from visqol_rs_py import ViSQOLCalculator

# 创建计算器
calculator = ViSQOLCalculator(max_workers=4)

# 批量计算
results = calculator.calculate_batch(
    reference_dir="./reference_audio",
    degraded_dir="./degraded_audio",
    output_file="results.json"
)

# 查看统计信息
stats = results["statistics"]
print(f"成功处理: {stats['successful']}/{stats['total_files']} 文件")
print(f"平均MOS-LQO: {stats['mos_lqo_mean']:.3f}")

import numpy as np
from visqol_rs_py import ViSQOLCalculator

# 创建计算器
calculator = ViSQOLCalculator(max_workers=4)

# 准备音频数据（1D numpy数组，值范围[-1.0, 1.0]）
sample_rate = 48000
duration = 3.0  # 3秒
t = np.linspace(0, duration, int(sample_rate * duration))

# 参考音频（纯正弦波）
reference_audio = 0.5 * np.sin(2 * np.pi * 440 * t)

# 降质音频（加噪声）
degraded_audio = reference_audio + 0.1 * np.random.randn(len(reference_audio))

# 单个音频对计算
result = calculator.calculate_single_numpy(
    reference_array=reference_audio,
    degraded_array=degraded_audio,
    sample_rate=sample_rate
)

print(f"MOS-LQO分数: {result['mos_lqo']:.3f}")
print(f"处理时间: {result['processing_time']:.2f}秒")

# 批量numpy数组计算
reference_arrays = [reference_audio, reference_audio * 0.8]
degraded_arrays = [degraded_audio, degraded_audio * 0.8]

batch_results = calculator.calculate_batch_numpy(
    reference_arrays=reference_arrays,
    degraded_arrays=degraded_arrays,
    sample_rate=sample_rate
)

print(f"批量处理完成，平均MOS-LQO: {batch_results['statistics']['mos_lqo_mean']:.3f}")

import numpy as np
import visqol_native

# 创建原生计算器（语音模式，16kHz优化）
calculator = visqol_native.VisqolCalculator.speech_mode()

# 准备音频数据（1D numpy数组，值范围[-1.0, 1.0]）
sample_rate = 16000  # 语音模式使用16kHz
duration = 2.0  # 2秒
t = np.linspace(0, duration, int(sample_rate * duration))

# 参考音频
reference_audio = 0.5 * np.sin(2 * np.pi * 440 * t)

# 降质音频（加噪声）
degraded_audio = reference_audio + 0.05 * np.random.randn(len(reference_audio))

# 直接计算（无文件I/O，无子进程）
result = calculator.calculate(
    reference_audio=reference_audio,
    degraded_audio=degraded_audio,
    sample_rate=sample_rate
)

print(f"MOS-LQO分数: {result.moslqo:.3f}")
print(f"相似度分数: {result.similarity_score:.3f}")
print(f"处理时间: {result.processing_time:.3f}秒")  # ~6x faster!

import os
optimal_workers = os.cpu_count()  # 获取CPU核心数
calculator = ViSQOLCalculator(max_workers=optimal_workers)

# 启用详细日志
calculator = ViSQOLCalculator(verbose=True)

# 或使用命令行
visqol-batch ./ref ./deg --verbose