import os import sys import json import argparse import subprocess now_dir = os.getcwd() sys.path.append(now_dir) from rvc.configs.config import Config from rvc.lib.tools.prerequisites_download import prequisites_download_pipeline from rvc.train.extract.preparing_files import generate_config, generate_filelist from rvc.lib.tools.pretrained_selector import pretrained_selector from rvc.train.process.model_blender import model_blender from rvc.train.process.model_information import model_information from rvc.train.process.extract_small_model import extract_small_model from rvc.infer.infer import infer_pipeline from rvc.lib.tools.analyzer import analyze_audio from rvc.lib.tools.launch_tensorboard import launch_tensorboard_pipeline from rvc.lib.tools.model_download import model_download_pipeline config = Config() current_script_directory = os.path.dirname(os.path.realpath(__file__)) logs_path = os.path.join(current_script_directory, "logs") # Get TTS Voices with open(os.path.join("rvc", "lib", "tools", "tts_voices.json"), "r") as f: voices_data = json.load(f) locales = list({voice["Locale"] for voice in voices_data}) # Infer def run_infer_script( f0up_key, filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, input_path, output_path, pth_path, index_path, split_audio, f0autotune, clean_audio, clean_strength, export_format, embedder_model, embedder_model_custom, upscale_audio, ): f0autotune = "True" if str(f0autotune) == "True" else "False" clean_audio = "True" if str(clean_audio) == "True" else "False" upscale_audio = "True" if str(upscale_audio) == "True" else "False" infer_pipeline( f0up_key, filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, input_path, output_path, pth_path, index_path, split_audio, f0autotune, clean_audio, clean_strength, export_format, embedder_model, embedder_model_custom, upscale_audio, ) return f"File {input_path} inferred successfully.", output_path.replace( ".wav", f".{export_format.lower()}" ) # Batch infer def run_batch_infer_script( f0up_key, filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, input_folder, output_folder, pth_path, index_path, split_audio, f0autotune, clean_audio, clean_strength, export_format, embedder_model, embedder_model_custom, upscale_audio, ): f0autotune = "True" if str(f0autotune) == "True" else "False" clean_audio = "True" if str(clean_audio) == "True" else "False" upscale_audio = "True" if str(upscale_audio) == "True" else "False" audio_files = [ f for f in os.listdir(input_folder) if f.endswith((".mp3", ".wav", ".flac")) ] print(f"Detected {len(audio_files)} audio files for inference.") for audio_file in audio_files: if "_output" in audio_file: pass else: input_path = os.path.join(input_folder, audio_file) output_file_name = os.path.splitext(os.path.basename(audio_file))[0] output_path = os.path.join( output_folder, f"{output_file_name}_output{os.path.splitext(audio_file)[1]}", ) print(f"Inferring {input_path}...") infer_pipeline( f0up_key, filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, input_path, output_path, pth_path, index_path, split_audio, f0autotune, clean_audio, clean_strength, export_format, embedder_model, embedder_model_custom, upscale_audio, ) return f"Files from {input_folder} inferred successfully." # TTS def run_tts_script( tts_text, tts_voice, tts_rate, f0up_key, filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, output_tts_path, output_rvc_path, pth_path, index_path, split_audio, f0autotune, clean_audio, clean_strength, export_format, embedder_model, embedder_model_custom, upscale_audio, ): f0autotune = "True" if str(f0autotune) == "True" else "False" clean_audio = "True" if str(clean_audio) == "True" else "False" upscale_audio = "True" if str(upscale_audio) == "True" else "False" tts_script_path = os.path.join("rvc", "lib", "tools", "tts.py") if os.path.exists(output_tts_path): os.remove(output_tts_path) command_tts = [ "python", tts_script_path, tts_text, tts_voice, str(tts_rate), output_tts_path, ] subprocess.run(command_tts) infer_pipeline( f0up_key, filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, output_tts_path, output_rvc_path, pth_path, index_path, split_audio, f0autotune, clean_audio, clean_strength, export_format, embedder_model, embedder_model_custom, upscale_audio, ) return f"Text {tts_text} synthesized successfully.", output_rvc_path.replace( ".wav", f".