| # verispoof |
|
|
| Inference package for VeriSpoof, one audio language model for speech deepfake detection, source verification and |
| grounded reasoning. Model card: https://huggingface.co/AustinXiao/VeriSpoof-Base |
|
|
| ```python |
| from verispoof import VeriSpoof |
| |
| model = VeriSpoof.from_pretrained("AustinXiao/VeriSpoof-Base") # or a local folder |
| model.detect("sample.wav") # {'label': 'spoof' | 'bonafide', 'score': log p(spoof) - log p(bona fide)} |
| model.explain("sample.wav") # reasoning lines, decision, spans, per-token probabilities |
| model.same_source("a.wav", "b.wav") # same generator? (also level="acoustic_model" / "vocoder") |
| model.compare("a.wav", "b.wav") # source-verification reasoning |
| model.condition("sample.wav", attr="codec") |
| model.generator("sample.wav", ["XTTS-v2", "Bark"]) |
| ``` |
|
|
| Needs a CUDA GPU (about 12 GB) and `transformers` >= 5.13. |
|
|
| Faster reasoning with vLLM (`pip install "./code[vllm]"`), generation only: |
|
|
| ```python |
| from verispoof.vllm_backend import VeriSpoofVLLM |
| |
| if __name__ == "__main__": # vLLM starts its engine in a separate process |
| m = VeriSpoofVLLM.from_pretrained("AustinXiao/VeriSpoof-Base") |
| m.explain(["a.wav", "b.wav", "c.wav"]) # batched |
| m.compare([("a.wav", "b.wav")]) |
| ``` |
|
|
| ## Files |
|
|
| | | | |
| |---|---| |
| | `verispoof/encoder.py` | audio encoder | |
| | `verispoof/audio_tokenizer.py` | audio tokenizer | |
| | `verispoof/model.py` | encoder → tokenizer → language model, per-token head, prompt layout | |
| | `verispoof/inference.py` | the `VeriSpoof` API: candidate scoring and greedy reasoning | |
| | `verispoof/vllm_backend.py` | `VeriSpoofVLLM`: reasoning with vLLM from prompt embeddings | |
|
|
