diff --git a/requirements.txt b/requirements.txt
index 182fd70..d62f6cd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,3 @@
-librosa>=0.10.0
-numpy>=1.24.0
+torch>=2.0.0
+torchaudio>=2.0.0
 soundfile>=0.12.0
diff --git a/songrate/analyzers/__init__.py b/songrate/analyzers/__init__.py
index 0e973df..849a2e2 100644
--- a/songrate/analyzers/__init__.py
+++ b/songrate/analyzers/__init__.py
@@ -1,18 +1,38 @@
-"""音频分析器基类和工具函数"""
-import numpy as np
+"""音频分析器基类和工具函数 - GPU 版本"""
+import torch
+import torchaudio
+
+
+def get_device():
+    """获取 GPU 设备"""
+    return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 
 def load_audio(filepath, sr=22050):
-    """加载音频文件，返回 (y, sr)"""
-    import librosa
-    return librosa.load(filepath, sr=sr)
+    """加载音频文件到 GPU，返回 (waveform, sr)"""
+    device = get_device()
+    waveform, orig_sr = torchaudio.load(filepath)
+    
+    # 单声道
+    if waveform.shape[0] > 1:
+        waveform = waveform.mean(dim=0, keepdim=True)
+    
+    # 重采样
+    if orig_sr != sr:
+        resampler = torchaudio.transforms.Resample(orig_sr, sr).to(device)
+        waveform = resampler(waveform.to(device))
+    
+    # 返回 1D 张量
+    return waveform.squeeze(0).to(device), sr
 
 
 def safe_float(val, default=0.0):
     """安全转换为 float，处理 NaN/Inf"""
     if val is None:
         return default
+    if isinstance(val, torch.Tensor):
+        val = val.item()
     val = float(val)
-    if np.isnan(val) or np.isinf(val):
+    if torch.isnan(torch.tensor(val)) or torch.isinf(torch.tensor(val)):
         return default
     return val
diff --git a/songrate/analyzers/danceability.py b/songrate/analyzers/danceability.py
index e751b29..9d4a1a6 100644
--- a/songrate/analyzers/danceability.py
+++ b/songrate/analyzers/danceability.py
@@ -1,51 +1,67 @@
-"""可舞性分析 - 低频占比、节拍清晰度、节奏规律性"""
-import numpy as np
+"""可舞性分析 - GPU 版本"""
+import torch
 from . import safe_float
 
 
-def analyze_danceability(y, sr):
-    """
-    分析可舞性维度
-    """
-    import librosa
-
-    # 低频能量占比 (bass ratio)
-    S = np.abs(librosa.stft(y))
-    freqs = librosa.fft_frequencies(sr=sr)
-
-    low_mask = freqs < 250  # 低频 < 250Hz
-    low_energy = np.sum(S[low_mask] ** 2)
-    total_energy = np.sum(S ** 2)
+def analyze_danceability(y, sr, stft_result=None):
+    """分析可舞性维度 (GPU)"""
+    device = y.device
+    
+    # 使用预计算 STFT 或重新计算
+    if stft_result is None:
+        stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
+    magnitude = stft_result.abs() ** 2  # 功率谱
+    
+    # 频率轴
+    freqs = torch.fft.fftfreq(2048, 1.0/sr)[:1025]
+    
+    # 低频能量占比 (< 250Hz)
+    low_mask = freqs < 250
+    low_energy = magnitude[low_mask].sum()
+    total_energy = magnitude.sum()
     bass_ratio = safe_float(low_energy / total_energy) if total_energy > 0 else 0.0
-
-    # 节拍清晰度
-    onset_env = librosa.onset.onset_strength(y=y, sr=sr)
-    pulse = librosa.beat.plp(onset_envelope=onset_env, sr=sr)
-    beat_clarity = safe_float(min(np.mean(pulse), 1.0))
-
-    # 节奏规律性 - 自相关峰值
-    if len(onset_env) > 10:
-        autocorr = np.correlate(onset_env, onset_env, mode='full')
-        autocorr = autocorr[len(autocorr) // 2:]
+    
+    # 节拍清晰度 (复用 rhythm 的逻辑)
+    spectral_flux = torch.diff(magnitude.sum(dim=0))
+    spectral_flux = torch.nn.functional.relu(spectral_flux)
+    
+    if len(spectral_flux) > 10:
+        mean_flux = spectral_flux.mean()
+        std_flux = spectral_flux.std()
+        if std_flux > 0:
+            kurtosis = torch.mean(((spectral_flux - mean_flux) / std_flux) ** 4)
+            beat_clarity = safe_float(torch.clamp(kurtosis / 10.0, 0, 1))
+        else:
+            beat_clarity = 0.3
+    else:
+        beat_clarity = 0.5
+    
+    # 节奏规律性 - 自相关
+    if len(spectral_flux) > 10:
+        autocorr = torch.nn.functional.conv1d(
+            spectral_flux.unsqueeze(0).unsqueeze(0),
+            spectral_flux.unsqueeze(0).unsqueeze(0).flip(-1),
+            padding=len(spectral_flux) - 1
+        ).squeeze()
+        autocorr = autocorr[len(autocorr)//2:]
         if autocorr[0] > 0:
             autocorr = autocorr / autocorr[0]
-        # 找第一个显著峰值（排除前几个采样点）
-        peaks = np.where(autocorr[10:] > 0.5)[0]
-        regularity = safe_float(min(len(peaks) / 20.0, 1.0)) if len(peaks) > 0 else 0.3
+        peaks = (autocorr[10:] > 0.5).sum().item()
+        regularity = min(peaks / 20.0, 1.0)
     else:
         regularity = 0.5
-
+    
     scores = {
         "bass_ratio": round(bass_ratio * 10, 2),
         "beat_clarity": round(beat_clarity * 10, 2),
         "regularity": round(regularity * 10, 2),
     }
-
+    
     score = (
         0.35 * scores["bass_ratio"] +
         0.35 * scores["beat_clarity"] +
         0.30 * scores["regularity"]
     )
     scores["score"] = round(score, 2)
-
+    
     return scores
diff --git a/songrate/analyzers/energy.py b/songrate/analyzers/energy.py
index 56f23b7..12e8c67 100644
--- a/songrate/analyzers/energy.py
+++ b/songrate/analyzers/energy.py
@@ -1,52 +1,61 @@
-"""能量分析 - RMS、动态范围、能量变化"""
-import numpy as np
+"""能量分析 - GPU 版本"""
+import torch
 from . import safe_float
 
