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Configuration error
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import argparse
import os
import json
import shutil
from datetime import datetime
from mvector.predict import MVectorPredictor
def if_not_mkdir(path):
if not os.path.exists(path):
os.mkdir(path)
def about():
print(r"""
___ ___ ____ __ __ _ ______ ____
/ | __ ______/ (_)___ / __ \____ _/ /_____ _________ / /_ | | / / __ \/ __ \
/ /| |/ / / / __ / / __ \ / / / / __ `/ __/ __ `/ ___/ _ \/ __/ | | / / /_/ / /_/ /
/ ___ / /_/ / /_/ / / /_/ / / /_/ / /_/ / /_/ /_/ (__ ) __/ /_ | |/ / ____/ _, _/
/_/ |_\__,_/\__,_/_/\____/ /_____/\__,_/\__/\__,_/____/\___/\__/ |___/_/ /_/ |_|
Audio Dataset Voiceprint Recognition Classifier by 2DIPW based on yeyupiaoling/VoiceprintRecognition-Pytorch
Licensed under GNU General Public License v3. Open source leads the world to a brighter future!
""")
if __name__ == "__main__":
about()
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--model_path', type=str, default="model/", help="Path to model.")
parser.add_argument('-c', '--configs', type=str, default="model/config.yml", help="Path to model config file.")
parser.add_argument('-d', '--device', type=str, default="gpu", help='Device to use, gpu or cpu.')
parser.add_argument('-l', '--label_path', type=str, default="labels/", help="Path to Voice feature library.")
parser.add_argument('-t', '--threshold', type=float, default=0.6, help="Threshold for judging compliance.")
parser.add_argument('-i', '--input_path', type=str, default="input/", help="Path to input files.")
parser.add_argument('-o', '--output_path', type=str, default="output/", help="Path to output files.")
parser.add_argument('-k', '--keep_unrecognized', action='store_true', default=False,
help='Do not move unrecognized files.')
args = parser.parse_args()
predictor = MVectorPredictor(configs=args.configs,
threshold=args.threshold,
label_path=args.label_path,
model_path=args.model_path,
use_gpu=True if args.device == "gpu" else False)
if not os.path.exists(args.input_path):
raise Exception("Input path not existed.")
# Get labels dict from label_path
labels_list = [f for f in os.listdir(args.label_path) if os.path.isdir(os.path.join(args.label_path, f))]
labels_dict = {}
for i, label in enumerate(labels_list):
labels_dict[label] = i + 1
print(f"Feature labels:{labels_dict}")
# Get input files list from input_path
input_files = []
for root, dirs, files in os.walk(args.input_path):
input_files += [os.path.abspath(os.path.join(root, f)) for f in files if f.split('.')[-1].upper() in ["WAV"]]
input_files_amount = len(input_files)
result_dicts_list = []
for i, file in enumerate(input_files):
try:
label, similarity = predictor.recognition(audio_data=file)
if label:
print(
f"\033[32m[{i + 1}/{input_files_amount}]\033[0m \033[33m{os.path.basename(file)}\033[0m is recognized as speaker \033[31m{label}\033[0m, the max similarity is \033[34m{similarity}\033[0m.")
result_dicts_list.append(
{"Filepath": file, "Label": labels_dict[label], "Similarity": float(similarity)})
else:
print(
f"\033[32m[{i + 1}/{input_files_amount}]\033[0m \033[33m{os.path.basename(file)}\033[0m could not be recognized as any speaker.")
result_dicts_list.append(
{"Filepath": file, "Label": 0, "Similarity": 0})
except Exception as e:
print(
f"\033[32m[{i + 1}/{input_files_amount}]\033[0m An error occurred while processing \033[33m{os.path.basename(file)}\033[0m : {e}")
output_path_for_this_run = os.path.join(args.output_path, datetime.now().strftime("VPR_Result_%Y%m%d_%H%M%S"))
if_not_mkdir(output_path_for_this_run)
json_path = os.path.abspath(os.path.join(output_path_for_this_run, "result.json"))
# Move input files to category folders
print("Moving input files to category folders...")
folder_list_without_unrecognized = [os.path.abspath(os.path.join(output_path_for_this_run, label)) for label in
labels_list]
folder_list = [os.path.abspath(
os.path.join(output_path_for_this_run, "Unrecognized"))] + folder_list_without_unrecognized
for folder in folder_list:
if_not_mkdir(folder)
for result in result_dicts_list:
if args.keep_unrecognized and result["Label"] == 0:
continue
destination_folder = folder_list[result["Label"]]
try:
shutil.move(result["Filepath"], destination_folder)
result["Filepath"] = os.path.abspath(os.path.join(destination_folder, os.path.basename(result["Filepath"])))
except Exception as e:
print(e)
# Write result json file to output_path
with open(json_path, "w") as f:
json.dump({"Labels": {str(i + 1): folder for i, folder in enumerate(folder_list_without_unrecognized)},
"Files": result_dicts_list}, f, indent=4)
print(f"Result json is saved as {json_path}")
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