From c2f174cd7811a7a11e6227ecb371887f97fd66d3 Mon Sep 17 00:00:00 2001
From: 夜雨飘零 <yeyupiaoling@foxmail.com>
Date: 星期五, 02 二月 2024 23:06:13 +0800
Subject: [PATCH] Use ffmpeg read data (#1349)
---
funasr/auto/auto_model.py | 76 +++++++++++++++++++------------------
1 files changed, 39 insertions(+), 37 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 5bfc080..d99fc56 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -88,7 +88,8 @@
class AutoModel:
def __init__(self, **kwargs):
- tables.print()
+ if kwargs.get("disable_log", False):
+ tables.print()
model, kwargs = self.build_model(**kwargs)
@@ -123,7 +124,6 @@
self.preset_spk_num = kwargs.get("preset_spk_num", None)
if self.preset_spk_num:
logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
- logging.warning("Many to print when using speaker model...")
self.kwargs = kwargs
self.model = model
@@ -134,8 +134,6 @@
self.spk_model = spk_model
self.spk_kwargs = spk_kwargs
self.model_path = kwargs.get("model_path")
-
-
def build_model(self, **kwargs):
assert "model" in kwargs
@@ -146,7 +144,7 @@
set_all_random_seed(kwargs.get("seed", 0))
device = kwargs.get("device", "cuda")
- if not torch.cuda.is_available() or kwargs.get("ngpu", 0) == 0:
+ if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0:
device = "cpu"
kwargs["batch_size"] = 1
kwargs["device"] = device
@@ -200,8 +198,6 @@
res = self.model(*args, kwargs)
return res
-
-
def generate(self, input, input_len=None, **cfg):
if self.vad_model is None:
return self.inference(input, input_len=input_len, **cfg)
@@ -232,7 +228,7 @@
data_batch = data_list[beg_idx:end_idx]
key_batch = key_list[beg_idx:end_idx]
batch = {"data_in": data_batch, "key": key_batch}
- if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
+ 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
@@ -261,7 +257,7 @@
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
@@ -286,10 +282,10 @@
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"]
@@ -314,11 +310,11 @@
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 (
@@ -329,8 +325,6 @@
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)
if self.spk_model is not None:
-
-
# compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
for _b in range(len(speech_j)):
vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0,
@@ -339,23 +333,20 @@
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):
index = sorted_data[j][1]
@@ -378,7 +369,7 @@
result[k] = restored_data[j][k]
else:
result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
- elif k == 'raw_text':
+ elif 'text' in k:
if k not in result:
result[k] = restored_data[j][k]
else:
@@ -392,9 +383,10 @@
# 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"]
-
+
# speaker embedding cluster after resorted
if self.spk_model is not None:
all_segments = sorted(all_segments, key=lambda x: x[0])
@@ -402,29 +394,39 @@
labels = self.cb_model(spk_embedding.cpu(), oracle_num=self.preset_spk_num)
del result['spk_embedding']
sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
- if self.spk_mode == 'vad_segment':
+ if self.spk_mode == 'vad_segment': # recover sentence_list
sentence_list = []
for res, vadsegment in zip(restored_data, vadsegments):
sentence_list.append({"start": vadsegment[0],\
"end": vadsegment[1],
"sentence": res['raw_text'],
"timestamp": res['timestamp']})
- else: # punc_segment
+ elif self.spk_mode == 'punc_segment':
sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
result['timestamp'], \
result['raw_text'])
distribute_spk(sentence_list, sv_output)
result['sentence_info'] = sentence_list
+ elif kwargs.get("sentence_timestamp", False):
+ sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
+ result['timestamp'], \
+ result['raw_text'])
+ result['sentence_info'] = sentence_list
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)
- 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}")
+ 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
+ # 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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