 
-def analyze_energy(y, sr):
-    """
-    分析能量维度
-    """
-    import librosa
-
-    # RMS 能量
-    rms = librosa.feature.rms(y=y, frame_length=2048, hop_length=512)[0]
-    rms_db = librosa.amplitude_to_db(rms, ref=np.max)
-    rms_mean = safe_float(np.mean(rms_db))
-
-    # 动态范围 (dB)
-    rms_valid = rms_db[rms_db > -60]  # 过滤静音段
-    if len(rms_valid) > 0:
-        dynamic_range = safe_float(np.max(rms_valid) - np.min(rms_valid))
+def analyze_energy(y, sr, stft_result=None):
+    """分析能量维度 (GPU)"""
+    device = y.device
+    
+    # RMS 能量 - 直接从音频计算
+    frame_length = 2048
+    hop_length = 512
+    n_frames = (len(y) - frame_length) // hop_length + 1
+    
+    if n_frames > 0:
+        frames = torch.nn.functional.unfold(
+            y.unsqueeze(0).unsqueeze(0),
+            kernel_size=(1, frame_length),
+            stride=(1, hop_length)
+        ).squeeze(0).squeeze(0)
+        rms = torch.sqrt(torch.mean(frames ** 2, dim=0))
+        rms_db = 20 * torch.log10(torch.clamp(rms, 1e-10, None))
+        rms_mean = safe_float(rms_db.mean())
+        
+        # 动态范围
+        rms_valid = rms_db[rms_db > -60]
+        if len(rms_valid) > 0:
+            dynamic_range = safe_float(rms_valid.max() - rms_valid.min())
+        else:
+            dynamic_range = 0.0
+        
+        # 能量变化
+        rms_mean_val = rms.mean()
+        if rms_mean_val > 0:
+            variation = safe_float(rms.std() / rms_mean_val)
+            variation_score = min(variation / 0.5, 1.0)
+        else:
+            variation_score = 0.0
     else:
+        rms_mean = -60.0
         dynamic_range = 0.0
-
-    # 能量变化 - 能量曲线的变异系数
-    if len(rms) > 0 and np.mean(rms) > 0:
-        variation = safe_float(np.std(rms) / np.mean(rms))
-        variation_score = min(variation / 0.5, 1.0)  # 50% 变化以上满分
-    else:
         variation_score = 0.0
-
+    
     scores = {
         "rms": round(rms_mean, 2),
         "dynamic_range": round(dynamic_range, 2),
         "variation": round(variation_score * 10, 2),
     }
-
-    # 评分逻辑：动态范围合理 + 有起伏 = 高分
-    # 动态范围：15-30 dB 为优秀（不过度压缩也不过于极端）
+    
+    # 评分
     if dynamic_range >= 10 and dynamic_range <= 35:
         dr_score = 8.0 + min((dynamic_range - 10) / 25 * 2, 2.0)
     elif dynamic_range > 35:
-        dr_score = 7.0  # 过大，可能有静音段
+        dr_score = 7.0
     else:
-        dr_score = dynamic_range / 10 * 8.0  # 过小，过度压缩
-
-    score = (
-        0.40 * dr_score +
-        0.60 * scores["variation"]
-    )
+        dr_score = dynamic_range / 10 * 8.0
+    
+    score = 0.40 * dr_score + 0.60 * scores["variation"]
     scores["score"] = round(min(score, 10), 2)
-
+    
     return scores
diff --git a/songrate/analyzers/mood.py b/songrate/analyzers/mood.py
index e59c010..6dfa0d4 100644
--- a/songrate/analyzers/mood.py
+++ b/songrate/analyzers/mood.py
@@ -1,72 +1,83 @@
-"""情绪分析 - 愉悦度、唤醒度、情绪清晰度"""
-import numpy as np
+"""情绪分析 - GPU 版本"""
+import torch
 from . import safe_float
 
