Align music crop sampling with rhythmic grid candidates

Constraint: Music queries often begin near stable pulse locations, but beat tracking can fail on sparse or synthetic signals and must degrade safely
Rejected: Depend on beat tracking alone for all rhythmic sampling | too brittle when beat extraction is weak or absent
Confidence: high
Scope-risk: moderate
Directive: Keep beat_aware as a lightweight candidate generator with onset fallback; future chorus/repeated-section logic should compose with beat-aware rather than bypass it
Tested: /usr/local/miniconda3/bin/python -m py_compile acr-engine/src/data/dataset.py acr-engine/src/data/manifest_tools.py acr-engine/train.py acr-engine/src/data/external_adapters.py; synthetic_v2 dry-run with --segment-strategy beat_aware; handcrafted 20s pulse-track fixture with beat_aware and hybrid offset checks
Not-tested: Full retraining/evaluation impact on open/internal datasets using beat_aware end-to-end

@@ -61,6 +61,40 @@ def compute_candidate_offsets(
             offsets.append(start)
             offsets.append(start)
         return sorted(set(offsets[: min(8, len(offsets))]))
         return sorted(set(offsets[: min(8, len(offsets))]))
 
 
+    if strategy == "beat_aware":
+        try:
+            tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr, hop_length=512, units="frames")
+            beat_samples = librosa.frames_to_samples(beat_frames, hop_length=512)
+        except Exception:
+            beat_samples = np.array([], dtype=int)
+        if beat_samples.size == 0:
+            try:
+                onset_frames = librosa.onset.onset_detect(y=y, sr=sr, hop_length=512, units="frames")
+                onset_samples = librosa.frames_to_samples(onset_frames, hop_length=512)
+                if onset_samples.size >= 2:
+                    diffs = np.diff(onset_samples)
+                    median_step = int(np.median(diffs)) if diffs.size else 0
+                    if median_step > 0:
+                        approx = [int(onset_samples[0])]
+                        while approx[-1] + median_step < len(y):
+                            approx.append(approx[-1] + median_step)
+                        beat_samples = np.array(approx, dtype=int)
+                elif onset_samples.size == 1:
+                    beat_samples = onset_samples
+            except Exception:
+                beat_samples = np.array([], dtype=int)
+        if beat_samples.size == 0:
+            return []
+        offsets = []
+        max_start = max(len(y) - segment_len, 0)
+        for beat in beat_samples.tolist():
+            start = max(0, min(int(beat), max_start))
+            offsets.append(start)
+        if not offsets:
+            return []
+        step = max(1, len(offsets) // 8)
+        return sorted(set(offsets[::step][:8]))
+
     return []
     return []
 
 
 
 
@@ -152,7 +186,7 @@ class ACRDataset(Dataset):
 
 
         if self.segment_strategy == "hybrid":
         if self.segment_strategy == "hybrid":
             candidate_pool: List[int] = []
             candidate_pool: List[int] = []
-            for strategy in ("high_energy", "onset_aware", "silence_aware"):
+            for strategy in ("beat_aware", "high_energy", "onset_aware", "silence_aware"):
                 candidate_pool.extend(
                 candidate_pool.extend(
                     compute_candidate_offsets(
                     compute_candidate_offsets(
                         y=y,
                         y=y,
@@ -331,7 +365,7 @@ class SongPairDataset(Dataset):
                     offset = min(random.choice(direct_candidates) / self.sr, max_offset)
                     offset = min(random.choice(direct_candidates) / self.sr, max_offset)
                 elif self.segment_strategy == "hybrid":
                 elif self.segment_strategy == "hybrid":
                     candidate_pool: List[int] = []
                     candidate_pool: List[int] = []
-                    for strategy in ("high_energy", "onset_aware", "silence_aware"):
+                    for strategy in ("beat_aware", "high_energy", "onset_aware", "silence_aware"):
                         candidate_pool.extend(
                         candidate_pool.extend(
                             compute_candidate_offsets(
                             compute_candidate_offsets(
                                 y=full_y,
                                 y=full_y,

