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| 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() | |