speech_recognition/FunASR
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prepare_data_iterator

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游雁 2024-01-05 16:46:42 +08:00
parent 3e8b21a9f1
commit e63169bb06
1 changed files with 5 additions and 5 deletions
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10
funasr/bin/inference.py
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@ -21,7 +21,7 @@ from funasr.utils.load_utils import load_audio_and_text_image_video, extract_fba
from funasr.utils.vad_utils import slice_padding_audio_samples
from funasr.utils.timestamp_tools import time_stamp_sentence
def build_iter_for_infer(data_in, input_len=None, data_type=None, key=None):
def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
"""
:param input:
@ -62,7 +62,7 @@ def build_iter_for_infer(data_in, input_len=None, data_type=None, key=None):
if data_type is not None and isinstance(data_type, (list, tuple)):
data_list_tmp = []
for data_in_i, data_type_i in zip(data_in, data_type):
key_list, data_list_i = build_iter_for_infer(data_in=data_in_i, data_type=data_type_i)
key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
data_list_tmp.append(data_list_i)
data_list = []
for item in zip(*data_list_tmp):
@ -204,7 +204,7 @@ class AutoModel:
# if kwargs.get("device", "cpu") == "cpu":
# batch_size = 1
key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type, key=key)
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=data_type, key=key)
speed_stats = {}
asr_result_list = []
@ -268,7 +268,7 @@ class AutoModel:
batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
kwargs["batch_size"] = batch_size
data_type = kwargs.get("data_type", "sound")
key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type)
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=data_type)
results_ret_list = []
time_speech_total_all_samples = 0.0
@ -397,7 +397,7 @@ class AutoFrontend:
kwargs.update(cfg)
key_list, data_list = build_iter_for_infer(input, input_len=input_len)
key_list, data_list = prepare_data_iterator(input, input_len=input_len)
batch_size = kwargs.get("batch_size", 1)
device = kwargs.get("device", "cpu")
if device == "cpu":