Update rvc.py

rvc.py

@@ -1,10 +1,61 @@
 from original import *
 import shutil, glob
+from infer_rvc_python import BaseLoader
 from easyfuncs import download_from_url, CachedModels
 os.makedirs("dataset",exist_ok=True)
 model_library = CachedModels()
 
-with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue="zinc")) as app:
+
+
+
+# Initialize the converter
+converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None)
+
+import gradio as gr
+import os
+from infer_rvc_python import BaseLoader
+
+# Initialize the converter
+converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None)
+
+def apply_conversion(audio_files, file_model, file_index, pitch_lvl, pitch_algo):
+    converter.apply_conf(
+        tag=file_model,
+        file_model=file_model,
+        pitch_algo=pitch_algo,
+        pitch_lvl=int(pitch_lvl),  # pitch_lvl should be an integer
+        file_index=file_index,
+        index_influence=0.66,
+        respiration_median_filtering=3,
+        envelope_ratio=0.25,
+        consonant_breath_protection=0.33
+    )
+    
+    speakers_list = [file_model]  # It should be a list if multiple speakers are possible
+
+    result = converter(
+        audio_files,
+        speakers_list,
+        overwrite=False,
+        parallel_workers=4
+    )
+    
+    output_path = "output_audio.wav"
+    # Assuming `result` is an array of audio data, save it to a file
+    result[0].export(output_path, format="wav")  # This is an example, modify as needed for your data type
+    
+    return output_path
+
+
+
+
+
+
+
+
+
+
+with gr.Blocks(title="Easy 🔊 GUI",theme="Hev832/Applio") as app:
     with gr.Row():
         gr.HTML("<img  src='file/a.png' alt='image'>")
     with gr.Tabs():
@@ -26,6 +77,21 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue=
                     value=0
                 )
                 but0 = gr.Button(value="Convert", variant="primary")
+                with gr.TabItem("Inference v2!"):
+                    audio_files_input = gr.Audio(label="your audios")
+                    file_model_input = gr.Dropdown(label="Model Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True)
+                    file_index_input = gr.Dropdown(label="Change Index",choices=sorted(index_paths),interactive=True,value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else '')
+                    pitch_lvl_input = gr.Number(label="Pitch",value=0)
+                    pitch_algo_input = gr.Dropdown(["pm", "harvest", "crepe", "rmvpe", "rmvpe+"], label="Pitch Algorithm")
+                    submit_button = gr.Button("Convert Audio")
+                    output_Audio= gr.Audio(label="Conversion Result")
+                    submit_button.click(
+                        apply_conversion,
+                        inputs=[audio_files_input, file_model_input, file_index_input, pitch_lvl_input, pitch_algo_input],
+                        outputs=output_audio
+                    )
+
+    
             with gr.Row():
                 with gr.Column():
                     with gr.Row():
@@ -198,7 +264,7 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue=
                         choices=["40k", "32k"],
                         value="32k",
                         interactive=True,
-                        visible=False
+                        visible=true
                     )
                     if_f0_3 = gr.Radio(
                         label="Will your model be used for singing? If not, you can ignore this.",
@@ -212,14 +278,14 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue=
                         choices=["v1", "v2"],
                         value="v2",
                         interactive=True,
-                        visible=False,
+                        visible=False, # this is default
                     )
                     dataset_folder = gr.Textbox(
                         label="dataset folder", value='dataset'
                     )
                     easy_uploader = gr.Files(label="Drop your audio files here",file_types=['audio'])
-                    but1 = gr.Button("1. Process", variant="primary")
-                    info1 = gr.Textbox(label="Information", value="",visible=True)
+                    
+                    #info1 = gr.Textbox(label="Information", value="",visible=True)
                     easy_uploader.upload(inputs=[dataset_folder],outputs=[],fn=lambda folder:os.makedirs(folder,exist_ok=True))
                     easy_uploader.upload(
                         fn=lambda files,folder: [shutil.copy2(f.name,os.path.join(folder,os.path.split(f.name)[1])) for f in files] if folder != "" else gr.Warning('Please enter a folder name for your dataset'),
@@ -243,12 +309,11 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue=
                         interactive=True,
                         visible=False
                     )
-                    but1.click(
-                        preprocess_dataset,
-                        [dataset_folder, training_name, sr2, np7],
-                        [info1],
-                        api_name="train_preprocess",
-                    ) 
+                    f0method8.change(
+                        fn=change_f0_method,
+                        inputs=[f0method8],
+                        outputs=[gpus_rmvpe],
+                    )
                 with gr.Column():
                     f0method8 = gr.Radio(
                         label="F0 extraction method",
@@ -262,27 +327,6 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue=
                         interactive=True,
                         visible=F0GPUVisible,
                     )
-                    but2 = gr.Button("2. Extract Features", variant="primary")
-                    info2 = gr.Textbox(label="Information", value="", max_lines=8)
-                    f0method8.change(
-                        fn=change_f0_method,
-                        inputs=[f0method8],
-                        outputs=[gpus_rmvpe],
-                    )
-                    but2.click(
-                        extract_f0_feature,
-                        [
-                            gpus6,
-                            np7,
-                            f0method8,
-                            if_f0_3,
-                            training_name,
-                            version19,
-                            gpus_rmvpe,
-                        ],
-                        [info2],
-                        api_name="train_extract_f0_feature",
-                    )
                 with gr.Column():
                     total_epoch11 = gr.Slider(
                         minimum=2,
@@ -292,6 +336,8 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue=
                         value=150,
                         interactive=True,
                     )
+                    but1 = gr.Button("1. Process", variant="primary")
+                    but2 = gr.Button("2. Extract Features", variant="primary")
                     but4 = gr.Button("3. Train Index", variant="primary")
                     but3 = gr.Button("4. Train Model", variant="primary")
                     info3 = gr.Textbox(label="Information", value="", max_lines=10)
@@ -380,6 +426,26 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue=
                         )
                     with gr.Row():
                         but5 = gr.Button("1 Click Training", variant="primary", visible=False)
+                        but1.click(
+                            preprocess_dataset,
+                            [dataset_folder, training_name, sr2, np7],
+                            [info3],
+                            api_name="train_preprocess",
+                        )
+                        but2.click(
+                            extract_f0_feature,
+                            [
+                                gpus6,
+                                np7,
+                                f0method8,
+                                if_f0_3,
+                                training_name,
+                                version19,
+                                gpus_rmvpe,
+                            ],
+                            info3,
+                            api_name="train_extract_f0_feature",
+                        )
                         but3.click(
                             click_train,
                             [
@@ -401,6 +467,7 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue=
                             info3,
                             api_name="train_start",
                         )
+                        
                         but4.click(train_index, [training_name, version19], info3)
                         but5.click(
                             train1key,