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README.md

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----
-license: mit
----
+# shanghai-binary
+
+Binary classifier: **Shanghai** vs **Not-Shanghai** (audio FBANK → GRU → MLP).
+
+## Files
+- `model.safetensors` — PyTorch weights (safetensors)
+- `config.json` — model architecture
+- `preprocessor_config.json` — audio feature extraction settings
+- `label_mapping.json` — index → label
+
+## Inference (PyTorch)
+```python
+import torch, json, numpy as np, librosa
+from safetensors.torch import load_file as load_safetensors
+
+# Load config
+import json, os
+model_dir = "./hf/models/shanghai-binary"
+cfg = json.load(open(os.path.join(model_dir, "config.json")))
+pp  = json.load(open(os.path.join(model_dir, "preprocessor_config.json")))
+lm  = json.load(open(os.path.join(model_dir, "label_mapping.json")))
+
+# Define the model class you trained (LanNetBinary)
+# (Same as in your training notebook)
+class LanNetBinary(torch.nn.Module):
+    def __init__(self, input_dim=40, hidden_dim=512, num_layers=2):
+        super().__init__()
+        self.gru = torch.nn.GRU(input_dim, hidden_dim, num_layers=num_layers, batch_first=True)
+        self.linear2 = torch.nn.Linear(hidden_dim, 192)
+        self.linear3 = torch.nn.Linear(192, 2)
+    def forward(self, x):
+        out, _ = self.gru(x)
+        last = out[:, -1, :]
+        x = self.linear2(last)
+        x = self.linear3(x)
+        return x
+
+# Load weights
+model = LanNetBinary(cfg["input_dim"], cfg["hidden_dim"], cfg["num_layers"])
+sd = load_safetensors(os.path.join(model_dir, "model.safetensors"))
+model.load_state_dict(sd, strict=True)
+model.eval()
+
+# Feature extraction should match preprocessor_config.json
+def fbanks_from_array(y, sr=pp["sampling_rate"], n_mels=pp["n_mels"], n_fft=pp["n_fft"], hop_length=pp["hop_length"], max_len=pp["max_len_frames"]):
+    mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length, power=2.0)
+    fbanks = librosa.power_to_db(mel).T
+    T = fbanks.shape[0]
+    if T < max_len:
+        import numpy as np
+        fbanks = np.pad(fbanks, ((0, max_len - T), (0, 0)), mode="constant")
+    else:
+        fbanks = fbanks[:max_len, :]
+    return torch.tensor(fbanks, dtype=torch.float32).unsqueeze(0)  # (1, T, F)
+
+# Example: predict from a waveform array "y" at 16kHz
+# y, _ = librosa.load("example.wav", sr=pp["sampling_rate"])
+# x = fbanks_from_array(y)
+# with torch.no_grad():
+#     logits = model(x)
+#     pred = int(torch.argmax(logits, dim=1))
+#     print(lm[str(pred)])

config.json

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+{
+  "_name_or_path": "shanghai-binary",
+  "model_type": "gru-audio-binary",
+  "input_dim": 40,
+  "hidden_dim": 512,
+  "num_layers": 2,
+  "num_labels": 2,
+  "classifier_dims": [
+    192,
+    2
+  ],
+  "pooling": "last_timestep"
+}

label_mapping.json

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+{
+  "0": "not-shanghai",
+  "1": "shanghai"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:03a883bf09cf68c11a4a4f5076ad7d827f976fa6475d768101b747a23a59e087
+size 10104032

preprocessor_config.json

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+{
+  "feature_type": "fbank",
+  "sampling_rate": 16000,
+  "n_mels": 40,
+  "n_fft": 400,
+  "hop_length": 160,
+  "max_len_frames": 200,
+  "log_db": true,
+  "mono": true
+}