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--- |
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base_model: |
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- gru-audio-binary |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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language: |
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- zh |
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metrics: |
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- accuracy |
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pipeline_tag: audio-classification |
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tags: |
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- speaker_dialect_classification |
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library_name: transformers |
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--- |
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# shanghai-binary |
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Binary classifier: **Shanghai** vs **Not-Shanghai** (audio FBANK → GRU → MLP). |
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## Files |
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- `model.safetensors` — PyTorch weights (safetensors) |
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- `config.json` — model architecture |
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- `preprocessor_config.json` — audio feature extraction settings |
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- `label_mapping.json` — index → label |
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## Inference (PyTorch) |
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```python |
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import torch, json, numpy as np, librosa |
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from safetensors.torch import load_file as load_safetensors |
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# Load config |
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import json, os |
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model_dir = "./hf/models/shanghai-binary" |
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cfg = json.load(open(os.path.join(model_dir, "config.json"))) |
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pp = json.load(open(os.path.join(model_dir, "preprocessor_config.json"))) |
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lm = json.load(open(os.path.join(model_dir, "label_mapping.json"))) |
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# Define the model class you trained (LanNetBinary) |
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# (Same as in your training notebook) |
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class LanNetBinary(torch.nn.Module): |
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def __init__(self, input_dim=40, hidden_dim=512, num_layers=2): |
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super().__init__() |
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self.gru = torch.nn.GRU(input_dim, hidden_dim, num_layers=num_layers, batch_first=True) |
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self.linear2 = torch.nn.Linear(hidden_dim, 192) |
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self.linear3 = torch.nn.Linear(192, 2) |
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def forward(self, x): |
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out, _ = self.gru(x) |
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last = out[:, -1, :] |
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x = self.linear2(last) |
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x = self.linear3(x) |
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return x |
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# Load weights |
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model = LanNetBinary(cfg["input_dim"], cfg["hidden_dim"], cfg["num_layers"]) |
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sd = load_safetensors(os.path.join(model_dir, "model.safetensors")) |
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model.load_state_dict(sd, strict=True) |
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model.eval() |
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# Feature extraction should match preprocessor_config.json |
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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"]): |
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mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length, power=2.0) |
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fbanks = librosa.power_to_db(mel).T |
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T = fbanks.shape[0] |
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if T < max_len: |
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import numpy as np |
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fbanks = np.pad(fbanks, ((0, max_len - T), (0, 0)), mode="constant") |
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else: |
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fbanks = fbanks[:max_len, :] |
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return torch.tensor(fbanks, dtype=torch.float32).unsqueeze(0) # (1, T, F) |
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# Example: predict from a waveform array "y" at 16kHz |
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# y, _ = librosa.load("example.wav", sr=pp["sampling_rate"]) |
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# x = fbanks_from_array(y) |
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# with torch.no_grad(): |
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# logits = model(x) |
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# pred = int(torch.argmax(logits, dim=1)) |
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# print(lm[str(pred)]) |
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``` |
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## References |
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- Model based from [https://github.com/Colt1990/chinese-dialect-recognition/tree/master](https://github.com/Colt1990/chinese-dialect-recognition/tree/master) |
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