import os, sys import gradio as gr import regex as re import shutil import datetime import random from core import ( run_infer_script, run_batch_infer_script, ) from assets.i18n.i18n import I18nAuto from rvc.lib.utils import format_title 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, "assets", "audios") 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 ] 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 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"}, ) 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: str) -> tuple: model_files_trip = re.sub(r"\.pth|\.onnx$", "", model_file) model_file_name = os.path.split(model_files_trip)[ -1 ] # Extract only the name, not the directory # Check if the sid0strip has the specific ending format _eXXX_sXXX if re.match(r".+_e\d+_s\d+$", model_file_name): base_model_name = model_file_name.rsplit("_", 2)[0] else: base_model_name = model_file_name sid_directory = os.path.join(model_root_relative, base_model_name) double_sid_directory = os.path.join(sid_directory, base_model_name) directories_to_search = [sid_directory] if os.path.exists(sid_directory) else [] directories_to_search += ( [double_sid_directory] if os.path.exists(double_sid_directory) else [] ) directories_to_search.append(model_root_relative) matching_index_files = [] for directory in directories_to_search: for filename in os.listdir(directory): if filename.endswith(".index") and "trained" not in filename: # Condition to match the name name_match = any( name.lower() in filename.lower() for name in [model_file_name, base_model_name] ) # If in the specific directory, it's automatically a match folder_match = directory == sid_directory if name_match or folder_match: index_path = os.path.join(directory, filename) if index_path in indexes_list: matching_index_files.append( ( index_path, os.path.getsize(index_path), " " not in filename, ) ) if matching_index_files: # Sort by favoring files without spaces and by size (largest size first) matching_index_files.sort(key=lambda x: (-x[2], -x[1])) best_match_index_path = matching_index_files[0][0] return best_match_index_path return "" def save_to_wav(record_button): if record_button is None: pass else: path_to_file = record_button new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + ".wav" target_path = os.path.join(audio_root_relative, os.path.basename(new_name)) shutil.move(path_to_file, target_path) return target_path, output_path_fn(target_path) def save_to_wav2(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) shutil.copy(file_path, target_path) return target_path, output_path_fn(target_path) def delete_outputs(): 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)) gr.Info(f"Outputs cleared!") # Inference tab def inference_tab(): default_weight = random.choice(names) if names else None with gr.Row(): with gr.Row(): model_file = gr.Dropdown( label=i18n("Voice Model"), choices=sorted(names, key=lambda path: os.path.getsize(path)), interactive=True, value=default_weight, allow_custom_value=True, ) index_file = gr.Dropdown( label=i18n("Index File"), choices=get_indexes(), value=match_index(default_weight) if default_weight else "", interactive=True, allow_custom_value=True, ) with gr.Column(): refresh_button = gr.Button(i18n("Refresh")) unload_button = gr.Button(i18n("Unload Voice")) unload_button.click( fn=lambda: ({"value": "", "__type__": "update"}), inputs=[], outputs=[model_file], ) model_file.select( fn=match_index, inputs=[model_file], outputs=[index_file], ) # Single inference tab with gr.Tab(i18n("Single")): with gr.Row(): with gr.Column(): upload_audio = gr.Audio( label=i18n("Upload Audio"), type="filepath", editable=False ) with gr.Row(): audio = gr.Dropdown( label=i18n("Select Audio"), choices=sorted(audio_paths), value=audio_paths[0] if audio_paths else "", interactive=True, allow_custom_value=True, ) with