{export_format.lower()}" ) # Preprocess def run_preprocess_script(model_name, dataset_path, sampling_rate): per = 3.0 if config.is_half else 3.7 preprocess_script_path = os.path.join("rvc", "train", "preprocess", "preprocess.py") command = [ "python", preprocess_script_path, *map( str, [ os.path.join(logs_path, model_name), dataset_path, sampling_rate, per, ], ), ] os.makedirs(os.path.join(logs_path, model_name), exist_ok=True) subprocess.run(command) return f"Model {model_name} preprocessed successfully." # Extract def run_extract_script( model_name, rvc_version, f0method, hop_length, sampling_rate, embedder_model, embedder_model_custom, ): model_path = os.path.join(logs_path, model_name) extract_f0_script_path = os.path.join( "rvc", "train", "extract", "extract_f0_print.py" ) extract_feature_script_path = os.path.join( "rvc", "train", "extract", "extract_feature_print.py" ) command_1 = [ "python", extract_f0_script_path, *map( str, [ model_path, f0method, hop_length, ], ), ] command_2 = [ "python", extract_feature_script_path, *map( str, [ config.device, "1", "0", "0", model_path, rvc_version, "True", embedder_model, embedder_model_custom, ], ), ] subprocess.run(command_1) subprocess.run(command_2) generate_config(rvc_version, sampling_rate, model_path) generate_filelist(f0method, model_path, rvc_version, sampling_rate) return f"Model {model_name} extracted successfully." # Train def run_train_script( model_name, rvc_version, save_every_epoch, save_only_latest, save_every_weights, total_epoch, sampling_rate, batch_size, gpu, pitch_guidance, overtraining_detector, overtraining_threshold, pretrained, custom_pretrained, sync_graph, g_pretrained_path=None, d_pretrained_path=None, ): f0 = 1 if str(pitch_guidance) == "True" else 0 latest = 1 if str(save_only_latest) == "True" else 0 save_every = 1 if str(save_every_weights) == "True" else 0 detector = 1 if str(overtraining_detector) == "True" else 0 sync = 1 if str(sync_graph) == "True" else 0 if str(pretrained) == "True": if str(custom_pretrained) == "False": pg, pd = pretrained_selector(f0)[rvc_version][sampling_rate] else: if g_pretrained_path is None or d_pretrained_path is None: raise ValueError( "Please provide the path to the pretrained G and D models." ) pg, pd = g_pretrained_path, d_pretrained_path else: pg, pd = "", "" train_script_path = os.path.join("rvc", "train", "train.py") command = [ "python", train_script_path, *map( str, [ "-se", save_every_epoch, "-te", total_epoch, "-pg", pg, "-pd", pd, "-sr", sampling_rate, "-bs", batch_size, "-g", gpu, "-e", os.path.join(logs_path, model_name), "-v", rvc_version, "-l", latest, "-c", "0", "-sw", save_every, "-f0", f0, "-od", detector, "-ot", overtraining_threshold, "-sg", sync, ], ), ] subprocess.run(command) run_index_script(model_name, rvc_version) return f"Model {model_name} trained successfully." # Index def run_index_script(model_name, rvc_version): index_script_path = os.path.join("rvc", "train", "process", "extract_index.py") command = [ "python", index_script_path, os.path.join(logs_path, model_name), rvc_version, ] subprocess.run(command) return f"Index file for {model_name} generated successfully." # Model extract def run_model_extract_script( pth_path, model_name, sampling_rate, pitch_guidance, rvc_version, epoch, step ): f0 = 1 if str(pitch_guidance) == "True" else 0 extract_small_model( pth_path, model_name, sampling_rate, f0, rvc_version, epoch, step ) return f"Model {model_name} extracted successfully." # Model information def run_model_information_script(pth_path): print(model_information(pth_path)) # Model blender def run_model_blender_script(model_name, pth_path_1, pth_path_2, ratio): message, model_blended = model_blender(model_name, pth_path_1, pth_path_2, ratio) return message, model_blended # Tensorboard def run_tensorboard_script(): launch_tensorboard_pipeline() # Download def run_download_script(model_link): model_download_pipeline(model_link) return f"Model downloaded successfully." # Prerequisites def run_prerequisites_script(pretraineds_v1, pretraineds_v2, models, exe): prequisites_download_pipeline(pretraineds_v1, pretraineds_v2, models, exe) return "Prerequisites installed successfully." # Audio analyzer def run_audio_analyzer_script(input_path, save_plot_path="logs/audio_analysis.png"): audio_info, plot_path = analyze_audio(input_path, save_plot_path) print( f"Audio info of {input_path}: {audio_info}", f"Audio file {input_path} analyzed successfully. Plot saved at: {plot_path}", ) return audio_info, plot_path # API def run_api_script(ip, port): command = [ "env/Scripts/uvicorn.exe" if os.name == "nt" else "uvicorn", "api:app", "--host", ip, "--port", port, ] subprocess.run(command) # Parse arguments def parse_arguments(): parser = argparse.ArgumentParser( description="Run the main.py script with specific parameters." ) subparsers = parser.add_subparsers( title="subcommands", dest="mode", help="Choose a mode" ) # Parser for 'infer' mode infer_parser = subparsers.add_parser("infer", help="Run inference") infer_parser.add_argument( "--f0up_key", type=str, help="Value for f0up_key", choices=[str(i) for i in range(-24, 25)], default="0", ) infer_parser.add_argument( "--filter_radius", type=str, help="Value for filter_radius", choices=[str(i) for i in range(11)], default="3", ) infer_parser.add_argument( "--index_rate", type=str, help="Value for index_rate", choices=[str(i / 10) for i in range(11)], default="0.3", ) infer_parser.add_argument( "--rms_mix_rate", type=str, help="Value for rms_mix_rate", choices=[str(i / 10) for i in range(11)], default="1", ) infer_parser.add_argument( "--protect", type=str, help="Value for protect", choices=[str(i / 10) for i in range(6)], default="0.33", ) infer_parser.add_argument( "--hop_length", type=str, help="Value for hop_length", choices=[str(i) for i in range(1, 513)], default="128", ) infer_parser.add_argument( "--f0method", type=str, help="Value for f0method", choices=[ "pm", "harvest", "dio", "crepe", "crepe-tiny", "rmvpe", "fcpe", "hybrid[crepe+rmvpe]", "hybrid[crepe+fcpe]", "hybrid[rmvpe+fcpe]", "hybrid[crepe+rmvpe+fcpe]", ], default="rmvpe", ) infer_parser.add_argument("--input_path", type=str, help="Input path") infer_parser.add_argument("--output_path", type=str, help="Output path") infer_parser.add_argument("--pth_path", type=str, help="Path to the .pth file") infer_parser.add_argument( "--index_path", type=str, help="Path to the .index file", ) infer_parser.add_argument( "--split_audio", type=str, help="Enable split audio", choices=["True", "False"], default="False", ) infer_parser.add_argument( "--f0autotune", type=str, help="Enable autotune", choices=["True", "False"], default="False", ) infer_parser.add_argument( "--clean_audio", type=str, help="Enable clean audio", choices=["True", "False"], default="False", ) infer_parser.add_argument( "--clean_strength", type=str, help="Value for clean_strength", choices=[str(i / 10) for i in range(11)], default="0.7", ) infer_parser.add_argument( "--export_format", type=str, help="Export format", choices=["WAV", "MP3", "FLAC", "OGG", "M4A"], default="WAV", ) infer_parser.add_argument( "--embedder_model", type=str, help="Embedder model", choices=["contentvec", "hubert", "custom"], default="hubert", ) infer_parser.add_argument( "--embedder_model_custom", type=str, help="Custom Embedder model", default=None, ) infer_parser.add_argument( "--upscale_audio", type=str, help="Enable audio upscaling", choices=["True", "False"], default="False", ) # Parser for 'batch_infer' mode batch_infer_parser = subparsers.add_parser( "batch_infer", help="Run batch inference" ) batch_infer_parser.add_argument( "--f0up_key", type=str, help="Value