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import os
import numpy as np
from predictions import get_embeddings
from utils.preprocessing import extract_fbanks
os.environ['KMP_DUPLICATE_LIB_OK']='True'
def load_data_speaker(labId):
speaker_path =f'./modelDir/{labId}/speaker/'
if os.path.exists(speaker_path):
data_dict = {}
for dir_name in os.listdir(speaker_path):
dir_path = os.path.join(speaker_path, dir_name)
if os.path.isdir(dir_path):
sub_data = {}
for file_name in os.listdir(dir_path):
if file_name.endswith('.npy'):
file_path = os.path.join(dir_path, file_name)
key = file_name.replace('.npy', '') # Sử dụng tên file làm key
value = np.load(file_path) # Load file .npy
sub_data[key] = value
data_dict[dir_name] = sub_data
return data_dict
else:
return "folder do not exist"
async def show_all_speaker(labId):
speaker_path =f'./modelDir/{labId}/speaker/'
if not os.path.exists(speaker_path):
os.makedirs(speaker_path)
list_user=os.listdir(speaker_path)
return {
"result": list_user
}
async def add_more_speaker(speech_file_path, speaker_name, labId):
speaker_path =f'./modelDir/{labId}/speaker/'
dir_ = speaker_path + speaker_name
if not os.path.exists(dir_):
os.makedirs(dir_)
fbanks = extract_fbanks(speech_file_path)
embeddings = get_embeddings(fbanks)
print('shape of embeddings: {}'.format(embeddings.shape), flush=True)
mean_embeddings = np.mean(embeddings, axis=0)
np.save(speaker_path+speaker_name+'/embeddings.npy',mean_embeddings)
list_user=os.listdir(speaker_path)
return {
"result": list_user
}
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