 
-def analyze_mood(y, sr):
-    """
-    分析情绪维度（基于音频特征的启发式方法）
-    后续可升级为 CLAP 深度学习模型
-    """
-    import librosa
-
-    # 提取特征
-    # 1. 频谱质心 (brightness) → 关联 valence
-    spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
-    brightness = safe_float(np.mean(spectral_centroid))
-
-    # 2. 频谱对比度 → 关联 arousal
-    spectral_contrast = librosa.feature.spectral_contrast(y=y, sr=sr)
-    contrast_mean = safe_float(np.mean(spectral_contrast))
-
-    # 3. 过零率 → 关联 arousal
-    zcr = librosa.feature.zero_crossing_rate(y)[0]
-    zcr_mean = safe_float(np.mean(zcr))
-
-    # 4. RMS → 关联 arousal
-    rms = librosa.feature.rms(y=y)[0]
-    rms_mean = safe_float(np.mean(rms))
-
-    # 5. 调性 (major/minor) → 关联 valence
-    chroma = librosa.feature.chroma_stft(y=y, sr=sr)
-    major_profile = np.array([6.35, 2.23, 3.48, 2.33, 4.38, 4.09,
-                              2.52, 5.19, 2.39, 3.66, 2.29, 2.88])
-    minor_profile = np.array([6.33, 2.68, 3.52, 5.38, 2.60, 3.53,
-                              2.54, 4.75, 3.98, 2.69, 3.34, 3.17])
-    major_profile = major_profile / np.sum(major_profile)
-    minor_profile = minor_profile / np.sum(minor_profile)
-
-    chroma_mean = np.mean(chroma, axis=1)
-    major_corr = np.dot(chroma_mean, major_profile) / (np.linalg.norm(chroma_mean) * np.linalg.norm(major_profile))
-    minor_corr = np.dot(chroma_mean, minor_profile) / (np.linalg.norm(chroma_mean) * np.linalg.norm(minor_profile))
+def analyze_mood(y, sr, stft_result=None):
+    """分析情绪维度 (GPU)"""
+    device = y.device
+    
+    # 使用预计算 STFT 或重新计算
+    if stft_result is None:
+        stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
+    magnitude = stft_result.abs()
+    
+    freqs = torch.fft.fftfreq(2048, 1.0/sr)[:1025]
+    
+    # 频谱质心 (brightness)
+    freq_sum = (freqs.unsqueeze(1) * magnitude).sum(dim=0)
+    mag_sum = magnitude.sum(dim=0)
+    spectral_centroid = freq_sum / torch.clamp(mag_sum, 1e-10)
+    brightness = safe_float(spectral_centroid.mean())
+    
+    # 频谱对比度
+    spectral_contrast = magnitude.max(dim=0).values - magnitude.min(dim=0).values
+    contrast_mean = safe_float(spectral_contrast.mean())
+    
+    # 过零率
+    zcr = torch.sum(torch.abs(torch.diff(torch.sign(y)))) / (2 * len(y))
+    zcr_mean = safe_float(zcr)
+    
+    # RMS
+    rms = torch.sqrt(torch.mean(y ** 2))
+    rms_mean = safe_float(rms)
+    
+    # 调性检测 (major/minor) - 简化版 chroma
+    # 12 个音级的能量
+    chroma = torch.zeros(12, device=device)
+    note_freqs = 440 * (2 ** (torch.arange(12, device=device, dtype=torch.float32) / 12))
+    for i, nf in enumerate(note_freqs):
+        # 找最接近的频率 bin
+        bin_idx = torch.argmin(torch.abs(freqs - nf)).item()
+        if bin_idx < magnitude.shape[0]:
+            chroma[i] = magnitude[bin_idx].mean()
+    
+    # 归一化
+    chroma = chroma / torch.clamp(chroma.sum(), 1e-10)
+    
+    # Krumhansl 模板 (简化)
+    major_profile = torch.tensor([6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88], device=device)
+    minor_profile = torch.tensor([6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17], device=device)
+    major_profile = major_profile / major_profile.sum()
+    minor_profile = minor_profile / minor_profile.sum()
+    
+    major_corr = torch.dot(chroma, major_profile) / (torch.norm(chroma) * torch.norm(major_profile) + 1e-10)
+    minor_corr = torch.dot(chroma, minor_profile) / (torch.norm(chroma) * torch.norm(minor_profile) + 1e-10)
     is_major = major_corr >= minor_corr
-
-    # 计算 valence (愉悦度): 大调 + 高亮度 + 高对比度 → 快乐
-    # 归一化到 0-1
-    brightness_norm = min(brightness / 4000, 1.0)  # 4000Hz 以上算高亮
+    
+    # Valence: 大调 + 高亮度 + 高对比度
+    brightness_norm = min(brightness / 4000, 1.0)
     valence = 0.4 * brightness_norm + 0.3 * (1.0 if is_major else 0.3) + 0.3 * min(contrast_mean / 30, 1.0)
     valence = safe_float(min(max(valence, 0), 1.0))
-
-    # 计算 arousal (唤醒度): 高 ZCR + 高 RMS → 兴奋
+    
+    # Arousal: 高 ZCR + 高 RMS
     zcr_norm = min(zcr_mean / 0.1, 1.0)
     rms_norm = min(rms_mean / 0.1, 1.0)
     arousal = 0.5 * zcr_norm + 0.5 * rms_norm
     arousal = safe_float(min(max(arousal, 0), 1.0))
-
-    # 情绪清晰度 - 特征是否集中在某个象限
-    # 如果 valence 和 arousal 都接近 0.5，说明情绪模糊
-    valence_distance = abs(valence - 0.5) * 2  # 0-1，离中心越远越清晰
+    
+    # 情绪清晰度
+    valence_distance = abs(valence - 0.5) * 2
     arousal_distance = abs(arousal - 0.5) * 2
     clarity = (valence_distance + arousal_distance) / 2
-
+    
     scores = {
         "valence": round(valence * 10, 2),
         "arousal": round(arousal * 10, 2),
         "clarity": round(clarity * 10, 2),
     }
-
-    # 情绪评分：清晰度为主
+    
     score = 0.40 * scores["clarity"] + 0.30 * scores["valence"] + 0.30 * scores["arousal"]
     scores["score"] = round(min(score, 10), 2)
-
+    
     return scores
diff --git a/songrate/analyzers/quality.py b/songrate/analyzers/quality.py
index 287fbce..f9062bc 100644
--- a/songrate/analyzers/quality.py
+++ b/songrate/analyzers/quality.py
@@ -1,63 +1,69 @@
-"""音频质量分析 - 信噪比、削波检测、频率均衡"""
-import numpy as np
+"""音频质量分析 - GPU 版本"""
+import torch
 from . import safe_float
 
