Update full_inference.py

Author: TheNeodev

tabs/full_inference.py

@@ -1,265 +1,11 @@
 from core import full_inference_program, download_music
 import sys, os
 import gradio as gr
-import regex as re
-from assets.i18n.i18n import I18nAuto
-import torch
-import shutil
-import unicodedata
-import gradio as gr
-from assets.i18n.i18n import I18nAuto
-
-
-i18n = I18nAuto()
-
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-
-
-model_root = os.path.join(now_dir, "logs")
-audio_root = os.path.join(now_dir, "audio_files", "original_files")
-
-
-model_root_relative = os.path.relpath(model_root, now_dir)
-audio_root_relative = os.path.relpath(audio_root, now_dir)
-
-
-sup_audioext = {
-    "wav",
-    "mp3",
-    "flac",
-    "ogg",
-    "opus",
-    "m4a",
-    "mp4",
-    "aac",
-    "alac",
-    "wma",
-    "aiff",
-    "webm",
-    "ac3",
-}
-
-
-names = [
-    os.path.join(root, file)
-    for root, _, files in os.walk(model_root_relative, topdown=False)
-    for file in files
-    if (
-        file.endswith((".pth", ".onnx"))
-        and not (file.startswith("G_") or file.startswith("D_"))
-    )
-]
-
-
-indexes_list = [
-    os.path.join(root, name)
-    for root, _, files in os.walk(model_root_relative, topdown=False)
-    for name in files
-    if name.endswith(".index") and "trained" not in name
-]
-
-
-audio_paths = [
-    os.path.join(root, name)
-    for root, _, files in os.walk(audio_root_relative, topdown=False)
-    for name in files
-    if name.endswith(tuple(sup_audioext))
-    and root == audio_root_relative
-    and "_output" not in name
-]
-
-
-vocals_model_names = [
-    "Mel-Roformer by KimberleyJSN",
-    "BS-Roformer by ViperX",
-    "MDX23C",
-]
-
-
-karaoke_models_names = [
-    "Mel-Roformer Karaoke by aufr33 and viperx",
-    "UVR-BVE",
-]
-
-
-denoise_models_names = [
-    "Mel-Roformer Denoise Normal by aufr33",
-    "Mel-Roformer Denoise Aggressive by aufr33",
-    "UVR Denoise",
-]
-
-
-dereverb_models_names = [
-    "MDX23C DeReverb by aufr33 and jarredou",
-    "UVR-Deecho-Dereverb",
-    "MDX Reverb HQ by FoxJoy",
-    "BS-Roformer Dereverb by anvuew",
-]
-
-
-deeecho_models_names = ["UVR-Deecho-Normal", "UVR-Deecho-Aggressive"]
-
-
-def get_indexes():
-
-    indexes_list = [
-        os.path.join(dirpath, filename)
-        for dirpath, _, filenames in os.walk(model_root_relative)
-        for filename in filenames
-        if filename.endswith(".index") and "trained" not in filename
-    ]
-
-    return indexes_list if indexes_list else ""
-
-
-def match_index(model_file_value):
-    if model_file_value:
-        model_folder = os.path.dirname(model_file_value)
-        model_name = os.path.basename(model_file_value)
-        index_files = get_indexes()
-        pattern = r"^(.*?)_"
-        match = re.match(pattern, model_name)
-        for index_file in index_files:
-            if os.path.dirname(index_file) == model_folder:
-                return index_file
-
-            elif match and match.group(1) in os.path.basename(index_file):
-                return index_file
-
-            elif model_name in os.path.basename(index_file):
-                return index_file
-
-    return ""
-
-
-def output_path_fn(input_audio_path):
-    original_name_without_extension = os.path.basename(input_audio_path).rsplit(".", 1)[
-        0
-    ]
-    new_name = original_name_without_extension + "_output.wav"
-    output_path = os.path.join(os.path.dirname(input_audio_path), new_name)
-
-    return output_path
-
-
-def get_number_of_gpus():
-    if torch.cuda.is_available():
-        num_gpus = torch.cuda.device_count()
-
-        return "-".join(map(str, range(num_gpus)))
-
-    else:
-
-        return "-"
-
-
-def max_vram_gpu(gpu):
-
-    if torch.cuda.is_available():
-        gpu_properties = torch.cuda.get_device_properties(gpu)
-        total_memory_gb = round(gpu_properties.total_memory / 1024 / 1024 / 1024)
-
-        return total_memory_gb / 2
-
-    else:
-
-        return "0"
-
-
-def format_title(title):
-
-    formatted_title = (
-        unicodedata.normalize("NFKD", title).encode("ascii", "ignore").decode("utf-8")
-    )
-
-    formatted_title = re.sub(r"[\u2500-\u257F]+", "", formatted_title)
-    formatted_title = re.sub(r"[^\w\s.-]", "", formatted_title)
-    formatted_title = re.sub(r"\s+", "_", formatted_title)
-
-    return formatted_title
-
-
-def save_to_wav(upload_audio):
-
-    file_path = upload_audio
-    formated_name = format_title(os.path.basename(file_path))
-    target_path = os.path.join(audio_root_relative, formated_name)
-
-    if os.path.exists(target_path):
-        os.remove(target_path)
-
-    os.makedirs(os.path.dirname(target_path), exist_ok=True)
-    shutil.copy(file_path, target_path)
-    
-    return target_path, output_path_fn(target_path)
-
-
-def delete_outputs():
-    gr.Info(f"Outputs cleared!")
-    for root, _, files in os.walk(audio_root_relative, topdown=False):
-        for name in files:
-            if name.endswith(tuple(sup_audioext)) and name.__contains__("_output"):
-                os.remove(os.path.join(root, name))
-
-
-def change_choices():
-    names = [
-        os.path.join(root, file)
-        for root, _, files in os.walk(model_root_relative, topdown=False)
-        for file in files
-        if (
-            file.endswith((".pth", ".onnx"))
-            and not (file.startswith("G_") or file.startswith("D_"))
-        )
-    ]
-
-    indexes_list = [
-        os.path.join(root, name)
-        for root, _, files in os.walk(model_root_relative, topdown=False)
-        for name in files
-        if name.endswith(".index") and "trained" not in name
-    ]
-
-    audio_paths = [
-        os.path.join(root, name)
-        for root, _, files in os.walk(audio_root_relative, topdown=False)
-        for name in files
-        if name.endswith(tuple(sup_audioext))
-        and root == audio_root_relative
-        and "_output" not in name
-    ]
-
-    return (
-        {"choices": sorted(names), "__type__": "update"},
-        {"choices": sorted(indexes_list), "__type__": "update"},
-        {"choices": sorted(audio_paths), "__type__": "update"},
-    )
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+from tabs.infer.variable import *
 
 
 def download_music_tab():
-    with gr.Row():
+    with gr.Row(equal_height=True):
         link = gr.Textbox(
             label=i18n("Music URL"),
             lines=1,
@@ -280,7 +26,7 @@ def download_music_tab():
 def full_inference_tab():
     default_weight = names[0] if names else None
 
-    with gr.Row():
+    with gr.Row(equal_height=True):
         with gr.Row():
             model_file = gr.Dropdown(
                 label=i18n("Voice Model"),
@@ -860,10 +606,10 @@ def full_inference_tab():
                     interactive=True,
                 )
 
-        with gr.Row():
+        with gr.Row(equal_height=True):
             convert_button = gr.Button(i18n("Convert"))
 
-        with gr.Row():
+        with gr.Row(equal_height=True):
             vc_output1 = gr.Textbox(
                 label=i18n("Output Information"),
                 info=i18n("The output information will be displayed here."),