gr.Accordion(i18n("Advanced Settings"), open=False): with gr.Column(): clear_outputs = gr.Button( i18n("Clear Outputs (Deletes all audios in assets/audios)") ) output_path = gr.Textbox( label=i18n("Output Path"), placeholder=i18n("Enter output path"), value=( output_path_fn(audio_paths[0]) if audio_paths else os.path.join(now_dir, "assets", "audios", "output.wav") ), interactive=True, ) split_audio = gr.Checkbox( label=i18n("Split Audio"), visible=True, value=False, interactive=True, ) pitch = gr.Slider( minimum=-24, maximum=24, step=1, label=i18n("Pitch"), value=0, interactive=True, ) filter_radius = gr.Slider( minimum=0, maximum=7, label=i18n( "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness" ), value=3, step=1, interactive=True, ) index_rate = gr.Slider( minimum=0, maximum=1, label=i18n("Search Feature Ratio"), value=0.75, interactive=True, ) hop_length = gr.Slider( minimum=1, maximum=512, step=1, label=i18n("Hop Length"), value=128, interactive=True, ) with gr.Column(): f0method = gr.Radio( label=i18n("Pitch extraction algorithm"), choices=[ "pm", "harvest", "dio", "crepe", "crepe-tiny", "rmvpe", ], value="rmvpe", interactive=True, ) convert_button1 = gr.Button(i18n("Convert")) with gr.Row(): # Defines output info + output audio download after conversion vc_output1 = gr.Textbox(label=i18n("Output Information")) vc_output2 = gr.Audio(label=i18n("Export Audio")) # Batch inference tab with gr.Tab(i18n("Batch")): with gr.Row(): with gr.Column(): input_folder_batch = gr.Textbox( label=i18n("Input Folder"), placeholder=i18n("Enter input path"), value=os.path.join(now_dir, "assets", "audios"), interactive=True, ) output_folder_batch = gr.Textbox( label=i18n("Output Folder"), placeholder=i18n("Enter output path"), value=os.path.join(now_dir, "assets", "audios"), interactive=True, ) with gr.Accordion(i18n("Advanced Settings"), open=False): with gr.Column(): clear_outputs = gr.Button( i18n("Clear Outputs (Deletes all audios in assets/audios)") ) pitch_batch = gr.Slider( minimum=-24, maximum=24, step=1, label=i18n("Pitch"), value=0, interactive=True, ) filter_radius_batch = gr.Slider( minimum=0, maximum=7, label=i18n( "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness" ), value=3, step=1, interactive=True, ) index_rate_batch = gr.Slider( minimum=0, maximum=1, label=i18n("Search Feature Ratio"), value=0.75, interactive=True, ) hop_length_batch = gr.Slider( minimum=1, maximum=512, step=1, label=i18n("Hop Length"), value=128, interactive=True, ) with gr.Column(): f0method_batch = gr.Radio( label=i18n("Pitch extraction algorithm"), choices=[ "pm", "harvest", "dio", "crepe", "crepe-tiny", "rmvpe", ], value="rmvpe", interactive=True, ) convert_button2 = gr.Button(i18n("Convert")) with gr.Row(): # Defines output info + output audio download after conversion vc_output3 = gr.Textbox(label=i18n("Output Information")) def toggle_visible(checkbox): return {"visible": checkbox, "__type__": "update"} refresh_button.click( fn=change_choices, inputs=[], outputs=[model_file, index_file, audio], ) audio.change( fn=output_path_fn, inputs=[audio], outputs=[output_path], ) upload_audio.upload( fn=save_to_wav2, inputs=[upload_audio], outputs=[audio, output_path], ) upload_audio.stop_recording( fn=save_to_wav, inputs=[upload_audio], outputs=[audio, output_path], ) clear_outputs.click( fn=delete_outputs, inputs=[], outputs=[], ) convert_button1.click( fn=run_infer_script, inputs=[ pitch, filter_radius, index_rate, hop_length, f0method, audio, output_path, model_file, index_file, split_audio, ], outputs=[vc_output1, vc_output2], ) convert_button2.click( fn=run_batch_infer_script, inputs=[ pitch_batch, filter_radius_batch, index_rate_batch, hop_length_batch, f0method_batch, input_folder_batch, output_folder_batch, model_file, index_file, ], outputs=[vc_output3], )