 
-def analyze_quality(y, sr):
-    """
-    分析音频质量维度
-    """
-    import librosa
-
-    # 信噪比估算 - 信号能量 vs 安静段能量
-    rms = librosa.feature.rms(y=y, frame_length=2048, hop_length=512)[0]
-    rms_sorted = np.sort(rms)
-    n = len(rms_sorted)
-    if n > 10:
-        noise_floor = np.mean(rms_sorted[:n // 10])  # 最安静的 10%
-        signal_level = np.mean(rms_sorted[-n // 10:])  # 最响的 10%
+def analyze_quality(y, sr, stft_result=None):
+    """分析音频质量维度 (GPU)"""
+    
+    # 信噪比估算
+    frame_length = 2048
+    hop_length = 512
+    n_frames = (len(y) - frame_length) // hop_length + 1
+    
+    if n_frames > 10:
+        frames = torch.nn.functional.unfold(
+            y.unsqueeze(0).unsqueeze(0),
+            kernel_size=(1, frame_length),
+            stride=(1, hop_length)
+        ).squeeze(0).squeeze(0)
+        rms = torch.sqrt(torch.mean(frames ** 2, dim=0))
+        rms_sorted, _ = torch.sort(rms)
+        n = len(rms_sorted)
+        noise_floor = rms_sorted[:n // 10].mean()
+        signal_level = rms_sorted[-n // 10:].mean()
         if noise_floor > 0:
-            snr_db = safe_float(20 * np.log10(signal_level / noise_floor))
+            snr_db = safe_float(20 * torch.log10(signal_level / noise_floor))
         else:
-            snr_db = 60.0  # 无噪声
+            snr_db = 60.0
     else:
         snr_db = 30.0
-
-    # 削波检测 - 检查是否有接近 1.0 或 -1.0 的采样
-    clipped = np.sum(np.abs(y) > 0.99) / len(y)
+    
+    # 削波检测
+    clipped = (torch.abs(y) > 0.99).sum().item() / len(y)
     clipping_score = 10.0 if clipped < 0.001 else max(0, 10.0 - clipped * 10000)
-
+    
     # 频率均衡 - 频谱平坦度
-    spectral_flatness = librosa.feature.spectral_flatness(y=y)[0]
-    flatness_mean = safe_float(np.mean(spectral_flatness))
-    # 频谱太平坦 = 白噪声，太不平坦 = 某些频段缺失
-    # 理想范围：0.001 - 0.1
+    if stft_result is not None:
+        magnitude = stft_result.abs()
+        geometric_mean = torch.exp(torch.log(torch.clamp(magnitude, 1e-10)).mean(dim=0))
+        arithmetic_mean = magnitude.mean(dim=0)
+        flatness = geometric_mean / torch.clamp(arithmetic_mean, 1e-10)
+        flatness_mean = safe_float(flatness.mean())
+    else:
+        flatness_mean = 0.05
+    
     if 0.001 <= flatness_mean <= 0.1:
         freq_balance = 8.0
     elif flatness_mean < 0.001:
-        freq_balance = 6.0  # 过于集中
+        freq_balance = 6.0
     else:
-        freq_balance = 5.0  # 过于平坦
-
+        freq_balance = 5.0
+    
     scores = {
         "snr": round(snr_db, 2),
         "clipping": round(clipping_score, 2),
         "frequency_balance": round(freq_balance, 2),
     }
-
-    # SNR 评分: >40dB 优秀, 20-40 良好, <20 差
+    
     if snr_db >= 40:
         snr_score = 10.0
     elif snr_db >= 20:
         snr_score = 6.0 + (snr_db - 20) / 20 * 4.0
     else:
         snr_score = max(snr_db / 20 * 6.0, 0)
-
-    score = (
-        0.40 * snr_score +
-        0.35 * scores["clipping"] +
-        0.25 * scores["frequency_balance"]
-    )
+    
+    score = 0.40 * snr_score + 0.35 * scores["clipping"] + 0.25 * scores["frequency_balance"]
     scores["score"] = round(min(score, 10), 2)
-
+    
     return scores
diff --git a/songrate/analyzers/rhythm.py b/songrate/analyzers/rhythm.py
index 1934db0..8a908e9 100644
--- a/songrate/analyzers/rhythm.py
+++ b/songrate/analyzers/rhythm.py
@@ -1,65 +1,93 @@
-"""节奏分析 - BPM、节拍清晰度、稳定性、律动感"""
-import numpy as np
+"""节奏分析 - GPU 版本 (PyTorch)"""
+import torch
 from . import safe_float
 
