Initial commit with RVC demo

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
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added_IVF403_Flat_nprobe_1_yebama_v2.index

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app.py

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+import gradio as gr
+import torch
+import librosa
+import numpy as np
+from huggingface_hub import hf_hub_download
+import zipfile
+import os
+
+# Descargar el modelo desde tu otra cuenta de Hugging Face
+def load_model():
+    # Descargar archivos del modelo (ajusta los nombres según tu repositorio)
+    model_path = hf_hub_download(repo_id="gitgato/yebama", filename="yebama_e200_s3200.pth")
+    index_path = hf_hub_download(repo_id="gitgato/yebama", filename="IVF403_Flat_nprobe_1_yebama_v2.index")
+    config_path = hf_hub_download(repo_id="gitgato/yebama", filename="config.json")
+    
+    # Aquí iría el código para cargar el modelo RVC (depende de tu implementación)
+    # Ejemplo simplificado:
+    # from rvc_inference import load_rvc_model
+    # model = load_rvc_model(model_path, config_path, index_path)
+    # return model
+
+def process_audio(audio_file):
+    # Cargar el audio (ejemplo con librosa)
+    audio, sr = librosa.load(audio_file, sr=44100)
+    
+    # Aquí iría la lógica de inferencia con RVC
+    # processed_audio = model.infer(audio)
+    
+    # Simulando un resultado (reemplaza con tu modelo real)
+    processed_audio = audio  # Esto es un placeholder
+    
+    return sr, processed_audio
+
+# Interfaz Gradio
+iface = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(type="filepath", label="Sube tu audio"),
+    outputs=gr.Audio(label="Audio convertido"),
+    title="Demo de Yebama RVC",
+    description="Convierte tu voz con el modelo Yebama RVC."
+)
+
+iface.launch()

config.json

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+{
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+      "normalized": false,
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+      "normalized": true,
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 600,
+  "normalize": false,
+  "pad_token": "<pad>",
+  "processor_class": "SpeechT5Processor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "SpeechT5Tokenizer",
+  "unk_token": "<unk>"
+}

requirements.txt

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+gradio>=3.0
+torch>=1.10
+librosa>=0.9.0
+huggingface_hub
+numpy

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yebama.zip

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yebama_e200_s3200.pth

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