|
import gradio as gr |
|
import torch |
|
import torchaudio |
|
from transformers import AutoProcessor, AutoModelForAudioClassification |
|
from transformers import AutoFeatureExtractor |
|
|
|
|
|
feature_extractor = AutoFeatureExtractor.from_pretrained("ThomasR/facebook_wav2vec2-large_October_03_2023_05h34PM") |
|
model = AutoModelForAudioClassification.from_pretrained("ThomasR/facebook_wav2vec2-large_October_03_2023_05h34PM") |
|
|
|
label2id={'fake':0, 'real':1} |
|
id2label = {v:k for k,v in label2id.items()} |
|
|
|
def predict(audio_path): |
|
wavform, sample_rate = sf.read(audio_path) |
|
|
|
inputs = feature_extractor( |
|
wavform, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt", max_length=16000, truncation=True, padding=True |
|
) |
|
|
|
with torch.no_grad(): |
|
logits = model(**inputs).logits |
|
|
|
probabilities = torch.sigmoid(logits[0]) |
|
|
|
labels = (probabilities > 0.5).long() |
|
|
|
pred_probs = list(probabilities.tolist()) |
|
|
|
idx = pred_probs.index(max(pred_probs)) |
|
|
|
LABELS=list(id2label.values()) |
|
|
|
max_label = LABELS[idx] |
|
results = {LABELS[i]: round(float(pred_probs[i]),4) for i in range(len(LABELS))} |
|
|
|
return results |
|
|
|
demo = gr.Interface(fn=predict, |
|
inputs=gr.Audio(type="filepath"), |
|
outputs="label", |
|
cache_examples=False |
|
) |
|
|
|
demo.launch(debug=False) |