 
-def analyze_rhythm(y, sr):
+def analyze_rhythm(y, sr, stft_result=None):
     """
-    分析节奏维度
-    输入: y (音频数据), sr (采样率)
-    输出: dict {bpm, beat_clarity, stability, groove, score}
+    分析节奏维度 (GPU)
+    输入: y (GPU tensor), sr, stft_result (预计算STFT)
     """
-    import librosa
-
-    # BPM
-    tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
-    bpm = safe_float(tempo)
-
-    # 节拍清晰度 (beat_track 返回的 confidence)
-    # 新版 librosa 返回 tuple (tempo, beats)，confidence 需要从 onset strength 推算
-    onset_env = librosa.onset.onset_strength(y=y, sr=sr)
-    pulse = librosa.beat.plp(onset_envelope=onset_env, sr=sr)
-    beat_clarity = safe_float(np.mean(pulse))
-    # 归一化到 0-1
-    beat_clarity = min(beat_clarity, 1.0)
-
-    # 节奏稳定性 - 节拍间隔的变异系数
-    if len(beats) > 2:
-        beat_times = librosa.frames_to_time(beats, sr=sr)
-        intervals = np.diff(beat_times)
-        if np.mean(intervals) > 0:
-            cv = np.std(intervals) / np.mean(intervals)
-            stability = safe_float(1.0 - min(cv, 1.0))
+    device = y.device
+    
+    # 使用预计算的 STFT 或重新计算
+    if stft_result is None:
+        stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
+    magnitude = stft_result.abs()
+    
+    # Onset strength (频谱变化率)
+    spectral_flux = torch.diff(magnitude.sum(dim=0))
+    spectral_flux = torch.nn.functional.relu(spectral_flux)  # 只保留正变化
+    
+    # 自相关找 BPM
+    if len(spectral_flux) > 100:
+        autocorr = torch.nn.functional.conv1d(
+            spectral_flux.unsqueeze(0).unsqueeze(0),
+            spectral_flux.unsqueeze(0).unsqueeze(0).flip(-1),
+            padding=len(spectral_flux) - 1
+        ).squeeze()
+        
+        # 找第一个峰值 (排除前 10 个采样点)
+        autocorr_lag = autocorr[10:]
+        if len(autocorr_lag) > 0:
+            peak_idx = torch.argmax(autocorr_lag).item()
+            # BPM = 60 * sr / hop_length / peak_idx
+            hop_per_second = sr / 512
+            bpm = 60.0 * hop_per_second / (peak_idx + 10)
         else:
-            stability = 0.0
+            bpm = 120.0
+    else:
+        bpm = 120.0
+    
+    # 节拍清晰度 - onset 的峰度
+    if len(spectral_flux) > 10:
+        mean_flux = spectral_flux.mean()
+        std_flux = spectral_flux.std()
+        if std_flux > 0:
+            kurtosis = torch.mean(((spectral_flux - mean_flux) / std_flux) ** 4)
+            beat_clarity = safe_float(torch.clamp(kurtosis / 10.0, 0, 1))
+        else:
+            beat_clarity = 0.3
+    else:
+        beat_clarity = 0.5
+    
+    # 节奏稳定性 - onset 间隔的变异系数
+    if len(spectral_flux) > 20:
+        # 简单检测峰值
+        peaks = (spectral_flux[1:-1] > spectral_flux[:-2]) & (spectral_flux[1:-1] > spectral_flux[2:])
+        peak_indices = torch.where(peaks)[0]
+        if len(peak_indices) > 2:
+            intervals = torch.diff(peak_indices.float())
+            cv = intervals.std() / intervals.mean() if intervals.mean() > 0 else 1.0
+            stability = safe_float(1.0 - torch.clamp(cv, 0, 1))
+        else:
+            stability = 0.5
     else:
         stability = 0.5
-
-    # 律动感 - 基于 onset strength 的方差（高方差 = 更多动态变化 = 更有律动）
-    if len(onset_env) > 0:
-        onset_std = np.std(onset_env)
-        onset_mean = np.mean(onset_env)
+    
+    # 律动感 - onset 的方差/均值
+    if len(spectral_flux) > 0:
+        onset_mean = spectral_flux.mean()
+        onset_std = spectral_flux.std()
         if onset_mean > 0:
-            groove = safe_float(min(onset_std / onset_mean, 1.0))
+            groove = safe_float(torch.clamp(onset_std / onset_mean, 0, 1))
         else:
             groove = 0.0
     else:
         groove = 0.0
-
-    # 子维度评分（均为 0-10）
+    
     scores = {
-        "bpm": bpm,
+        "bpm": round(safe_float(bpm), 1),
         "beat_clarity": round(beat_clarity * 10, 2),
         "stability": round(stability * 10, 2),
         "groove": round(groove * 10, 2),
     }
-
-    # 维度综合分（不限制 BPM，只评估质量）
+    
     score = (
         0.35 * scores["beat_clarity"] +
         0.30 * scores["stability"] +
         0.35 * scores["groove"]
     )
     scores["score"] = round(score, 2)
-
+    
     return scores
diff --git a/songrate/analyzers/timbre.py b/songrate/analyzers/timbre.py
index e0252a3..dc6b93f 100644
--- a/songrate/analyzers/timbre.py
+++ b/songrate/analyzers/timbre.py
@@ -1,54 +1,58 @@
-"""音色分析 - 音色丰富度、频谱平衡"""
-import numpy as np
+"""音色分析 - GPU 版本"""
+import torch
+import torchaudio
 from . import safe_float
 