@@ -516,7 +516,7 @@ def main():
     p.add_argument("--eval-ratio", type=float, default=0.2)
     p.add_argument("--eval-ratio", type=float, default=0.2)
     p.add_argument("--query-duration", type=float, default=8.0)
     p.add_argument("--query-duration", type=float, default=8.0)
     p.add_argument("--query-stride", type=float, default=None)
     p.add_argument("--query-stride", type=float, default=None)
-    p.add_argument("--query-strategy", choices=["random", "sliding", "silence_aware", "high_energy", "onset_aware", "hybrid"], default="random")
+    p.add_argument("--query-strategy", choices=["random", "sliding", "silence_aware", "high_energy", "onset_aware", "beat_aware", "hybrid"], default="random")
     p.add_argument("--silence-top-db", type=int, default=30)
     p.add_argument("--silence-top-db", type=int, default=30)
     p.add_argument("--seed", type=int, default=42)
     p.add_argument("--seed", type=int, default=42)
 
 
@@ -548,8 +548,8 @@ def main():
     p.add_argument("--eval-ratio", type=float, default=0.2)
     p.add_argument("--eval-ratio", type=float, default=0.2)
     p.add_argument("--query-duration", type=float, default=8.0)
     p.add_argument("--query-duration", type=float, default=8.0)
     p.add_argument("--query-stride", type=float, default=None)
     p.add_argument("--query-stride", type=float, default=None)
-    p.add_argument("--query-strategy", choices=["random", "sliding", "silence_aware", "high_energy", "onset_aware", "hybrid"], default="random")
-    p.add_argument("--segment-strategy", choices=["random", "silence_aware", "high_energy", "onset_aware", "hybrid"], default="random")
+    p.add_argument("--query-strategy", choices=["random", "sliding", "silence_aware", "high_energy", "onset_aware", "beat_aware", "hybrid"], default="random")
+    p.add_argument("--segment-strategy", choices=["random", "silence_aware", "high_energy", "onset_aware", "beat_aware", "hybrid"], default="random")
     p.add_argument("--silence-top-db", type=int, default=30)
     p.add_argument("--silence-top-db", type=int, default=30)
     p.add_argument("--index-checkpoint-every-refs", type=int, default=100)
     p.add_argument("--index-checkpoint-every-refs", type=int, default=100)
     p.add_argument("--seed", type=int, default=42)
     p.add_argument("--seed", type=int, default=42)

@@ -117,14 +117,14 @@ def build_train_eval_from_audio_dir(
 
 
         if duration >= query_duration:
         if duration >= query_duration:
             strategy_offsets = []
             strategy_offsets = []
-            if query_strategy in {"silence_aware", "high_energy", "onset_aware"}:
+            if query_strategy in {"silence_aware", "high_energy", "onset_aware", "beat_aware"}:
                 strategy_offsets = compute_strategy_offsets(path, duration, query_strategy)
                 strategy_offsets = compute_strategy_offsets(path, duration, query_strategy)
             elif query_strategy == "hybrid":
             elif query_strategy == "hybrid":
-                for strategy in ("high_energy", "onset_aware", "silence_aware"):
+                for strategy in ("beat_aware", "high_energy", "onset_aware", "silence_aware"):
                     strategy_offsets.extend(compute_strategy_offsets(path, duration, strategy))
                     strategy_offsets.extend(compute_strategy_offsets(path, duration, strategy))
                 strategy_offsets = sorted(set(strategy_offsets))
                 strategy_offsets = sorted(set(strategy_offsets))
 