for f0up_key", choices=[str(i) for i in range(-24, 25)], default="0", ) batch_infer_parser.add_argument( "--filter_radius", type=str, help="Value for filter_radius", choices=[str(i) for i in range(11)], default="3", ) batch_infer_parser.add_argument( "--index_rate", type=str, help="Value for index_rate", choices=[str(i / 10) for i in range(11)], default="0.3", ) batch_infer_parser.add_argument( "--rms_mix_rate", type=str, help="Value for rms_mix_rate", choices=[str(i / 10) for i in range(11)], default="1", ) batch_infer_parser.add_argument( "--protect", type=str, help="Value for protect", choices=[str(i / 10) for i in range(6)], default="0.33", ) batch_infer_parser.add_argument( "--hop_length", type=str, help="Value for hop_length", choices=[str(i) for i in range(1, 513)], default="128", ) batch_infer_parser.add_argument( "--f0method", type=str, help="Value for f0method", choices=[ "pm", "harvest", "dio", "crepe", "crepe-tiny", "rmvpe", "fcpe", "hybrid[crepe+rmvpe]", "hybrid[crepe+fcpe]", "hybrid[rmvpe+fcpe]", "hybrid[crepe+rmvpe+fcpe]", ], default="rmvpe", ) batch_infer_parser.add_argument("--input_folder", type=str, help="Input folder") batch_infer_parser.add_argument("--output_folder", type=str, help="Output folder") batch_infer_parser.add_argument( "--pth_path", type=str, help="Path to the .pth file" ) batch_infer_parser.add_argument( "--index_path", type=str, help="Path to the .index file", ) batch_infer_parser.add_argument( "--split_audio", type=str, help="Enable split audio", choices=["True", "False"], default="False", ) batch_infer_parser.add_argument( "--f0autotune", type=str, help="Enable autotune", choices=["True", "False"], default="False", ) batch_infer_parser.add_argument( "--clean_audio", type=str, help="Enable clean audio", choices=["True", "False"], default="False", ) batch_infer_parser.add_argument( "--clean_strength", type=str, help="Value for clean_strength", choices=[str(i / 10) for i in range(11)], default="0.7", ) batch_infer_parser.add_argument( "--export_format", type=str, help="Export format", choices=["WAV", "MP3", "FLAC", "OGG", "M4A"], default="WAV", ) batch_infer_parser.add_argument( "--embedder_model", type=str, help="Embedder model", choices=["contentvec", "hubert", "custom"], default="hubert", ) batch_infer_parser.add_argument( "--embedder_model_custom", type=str, help="Custom Embedder model", default=None, ) batch_infer_parser.add_argument( "--upscale_audio", type=str, help="Enable audio upscaling", choices=["True", "False"], default="False", ) # Parser for 'tts' mode tts_parser = subparsers.add_parser("tts", help="Run TTS") tts_parser.add_argument( "--tts_text", type=str, help="Text to be synthesized", ) tts_parser.add_argument( "--tts_voice", type=str, help="Voice to be used", choices=locales, ) tts_parser.add_argument( "--tts_rate", type=str, help="Increase or decrease TTS speed", choices=[str(i) for i in range(-100, 100)], default="0", ) tts_parser.add_argument( "--f0up_key", type=str, help="Value for f0up_key", choices=[str(i) for i in range(-24, 25)], default="0", ) tts_parser.add_argument( "--filter_radius", type=str, help="Value for filter_radius", choices=[str(i) for i in range(11)], default="3", ) tts_parser.add_argument( "--index_rate", type=str, help="Value for index_rate", choices=[str(i / 10) for i in range(11)], default="0.3", ) tts_parser.add_argument( "--rms_mix_rate", type=str, help="Value for rms_mix_rate", choices=[str(i / 10) for i in range(11)], default="1", ) tts_parser.add_argument( "--protect", type=str, help="Value for protect", choices=[str(i / 10) for i in range(6)], default="0.33", ) tts_parser.add_argument( "--hop_length", type=str, help="Value for hop_length", choices=[str(i) for i in range(1, 513)], default="128", ) tts_parser.add_argument( "--f0method", type=str, help="Value for f0method", choices=[ "pm", "harvest", "dio", "crepe", "crepe-tiny", "rmvpe", "fcpe", "hybrid[crepe+rmvpe]", "hybrid[crepe+fcpe]", "hybrid[rmvpe+fcpe]", "hybrid[crepe+rmvpe+fcpe]", ], default="rmvpe", ) tts_parser.add_argument("--output_tts_path", type=str, help="Output