 
-def analyze_timbre(y, sr):
-    """
-    分析音色维度
-    """
-    import librosa
-
-    # 频谱质心 (brightness)
-    spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
-    centroid_mean = safe_float(np.mean(spectral_centroid))
-
-    # MFCC (13维音色指纹)
-    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
-    mfcc_mean = np.mean(mfcc, axis=1)
-
-    # 音色丰富度 - MFCC 的方差（方差大 = 音色变化丰富）
-    mfcc_variance = np.var(mfcc, axis=1)
-    richness = safe_float(np.mean(mfcc_variance))
-    richness_norm = min(richness / 100, 1.0)  # 归一化
-
-    # 频谱平衡 - 各频段能量分布的均匀度
-    S = np.abs(librosa.stft(y))
-    freqs = librosa.fft_frequencies(sr=sr)
-
-    # 分频段：低频 (<250Hz), 中频 (250-2000Hz), 高频 (>2000Hz)
-    low = np.sum(S[freqs < 250] ** 2)
-    mid = np.sum((freqs >= 250) & (freqs < 2000))
-    mid_energy = np.sum(S[(freqs >= 250) & (freqs < 2000)] ** 2)
-    high_energy = np.sum(S[freqs >= 2000] ** 2)
-
-    total = low + mid_energy + high_energy
+def analyze_timbre(y, sr, stft_result=None):
+    """分析音色维度 (GPU)"""
+    device = y.device
+    
+    if stft_result is None:
+        stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
+    magnitude = stft_result.abs()
+    
+    freqs = torch.fft.fftfreq(2048, 1.0/sr)[:1025]
+    
+    # 频谱质心
+    freq_sum = (freqs.unsqueeze(1) * magnitude).sum(dim=0)
+    mag_sum = magnitude.sum(dim=0)
+    spectral_centroid = freq_sum / torch.clamp(mag_sum, 1e-10)
+    centroid_mean = safe_float(spectral_centroid.mean())
+    
+    # MFCC (简化版 - 用 mel 滤波器组)
+    mel_spec = torchaudio.transforms.MelSpectrogram(
+        sample_rate=sr, n_fft=2048, hop_length=512, n_mels=40
+    ).to(device)(y)
+    mel_log = torch.log(torch.clamp(mel_spec, 1e-10))
+    
+    # DCT 得到 MFCC
+    mfcc = torch.fft.fft(mel_log, dim=0).real[:13]
+    mfcc_variance = torch.var(mfcc, dim=1)
+    richness = safe_float(mfcc_variance.mean())
+    richness_norm = min(richness / 100, 1.0)
+    
+    # 频谱平衡
+    power = magnitude ** 2
+    low = power[freqs < 250].sum()
+    mid = power[(freqs >= 250) & (freqs < 2000)].sum()
+    high = power[freqs >= 2000].sum()
+    total = low + mid + high
+    
     if total > 0:
-        ratios = [low / total, mid_energy / total, high_energy / total]
-        # 理想比例：低频 0.2-0.4，中频 0.3-0.5，高频 0.1-0.3
-        # 计算与理想比例的偏差
+        ratios = [safe_float(low / total), safe_float(mid / total), safe_float(high / total)]
         ideal = [0.3, 0.4, 0.2]
         deviation = sum(abs(r - i) for r, i in zip(ratios, ideal))
         balance = safe_float(1.0 - min(deviation / 1.0, 1.0))
     else:
         balance = 0.0
-
+    
     scores = {
         "richness": round(richness_norm * 10, 2),
         "balance": round(balance * 10, 2),
     }
-
+    
     score = 0.50 * scores["richness"] + 0.50 * scores["balance"]
     scores["score"] = round(min(score, 10), 2)
-
+    
     return scores
diff --git a/songrate/analyzers/tonality.py b/songrate/analyzers/tonality.py
index de9bfbc..9f92ef8 100644
--- a/songrate/analyzers/tonality.py
+++ b/songrate/analyzers/tonality.py
@@ -1,57 +1,61 @@
-"""调性分析 - 调性清晰度、和声丰富度、转调合理性"""
-import numpy as np
+"""调性分析 - GPU 版本"""
+import torch
 from . import safe_float
 