 
-            if query_strategy in {"silence_aware", "high_energy", "onset_aware"} and strategy_offsets:
+            if query_strategy in {"silence_aware", "high_energy", "onset_aware", "beat_aware"} and strategy_offsets:
                 offsets = strategy_offsets
                 offsets = strategy_offsets
             elif query_strategy == "hybrid" and strategy_offsets:
             elif query_strategy == "hybrid" and strategy_offsets:
                 if query_stride and query_stride > 0:
                 if query_stride and query_stride > 0:
@@ -277,7 +277,7 @@ def main():
     p.add_argument("--eval-ratio", type=float, default=0.2)
     p.add_argument("--eval-ratio", type=float, default=0.2)
     p.add_argument("--query-duration", type=float, default=8.0)
     p.add_argument("--query-duration", type=float, default=8.0)
     p.add_argument("--query-stride", type=float, default=None)
     p.add_argument("--query-stride", type=float, default=None)
-    p.add_argument("--query-strategy", choices=["random", "sliding", "silence_aware", "high_energy", "onset_aware", "hybrid"], default="random")
+    p.add_argument("--query-strategy", choices=["random", "sliding", "silence_aware", "high_energy", "onset_aware", "beat_aware", "hybrid"], default="random")
     p.add_argument("--silence-top-db", type=int, default=30)
     p.add_argument("--silence-top-db", type=int, default=30)
     p.add_argument("--seed", type=int, default=42)
     p.add_argument("--seed", type=int, default=42)
 
 

@@ -125,7 +125,7 @@ def main():
     parser.add_argument("--epochs", type=int, default=None)
     parser.add_argument("--epochs", type=int, default=None)
     parser.add_argument("--batch-size", type=int, default=None)
     parser.add_argument("--batch-size", type=int, default=None)
     parser.add_argument("--lr", type=float, default=None)
     parser.add_argument("--lr", type=float, default=None)
-    parser.add_argument("--segment-strategy", choices=["random", "silence_aware", "high_energy", "onset_aware", "hybrid"], default="random")
+    parser.add_argument("--segment-strategy", choices=["random", "silence_aware", "high_energy", "onset_aware", "beat_aware", "hybrid"], default="random")
     parser.add_argument("--silence-top-db", type=int, default=30)
     parser.add_argument("--silence-top-db", type=int, default=30)
     parser.add_argument("--dry-run", action="store_true")
     parser.add_argument("--dry-run", action="store_true")
     args = parser.parse_args()
     args = parser.parse_args()

@@ -5569,3 +5569,52 @@
   - beat-aware
   - beat-aware
   - chorus-aware
   - chorus-aware
   - repeated-section-aware
   - repeated-section-aware
+
+### Stage: beat-aware music segmentation
+
+完成项：
+- 在 `acr-engine/src/data/dataset.py` 新增：
+  - `beat_aware` 候选切片策略
+- 在 `acr-engine/src/data/manifest_tools.py` 新增：
+  - `--query-strategy beat_aware`
+- 在 `train.py` 与 `external_adapters.py` 暴露：
+  - `beat_aware` 选项
+- 为 `beat_aware` 增加容错：
+  - 优先使用 `librosa.beat.beat_track`
+  - 若 beat 检测失败，则回退到 onset 间隔估计生成近似节拍点
+- 将 `hybrid` 扩展为优先复用：
+  - `beat_aware`
+  - `high_energy`
+  - `onset_aware`
+  - `silence_aware`
+
+验证结果：
+- 编译验证：
+  - `/usr/local/miniconda3/bin/python -m py_compile src/data/dataset.py src/data/manifest_tools.py train.py src/data/external_adapters.py`
+- 人造节拍音频验证：
+  - 构造 `20s` 音频
+  - `4s-18s` 区间每 `0.5s` 注入一次脉冲（约 120 BPM）
+  - 再叠加轻微 tonal bed
+- 直接 beat 候选结果：
+  - `DIRECT_BEAT_CANDIDATES_SEC`：
+    - `4.032, 5.952, 7.872, 9.792, 11.712, 13.632, 15.0`
+- query 生成结果：
+  - `BEAT_QUERY_OFFSETS`：
+    - `4.032, 7.872, 9.792, 11.712, 13.632, 15.0`
+  - `HYBRID_QUERY_OFFSETS`：
+    - `3.968, 4.032, 4.064, 4.544, 5.0, 5.536, 6.016, 6.048, 7.872, 9.591, 9.792, 10.0`
+- 训练侧偏移验证：
+  - `TRAIN_BEAT_AWARE_OFFSETS`：
+    - `13.632, 4.032, 4.032, 13.632, 7.872, 5.952`
+  - `TRAIN_HYBRID_OFFSETS`：
+    - `2.5, 5.536, 4.064, 12.5, 7.872, 4.032`
+  - 说明 beat-aware 已明显偏向规则拍点，hybrid 也已吸收 beat-aware 候选
+- dry-run 验证：
+  - `train.py --data data/synthetic_v2 --dry-run --segment-strategy beat_aware`
+  - forward/backward 成功，`Embedding shape: torch.Size([64, 192])`
+
+结论：
+- 当前项目的音乐感知切片已经进一步从“高能/起音”扩展到“规则拍点”
+- 下一步可继续叠加：
+  - repeated-section-aware
+  - chorus-like candidate mining

