使用【python】写【声纹识别输出数据评价流程指标】的代码
时间: 2024-09-18 17:06:52 浏览: 16
根据提供的声纹识别评价流程描述,下面是一个简化版的Python脚本实现,它展示了如何基于给出的数据结构和计算方法来进行相关的评价指标计算:
```python
def evaluate_voice_recognition(pred_data, true_data):
# 初始化计数器
mutecount, mutefalse, newspeak_nopred, mutetrue = 0, 0, 0, 0
newspeak_false, newspeak_true, timefalse, timetrue = 0, 0, 0, 0
# 遍历预测数据
for index in range(len(pred_data)):
pred_speakid = pred_data[index]['speaker_id']
pred_length = pred_data[index].get('length', 0)
true_speakid = true_data[index]['speaker_id']
pred_begin = pred_data[index].get('begin_time', 0)
pred_end = pred_data[index].get('end_time', 0)
true_begin = true_data[index].get('begin_time', 0)
true_end = true_data[index].get('end_time', 0)
# 处理静音片段
if pred_speakid == 0:
mutecount += 1
if pred_length > 5.72 and pred_speakid != true_speakid:
if true_speakid == 1:
mutefalse += 1
elif true_speakid > 1:
newspeak_nopred += 1
else:
mutetrue += 1
# 处理新增说话人片段
elif pred_speakid > 1:
if pred_speakid != true_speakid:
if true_speakid == 1:
newspeak_false += 1
else:
newspeak_true += 1
# 处理时间覆盖
if true_speakid == 0:
if abs((pred_begin - true_begin)) > 1000 and abs((pred_end - true_end)) > 1000:
timefalse += 2
else:
timetrue += 1
# 计算各项评价指标
mute_accuracy = (mutecount - mutefalse) / mutecount if mutecount > 0 else 0
newspeak_precision = newspeak_true / (newspeak_true + newspeak_false) if (newspeak_true + newspeak_false) > 0 else 0
newspeak_recall = newspeak_true / (newspeak_true + newspeak_nopred) if (newspeak_true + newspeak_nopred) > 0 else 0
newspeak_F1 = 2 * newspeak_precision * newspeak_recall / (newspeak_precision + newspeak_recall) if (newspeak_precision + newspeak_recall) > 0 else 0
time_accuracy = timetrue / (timetrue + timefalse) if (timetrue + timefalse) > 0 else 0
return {
"mute_accuracy": mute_accuracy,
"newspeak_precision": newspeak_precision,
"newspeak_recall": newspeak_recall,
"newspeak_F1": newspeak_F1,
"time_accuracy": time_accuracy
}
# 示例输入数据
predicted_segments = [
{"speaker_id": 0, "length": 6.0, "begin_time": 0, "end_time": 10},
{"speaker_id": 1, "length": 4.5, "begin_time": 11, "end_time": 20},
# 更多预测片段...
]
true_segments = [
{"speaker_id": 1, "begin_time": 0, "end_time": 10},
{"speaker_id": 1, "begin_time": 11, "end_time": 20},
# 更多真实片段...
]
evaluation_metrics = evaluate_voice_recognition(predicted_segments, true_segments)
print(evaluation_metrics)
```
此代码仅实现了部分功能且做了大量简化处理,例如没有处理复杂的音频片段划分及更详细的错误类型分析等。在实际应用中,还需要进一步扩展和完善,比如引入更多细节校验、异常处理机制以及支持更复杂的数据格式等。