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2e82ca2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | # CAM++ Speaker Recognition Model (MLX)
Converted from: `iic/speech_campplus_sv_zh_en_16k-common_advanced`
## Model Details
- **Architecture**: CAM++ (Context-Aware Masking++)
- **Framework**: MLX (Apple Silicon optimized)
- **Input**: Mel-spectrogram features (320 dimensions)
- **Output**: Speaker embedding (192 dimensions)
- **Quantized**: False
## Usage
```python
from huggingface_hub import snapshot_download
import mlx.core as mx
import sys
# Download model
model_path = snapshot_download("mlx-community/campp-mlx")
sys.path.append(model_path)
from model import CAMPPModel
import json
# Load model
with open(f"{model_path}/config.json") as f:
config = json.load(f)
model = CAMPPModel(
input_dim=config["input_dim"],
embedding_dim=config["embedding_dim"],
input_channels=config.get("input_channels", 64)
)
weights = mx.load(f"{model_path}/weights.npz")
model.load_weights(weights)
# Use model
audio_features = mx.random.normal((1, 320, 200)) # Your audio features
embedding = model(audio_features)
```
## Performance
- Optimized for Apple Silicon (M1/M2/M3/M4)
- Faster inference than PyTorch on Mac
- Lower memory usage with MLX unified memory
## Original Paper
CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking
https://arxiv.org/abs/2303.00332
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