tts path") tts_parser.add_argument("--output_rvc_path", type=str, help="Output rvc path") tts_parser.add_argument("--pth_path", type=str, help="Path to the .pth file") tts_parser.add_argument( "--index_path", type=str, help="Path to the .index file", ) tts_parser.add_argument( "--split_audio", type=str, help="Enable split audio", choices=["True", "False"], default="False", ) tts_parser.add_argument( "--f0autotune", type=str, help="Enable autotune", choices=["True", "False"], default="False", ) tts_parser.add_argument( "--clean_audio", type=str, help="Enable clean audio", choices=["True", "False"], default="False", ) tts_parser.add_argument( "--clean_strength", type=str, help="Value for clean_strength", choices=[str(i / 10) for i in range(11)], default="0.7", ) tts_parser.add_argument( "--export_format", type=str, help="Export format", choices=["WAV", "MP3", "FLAC", "OGG", "M4A"], default="WAV", ) tts_parser.add_argument( "--embedder_model", type=str, help="Embedder model", choices=["contentvec", "hubert", "custom"], default="hubert", ) tts_parser.add_argument( "--embedder_model_custom", type=str, help="Custom Embedder model", default=None, ) tts_parser.add_argument( "--upscale_audio", type=str, help="Enable audio upscaling", choices=["True", "False"], default="False", ) # Parser for 'preprocess' mode preprocess_parser = subparsers.add_parser("preprocess", help="Run preprocessing") preprocess_parser.add_argument("--model_name", type=str, help="Name of the model") preprocess_parser.add_argument( "--dataset_path", type=str, help="Path to the dataset", ) preprocess_parser.add_argument( "--sampling_rate", type=str, help="Sampling rate", choices=["32000", "40000", "48000"], ) # Parser for 'extract' mode extract_parser = subparsers.add_parser("extract", help="Run extract") extract_parser.add_argument( "--model_name", type=str, help="Name of the model", ) extract_parser.add_argument( "--rvc_version", type=str, help="Version of the model", choices=["v1", "v2"], default="v2", ) extract_parser.add_argument( "--f0method", type=str, help="Value for f0method", choices=[ "pm", "harvest", "dio", "crepe", "crepe-tiny", "rmvpe", ], default="rmvpe", ) extract_parser.add_argument( "--hop_length", type=str, help="Value for hop_length", choices=[str(i) for i in range(1, 513)], default="128", ) extract_parser.add_argument( "--sampling_rate", type=str, help="Sampling rate", choices=["32000", "40000", "48000"], ) extract_parser.add_argument( "--embedder_model", type=str, help="Embedder model", choices=["contentvec", "hubert", "custom"], default="hubert", ) extract_parser.add_argument( "--embedder_model_custom", type=str, help="Custom Embedder model", default=None, ) # Parser for 'train' mode train_parser = subparsers.add_parser("train", help="Run training") train_parser.add_argument( "--model_name", type=str, help="Name of the model", ) train_parser.add_argument( "--rvc_version", type=str, help="Version of the model", choices=["v1", "v2"], default="v2", ) train_parser.add_argument( "--save_every_epoch", type=str, help="Save every epoch", choices=[str(i) for i in range(1, 101)], ) train_parser.add_argument( "--save_only_latest", type=str, help="Save weight only at last epoch", choices=["True", "False"], default="False", ) train_parser.add_argument( "--save_every_weights", type=str, help="Save weight every epoch", choices=["True", "False"], default="True", ) train_parser.add_argument( "--total_epoch", type=str, help="Total epoch", choices=[str(i) for i in range(1, 10001)], default="1000", ) train_parser.add_argument( "--sampling_rate", type=str, help="Sampling rate", choices=["32000", "40000", "48000"], ) train_parser.add_argument( "--batch_size", type=str, help="Batch size", choices=[str(i) for i in range(1, 51)], default="8", ) train_parser.add_argument( "--gpu", type=str, help="GPU number", default="0", ) train_parser.add_argument( "--pitch_guidance", type=str, help="Pitch