Create app.py

app.py

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+import torch
+import gradio as gr
+from torchaudio.sox_effects import apply_effects_file
+from transformers import AutoFeatureExtractor, AutoModelForAudioXVector
+
+device = "cuda" if toch.cuda.is_available() else "cpu"
+EFFECTS = [
+    ['remix', '-'], # pour fusionner tous les canaux
+    ["channels", "1"], #channel-->mono
+    ["rate", "16000"], # rééchantillonner à 16000 Hz
+    ["gain", "-1.0"], #Atténuation -1 dB
+    ["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
+    # ['pad', '0', '1.5'], # pour ajouter 1,5 seconde à la fin
+    ['trim', '0', '10'], # obtenir les 10 premières secondes
+]
+
+model_name = "microsoft/unispeech-sat-base-plus-sv"
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)
+
+#Réglage de la valeur seuil
+SEUIL = 0,85
+
+cosine_similarity = torch.nn.CosineSimilarity(dim=-1)
+
+def similarity_fn(path1, path2):
+  if not (path1 and path2):
+    return 'ERROR: Please record audio for *both* speakers!'
+  #Applying the effects to both the audio input files
+  wav1, _ = apply_effects_file(path1, EFFECTS)
+  wav2, _ = apply_effects_file(path2,EFFECTS)
+  #Extracting features
+  input1 = feature_extractor(wav1.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
+  input2 = feature_extractor(wav2.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
+  with torch.no_grad():
+    emb1 = model(input1).embeddings
+    emb2 = model(input2).embeddings
+  emb1 = torch.nn.functional.normalize(emb1, dim=-1).to(device)
+  emb2 = torch.nn.functional.normalize(emb2, dim=-1).to(device)
+  similarity = cosine_similarity(emb1, emb2).numpy()[0]
+  if similarity>= THRESHOLD:
+    return f"Similarity score is {similarity :.0%}. Audio belongs to the same person "
+  elif similarity< THRESHOLD:
+    return f"Similarity score is {similarity:.0%}. Audio doesn't belong to the same person.Authentication failed!"
+
+inputs = [
+    gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #1"),
+    gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #2"),
+]
+
+outputs = gr.outputs.Textbox(label="Output Text")
+description = (
+    "This app evaluates whether the given audio speech inputs belong to the same individual based on Cosine Similarity score. "
+)
+
+interface = gr.Interface(
+    fn=similarity_fn,
+    inputs=inputs,
+    outputs=outputs,
+    title="Voice Authentication with UniSpeech-SAT + X-Vectors",
+    description=description,
+    layout="horizontal",
+    theme="grass",
+    allow_flagging=False,
+    live=False,
+    examples=[
+        ["cate_blanch.mp3", "cate_blanch_2.mp3"],
+        ["cate_blanch.mp3", "denzel_washington.mp3"]
+    ]
+)
+
+interface.launch(enable_queue=True)