README.md

---
license: mit
base_model:
- moonshotai/Kimi-Audio-7B-Instruct
pipeline_tag: feature-extraction
---
# Kimi-Audio Whisper Encoder

Kimi-Audioでファインチューニングされたwhisperエンコーダー。音声から連続的な音響特徴量を抽出。

## Model Info

- **Base**: whisper-large-v3
- **Hidden Size**: 1280
- **Original**: [moonshotai/Kimi-Audio-7B-Instruct](https://huggingface.co/moonshotai/Kimi-Audio-7B-Instruct)

## Installation

```bash
pip install transformers librosa torch
```

## Usage

### Using Transformers (Recommended)

```python
import torch
import librosa
from transformers import WhisperModel

# Load model
model = WhisperModel.from_pretrained("Atotti/Kimi-Audio-Whisper-Encoder")
model = model.encoder.to("cuda", dtype=torch.bfloat16)
model.eval()

# Load audio
audio, sr = librosa.load("audio.wav", sr=16000)

# Extract features using Whisper's feature extractor
from transformers import WhisperFeatureExtractor
feature_extractor = WhisperFeatureExtractor.from_pretrained("Atotti/Kimi-Audio-Whisper-Encoder")
inputs = feature_extractor(audio, sampling_rate=16000, return_tensors="pt")
input_features = inputs.input_features.to("cuda", dtype=torch.bfloat16)

# Get encoder output
with torch.no_grad():
    encoder_output = model(input_features)
    features = encoder_output.last_hidden_state  # [1, T, 1280]

print(f"Features shape: {features.shape}")
```

### Pooled Features

```python
# Mean pooling for utterance-level embedding
pooled = features.mean(dim=1)  # [1, 1280]
```

## Output

- **Sequential features**: `[batch, time_steps, 1280]` - 時系列特徴量
- **Pooled features**: `[batch, 1280]` - 発話レベル特徴量

## License

See [moonshotai/Kimi-Audio-7B-Instruct](https://huggingface.co/moonshotai/Kimi-Audio-7B-Instruct) for license information.