 
-def analyze_tonality(y, sr):
-    """
-    分析调性维度
-    """
-    import librosa
-
-    # Chroma 特征
-    chroma = librosa.feature.chroma_stft(y=y, sr=sr)
-
-    # 调性清晰度 - chroma 的峰值与均值之比
-    chroma_mean = np.mean(chroma, axis=1)
-    if np.sum(chroma_mean) > 0:
-        peak_ratio = np.max(chroma_mean) / np.mean(chroma_mean)
-        key_clarity = safe_float(min((peak_ratio - 1.0) / 2.0, 1.0))  # 峰值是均值的 2 倍以上满分
-    else:
-        key_clarity = 0.0
-
-    # 和声丰富度 - 活跃音级数量
-    active_notes = np.sum(chroma_mean > np.mean(chroma_mean) * 0.5)
-    harmony = safe_float(min(active_notes / 7.0, 1.0))  # 7 个以上活跃音级满分
-
-    # 转调检测 - 滑动窗口 chroma 变化
-    hop = chroma.shape[1] // 4
-    if hop > 10 and chroma.shape[1] > hop * 2:
-        seg1 = np.mean(chroma[:, :hop], axis=1)
-        seg4 = np.mean(chroma[:, -hop:], axis=1)
-        # 余弦相似度
-        cos_sim = np.dot(seg1, seg4) / (np.linalg.norm(seg1) * np.linalg.norm(seg4) + 1e-10)
-        # 相似度高 = 没转调（稳定），中等 = 有转调（合理），低 = 大转调
+def analyze_tonality(y, sr, stft_result=None):
+    """分析调性维度 (GPU)"""
+    device = y.device
+    
+    if stft_result is None:
+        stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
+    magnitude = stft_result.abs()
+    
+    freqs = torch.fft.fftfreq(2048, 1.0/sr)[:1025]
+    
+    # Chroma 特征 (12 音级)
+    chroma = torch.zeros(12, device=device)
+    note_freqs = 440 * (2 ** (torch.arange(12, device=device, dtype=torch.float32) / 12))
+    for i, nf in enumerate(note_freqs):
+        bin_idx = torch.argmin(torch.abs(freqs - nf)).item()
+        if bin_idx < magnitude.shape[0]:
+            chroma[i] = magnitude[bin_idx].mean()
+    
+    chroma = chroma / torch.clamp(chroma.sum(), 1e-10)
+    
+    # 调性清晰度
+    chroma_mean = chroma
+    peak_ratio = chroma_mean.max() / torch.clamp(chroma_mean.mean(), 1e-10)
+    key_clarity = safe_float(torch.clamp((peak_ratio - 1.0) / 2.0, 0, 1))
+    
+    # 和声丰富度
+    active_notes = (chroma_mean > chroma_mean.mean() * 0.5).sum().item()
+    harmony = safe_float(min(active_notes / 7.0, 1.0))
+    
+    # 转调检测 (简化 - 前后段 chroma 差异)
+    n_frames = magnitude.shape[1]
+    hop = n_frames // 4
+    if hop > 10 and n_frames > hop * 2:
+        seg1 = magnitude[:, :hop].mean(dim=1)
+        seg4 = magnitude[:, -hop:].mean(dim=1)
+        cos_sim = torch.dot(seg1, seg4) / (torch.norm(seg1) * torch.norm(seg4) + 1e-10)
+        cos_sim = safe_float(cos_sim)
         if cos_sim > 0.8:
-            modulation_score = 0.8  # 稳定
+            modulation_score = 0.8
         elif cos_sim > 0.5:
-            modulation_score = 1.0  # 适度转调
+            modulation_score = 1.0
         else:
-            modulation_score = 0.5  # 大转调
+            modulation_score = 0.5
     else:
-        modulation_score = 0.7  # 无法判断，给中等分
-
+        modulation_score = 0.7
+    
     scores = {
         "key_clarity": round(key_clarity * 10, 2),
         "harmony": round(harmony * 10, 2),
         "modulation": round(modulation_score * 10, 2),
     }
-
-    score = (
-        0.40 * scores["key_clarity"] +
-        0.35 * scores["harmony"] +
-        0.25 * scores["modulation"]
-    )
+    
+    score = 0.40 * scores["key_clarity"] + 0.35 * scores["harmony"] + 0.25 * scores["modulation"]
     scores["score"] = round(min(score, 10), 2)
-
+    
     return scores
diff --git a/songrate/evaluator.py b/songrate/evaluator.py
index 8ac4d56..6c0b669 100644
--- a/songrate/evaluator.py
+++ b/songrate/evaluator.py
@@ -1,7 +1,8 @@
-"""主评估逻辑 - 编排所有分析器，返回加权总分"""
+"""主评估逻辑 - GPU 版本，预计算公共特征"""
 import os
-import numpy as np
+import torch
 from .scenes import get_scene_config, SCENES, DIMENSIONS
+from .analyzers import load_audio, get_device
 from .analyzers.rhythm import analyze_rhythm
 from .analyzers.danceability import analyze_danceability
 from .analyzers.energy import analyze_energy
@@ -24,7 +25,7 @@ ANALYZER_MAP = {
 
