agregar archivos

interfaz.py

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+import gradio as gr
+import torch
+from nemo.collections.asr.models import EncDecSpeakerLabelModel
+import json
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
+
+THRESHOLD = 0.60
+
+model_name = "nvidia/speakerverification_en_titanet_large"
+model = EncDecSpeakerLabelModel.from_pretrained(model_name).to(device)
+
+def create_voice_print(audio):
+    if not audio:
+        return json.dumps({ "error": "no se proporciono un audio"})
+    
+    embs1 = model.get_embedding(audio).squeeze()
+
+    X = embs1 / torch.linalg.norm(embs1)
+    
+    # return X.tolist()
+    return X
+
+def compare_voice_print(X, Y):
+    # Score
+    similarity_score = torch.dot(X, Y) / ((torch.dot(X, X) * torch.dot(Y, Y)) ** 0.5)
+    similarity_score = (similarity_score + 1) / 2
+    return similarity_score.item()
+
+# encontrar como ejecutar la huella de voz
+def find_matches(file, voice_print):
+    matches = []
+    if not file:
+        return json.dumps({"error": "No se proporcionó un archivo JSON"})
+    
+    try:
+
+        json_content = json.load(open(file))
+    except json.JSONDecodeError:
+        return json.dumps({"error": "El archivo JSON no es válido"})
+
+    data = json_content.get("data", [])
+
+    # Convertir a tensor
+    voice_print = torch.tensor(json.loads(voice_print))
+
+    for speaker in data:
+        speaker_voice_print = torch.tensor(json.loads(speaker['voice_print']))
+        # speaker_voice_print = eval(speaker['voice_print'])
+        similarity_score = compare_voice_print(voice_print, speaker_voice_print)
+        print(similarity_score)
+        if similarity_score >= THRESHOLD:
+            matches.append({ "speaker": speaker, "similarity_score": similarity_score })
+    
+    matches.sort(key=lambda match: match['similarity_score'], reverse=True)
+    return matches[:3]
+
+
+voice_print_maker = gr.Interface(
+    fn=create_voice_print,
+    inputs=[gr.Audio(type="filepath")],
+    outputs=gr.JSON(),
+)
+
+voice_prints_loader = gr.Interface(
+    fn=find_matches,
+    inputs=[
+        gr.File(type="filepath", label="Upload JSON file"),
+        gr.TextArea()
+    ],
+    outputs=gr.JSON(),
+)
+
+demo = gr.TabbedInterface([voice_print_maker, voice_prints_loader], ["app", "loader"])
+
+demo.launch()

requirements.txt

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+gradio==4.29.0
+torch==2.4.0
+nemo_toolkit==1.23.0
+huggingface-hub==0.23.2