_grad():
results, meta_data = model.inference(**batch, **kwargs)
time2 = time.perf_counter()
-
+
asr_result_list.extend(results)
# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
@@ -261,31 +261,29 @@
pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
torch.cuda.empty_cache()
return asr_result_list
-
+
def inference_with_vad(self, input, input_len=None, **cfg):
-
+ kwargs = self.kwargs
# step.1: compute the vad model
self.vad_kwargs.update(cfg)
beg_vad = time.time()
res = self.inference(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
end_vad = time.time()
- print(f"time cost vad: {end_vad - beg_vad:0.3f}")
# step.2 compute asr model
model = self.model
- kwargs = self.kwargs
kwargs.update(cfg)
batch_size = int(kwargs.get("batch_size_s", 300))*1000
batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
kwargs["batch_size"] = batch_size
-
+
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 = 1e-6
beg_total = time.time()
- pbar_total = tqdm(colour="red", total=len(res), dynamic_ncols=True)
+ pbar_total = tqdm(colour="red", total=len(res), dynamic_ncols=True) if not kwargs.get("disable_pbar", False) else None
for i in range(len(res)):
key = res[i]["key"]
vadsegments = res[i]["value"]
@@ -296,14 +294,14 @@
data_with_index = [(vadsegments[i], i) for i in range(n)]
sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
results_sorted = []
-
+
if not len(sorted_data):
logging.info("decoding, utt: {}, empty speech".format(key))
continue
if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
-
+
batch_size_ms_cum = 0
beg_idx = 0
beg_asr_total = time.time()
@@ -322,8 +320,8 @@
continue
batch_size_ms_cum = 0
end_idx = j + 1
- speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
- results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, disable_pbar=True, **cfg)
+ speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
+ results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
if self.spk_model is not None:
# compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
for _b in range(len(speech_j)):
@@ -333,26 +331,26 @@
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, disable_pbar=True, **cfg)
+ spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **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}")
-
+
restored_data = [0] * n
for j in range(n):
index = sorted_data[j][1]
restored_data[index] = results_sorted[j]
result = {}
-
+
# results combine for texts, timestamps, speaker embeddings and others
# TODO: rewrite for clean code
for j in range(n):
@@ -379,18 +377,18 @@
result[k] = restored_data[j][k]
else:
result[k] += restored_data[j][k]
-
- return_raw_text = kwargs.get('return_raw_text', False)
+
+ return_raw_text = kwargs.get('return_raw_text', False)
# 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, disable_pbar=True, **cfg)
+ punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
raw_text = copy.copy(result["text"])
if return_raw_text: result['raw_text'] = raw_text
result["text"] = punc_res[0]["text"]
else:
raw_text = None
-
+
# speaker embedding cluster after resorted
if self.spk_model is not None and kwargs.get('return_spk_res', True):
if raw_text is None:
@@ -429,13 +427,14 @@
return_raw_text=return_raw_text)
result['sentence_info'] = sentence_list
if "spk_embedding" in result: del result['spk_embedding']
-
+
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.set_description(f"rtf_avg: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
+ if pbar_total:
+ 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}")
--
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