@@ -356,12 +356,13 @@ flowchart TD
 | `silence_aware` | 训练 query / 外部 query 生成 | 优先避开静音，落到真正有音乐内容的片段 | 是 |
 | `silence_aware` | 训练 query / 外部 query 生成 | 优先避开静音，落到真正有音乐内容的片段 | 是 |
 | `high_energy` | 训练 query / 外部 query 生成 | 优先抽取 RMS 高能区，更接近副歌/主唱/强节奏段 | 是 |
 | `high_energy` | 训练 query / 外部 query 生成 | 优先抽取 RMS 高能区，更接近副歌/主唱/强节奏段 | 是 |
 | `onset_aware` | 训练 query / 外部 query 生成 | 优先靠近起音事件，减少截到拖尾/空拍 | 是 |
 | `onset_aware` | 训练 query / 外部 query 生成 | 优先靠近起音事件，减少截到拖尾/空拍 | 是 |
+| `beat_aware` | 训练 query / 外部 query 生成 | 优先靠近节拍点，适合强节奏流行/电子/舞曲等 | 是 |
 | `hybrid` | 训练 query / 外部 query 生成 | 混合 silence-aware + random，兼顾稳定性与泛化 | 是 |
 | `hybrid` | 训练 query / 外部 query 生成 | 混合 silence-aware + random，兼顾稳定性与泛化 | 是 |
 
 
 推荐理解：
 推荐理解：
 
 
 1. **训练不是全部随机切**  
 1. **训练不是全部随机切**  
-   当前训练集可用 `random / silence_aware / high_energy / onset_aware / hybrid`
+   当前训练集可用 `random / silence_aware / high_energy / onset_aware / beat_aware / hybrid`
 2. **reference 建库不是随机切**  
 2. **reference 建库不是随机切**  
    建库仍然是固定滑窗
    建库仍然是固定滑窗
 3. **外部数据 query 生成也不是只能随机切**  
 3. **外部数据 query 生成也不是只能随机切**  
@@ -388,6 +389,7 @@ flowchart TD
 - 已知原始音频静音很多：`silence_aware`
 - 已知原始音频静音很多：`silence_aware`
 - 更想贴近副歌/强节奏：`high_energy`
 - 更想贴近副歌/强节奏：`high_energy`
 - 更想贴近短音起点/打点：`onset_aware`
 - 更想贴近短音起点/打点：`onset_aware`
+- 更想贴近稳定节拍网格：`beat_aware`
 
 
 ### 外部数据 query 生成推荐
 ### 外部数据 query 生成推荐
 
 
@@ -409,6 +411,7 @@ flowchart TD
 | 录音静音头尾很多 | `silence_aware` |
 | 录音静音头尾很多 | `silence_aware` |
 | 更想贴近副歌/主段 | `high_energy` |
 | 更想贴近副歌/主段 | `high_energy` |
 | 更想贴近打点/起唱点 | `onset_aware` |
 | 更想贴近打点/起唱点 | `onset_aware` |
+| 更想贴近规则拍点/律动骨架 | `beat_aware` |
 | 既要音乐感知，又要保留泛化 | `hybrid` |
 | 既要音乐感知，又要保留泛化 | `hybrid` |
 
 
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