 def evaluate_song(filepath, scene="pop"):
     """
-    评估一首歌曲
+    评估一首歌曲 (GPU 加速)
     
     参数:
         filepath: 音频文件路径
@@ -34,29 +35,33 @@ def evaluate_song(filepath, scene="pop"):
         dict: {
             "total_score": float,
             "scene": str,
-            "dimensions": [
-                {"key": str, "name": str, "score": float, "weight": float, "weighted": float, "details": dict}
-            ]
+            "device": str,
+            "dimensions": [...]
         }
     """
-    import librosa
-
     if scene not in SCENES:
         return {"error": f"未知场景: {scene}", "available_scenes": list(SCENES.keys())}
 
     if not os.path.exists(filepath):
         return {"error": f"文件不存在: {filepath}"}
 
-    # 加载音频
+    device = get_device()
+
+    # 加载音频到 GPU
     try:
-        y, sr = librosa.load(filepath, sr=22050)
+        y, sr = load_audio(filepath, sr=22050)
     except Exception as e:
         return {"error": f"音频加载失败: {str(e)}"}
 
+    # 预计算 STFT (所有分析器共享)
+    try:
+        stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
+    except Exception as e:
+        return {"error": f"STFT 计算失败: {str(e)}"}
+
     config = get_scene_config(scene)
     weights = config["weights"]
 
-    # 运行各维度分析器（仅运行启用且权重 > 0 的维度）
     dimensions = []
     total_weighted = 0.0
 
@@ -69,7 +74,7 @@ def evaluate_song(filepath, scene="pop"):
             continue
 
         try:
-            result = analyzer(y, sr)
+            result = analyzer(y, sr, stft_result=stft_result)
             dim_score = result.pop("score", 0)
             dim_name = DIMENSIONS.get(dim_key, {}).get("name", dim_key)
             weighted = dim_score * weight
@@ -93,12 +98,17 @@ def evaluate_song(filepath, scene="pop"):
                 "details": {"error": str(e)}
             })
 
-    # 按权重排序
+    # 清理 GPU 内存
+    del y, stft_result
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
     dimensions.sort(key=lambda d: d["weight"], reverse=True)
 
     return {
         "total_score": round(total_weighted, 2),
         "scene": scene,
         "scene_name": config["name"],
+        "device": str(device),
         "dimensions": dimensions
     }