guidance", choices=["True", "False"], default="True", ) train_parser.add_argument( "--pretrained", type=str, help="Pretrained", choices=["True", "False"], default="True", ) train_parser.add_argument( "--custom_pretrained", type=str, help="Custom pretrained", choices=["True", "False"], default="False", ) train_parser.add_argument( "--g_pretrained_path", type=str, nargs="?", default=None, help="Path to the pretrained G file", ) train_parser.add_argument( "--d_pretrained_path", type=str, nargs="?", default=None, help="Path to the pretrained D file", ) train_parser.add_argument( "--overtraining_detector", type=str, help="Overtraining detector", choices=["True", "False"], default="False", ) train_parser.add_argument( "--overtraining_threshold", type=str, help="Overtraining threshold", choices=[str(i) for i in range(1, 101)], default="50", ) train_parser.add_argument( "--sync_graph", type=str, help="Sync graph", choices=["True", "False"], default="False", ) # Parser for 'index' mode index_parser = subparsers.add_parser("index", help="Generate index file") index_parser.add_argument( "--model_name", type=str, help="Name of the model", ) index_parser.add_argument( "--rvc_version", type=str, help="Version of the model", choices=["v1", "v2"], default="v2", ) # Parser for 'model_extract' mode model_extract_parser = subparsers.add_parser("model_extract", help="Extract model") model_extract_parser.add_argument( "--pth_path", type=str, help="Path to the .pth file", ) model_extract_parser.add_argument( "--model_name", type=str, help="Name of the model", ) model_extract_parser.add_argument( "--sampling_rate", type=str, help="Sampling rate", choices=["40000", "48000"], ) model_extract_parser.add_argument( "--pitch_guidance", type=str, help="Pitch guidance", choices=["True", "False"], ) model_extract_parser.add_argument( "--rvc_version", type=str, help="Version of the model", choices=["v1", "v2"], default="v2", ) model_extract_parser.add_argument( "--epoch", type=str, help="Epochs of the model", choices=[str(i) for i in range(1, 10001)], ) model_extract_parser.add_argument( "--step", type=str, help="Steps of the model", ) # Parser for 'model_information' mode model_information_parser = subparsers.add_parser( "model_information", help="Print model information" ) model_information_parser.add_argument( "--pth_path", type=str, help="Path to the .pth file", ) # Parser for 'model_blender' mode model_blender_parser = subparsers.add_parser( "model_blender", help="Fuse two models" ) model_blender_parser.add_argument( "--model_name", type=str, help="Name of the model", ) model_blender_parser.add_argument( "--pth_path_1", type=str, help="Path to the first .pth file", ) model_blender_parser.add_argument( "--pth_path_2", type=str, help="Path to the second .pth file", ) model_blender_parser.add_argument( "--ratio", type=str, help="Value for blender ratio", choices=[str(i / 10) for i in range(11)], default="0.5", ) # Parser for 'tensorboard' mode subparsers.add_parser("tensorboard", help="Run tensorboard") # Parser for 'download' mode download_parser = subparsers.add_parser("download", help="Download models") download_parser.add_argument( "--model_link", type=str, help="Link of the model", ) # Parser for 'prerequisites' mode prerequisites_parser = subparsers.add_parser( "prerequisites", help="Install prerequisites" ) prerequisites_parser.add_argument( "--pretraineds_v1", type=str, choices=["True", "False"], default="True", help="Download pretrained models for v1", ) prerequisites_parser.add_argument( "--pretraineds_v2", type=str, choices=["True", "False"], default="True", help="Download pretrained models for v2", ) prerequisites_parser.add_argument( "--models", type=str, choices=["True", "False"], default="True", help="Donwload models", ) prerequisites_parser.add_argument( "--exe", type=str, choices=["True", "False"], default="True", help="Download executables", ) # Parser for 'audio_analyzer' mode audio_analyzer = subparsers.add_parser("audio_analyzer", help="Run audio analyzer") audio_analyzer.add_argument( "--input_path", type=str, help="Path to the input audio file", ) # Parser for 'api' mode api_parser = subparsers.add_parser("api", help="Run the API") api_parser.add_argument( "--host", type=str, help="Host address", default="127.0.0.1" ) api_parser.add_argument("--port", type=str, help="Port", default="8000") return parser.parse_args() def main(): if len(sys.argv) == 1: print("Please run the script with '-h' for more information.") sys.exit(1) args = parse_arguments() try: if args.mode == "infer": run_infer_script( str(args.f0up_key), str(args.filter_radius), str(args.index_rate), str(args.rms_mix_rate), str(args.protect), str(args.hop_length), str(args.f0method), str(args.input_path), str(args.output_path), str(args.pth_path), str(args.index_path), str(args.split_audio), str(args.f0autotune), str(args.clean_audio), str(args.clean_strength), str(args.export_format), str(args.embedder_model), str(args.embedder_model_custom), str(args.upscale_audio), ) elif args.mode == "batch_infer": run_batch_infer_script( str(args.f0up_key), str(args.filter_radius), str(args.index_rate), str(args.rms_mix_rate), str(args.protect), str(args.hop_length), str(args.f0method), str(args.input_folder), str(args.output_folder), str(args.pth_path), str(args.index_path), str(args.split_audio), str(args.f0autotune), str(args.clean_audio), str(args.clean_strength), str(args.export_format), str(args.embedder_model), str(args.embedder_model_custom), str(args.upscale_audio), ) elif args.mode == "tts": run_tts_script( str(args.tts_text), str(args.tts_voice), str(args.tts_rate), str(args.f0up_key), str(args.filter_radius), str(args.index_rate), str(args.rms_mix_rate), str(args.protect), str(args.hop_length), str(args.f0method), str(args.output_tts_path), str(args.output_rvc_path), str(args.pth_path), str(args.index_path), str(args.split_audio), str(args.f0autotune), str(args.clean_audio), str(args.clean_strength), str(args.export_format), str(args.embedder_model), str(args.embedder_model_custom), str(args.upscale_audio), ) elif args.mode == "preprocess": run_preprocess_script( str(args.model_name), str(args.dataset_path), str(args.sampling_rate), ) elif args.mode == "extract": run_extract_script( str(args.model_name), str(args.rvc_version), str(args.f0method), str(args.hop_length), str(args.sampling_rate), str(args.embedder_model), str(args.embedder_model_custom), ) elif args.mode == "train": run_train_script( str(args.model_name), str(args.rvc_version), str(args.save_every_epoch), str(args.save_only_latest), str(args.save_every_weights), str(args.total_epoch), str(args.sampling_rate), str(args.batch_size), str(args.gpu), str(args.pitch_guidance), str(args.overtraining_detector), str(args.overtraining_threshold), str(args.pretrained), str(args.custom_pretrained), str(args.sync_graph), str(args.g_pretrained_path), str(args.d_pretrained_path), ) elif args.mode == "index": run_index_script( str(args.model_name), str(args.rvc_version), ) elif args.mode == "model_extract": run_model_extract_script( str(args.pth_path), str(args.model_name), str(args.sampling_rate), str(args.pitch_guidance), str(args.rvc_version), str(args.epoch), str(args.step), ) elif args.mode == "model_information": run_model_information_script( str(args.pth_path), ) elif args.mode == "model_blender": run_model_blender_script( str(args.model_name), str(args.pth_path_1), str(args.pth_path_2), str(args.ratio), ) elif args.mode == "tensorboard": run_tensorboard_script() elif args.mode == "download": run_download_script( str(args.model_link), ) elif args.mode == "prerequisites": run_prerequisites_script( str(args.pretraineds_v1), str(args.pretraineds_v2), str(args.models), str(args.exe), ) elif args.mode == "audio_analyzer": run_audio_analyzer_script( str(args.input_path), ) elif args.mode == "api": run_api_script( str(args.host), str(args.port), ) except Exception as error: print(f"Error: {error}") if __name__ == "__main__": main()