import os import sys import json import argparse import subprocess from functools import lru_cache from distutils.util import strtobool now_dir = os.getcwd() sys.path.append(now_dir) current_script_directory = os.path.dirname(os.path.realpath(__file__)) logs_path = os.path.join(current_script_directory, "logs") from rvc.lib.tools.prerequisites_download import prequisites_download_pipeline from rvc.train.extract.preparing_files import generate_config, generate_filelist 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.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 python = sys.executable # Get TTS Voices -> https://speech.platform.bing.com/consumer/speech/synthesize/readaloud/voices/list?trustedclienttoken=6A5AA1D4EAFF4E9FB37E23D68491D6F4 @lru_cache(maxsize=1) # Cache only one result since the file is static def load_voices_data(): with open(os.path.join("rvc", "lib", "tools", "tts_voices.json")) as f: return json.load(f) voices_data = load_voices_data() locales = list({voice["Locale"] for voice in voices_data}) @lru_cache(maxsize=None) def import_voice_converter(): from rvc.infer.infer import VoiceConverter return VoiceConverter() @lru_cache(maxsize=1) def get_config(): from rvc.configs.config import Config return Config() # Infer def run_infer_script( pitch: int, filter_radius: int, index_rate: float, volume_envelope: int, protect: float, hop_length: int, f0_method: str, input_path: str, output_path: str, pth_path: str, index_path: str, split_audio: bool, f0_autotune: bool, clean_audio: bool, clean_strength: float, export_format: str, upscale_audio: bool, f0_file: str, embedder_model: str, embedder_model_custom: str = None, ): infer_pipeline = import_voice_converter() infer_pipeline.convert_audio( pitch=pitch, filter_radius=filter_radius, index_rate=index_rate, volume_envelope=volume_envelope, protect=protect, hop_length=hop_length, f0_method=f0_method, audio_input_path=input_path, audio_output_path=output_path, model_path=pth_path, index_path=index_path, split_audio=split_audio, f0_autotune=f0_autotune, clean_audio=clean_audio, clean_strength=clean_strength, export_format=export_format, upscale_audio=upscale_audio, f0_file=f0_file, embedder_model=embedder_model, embedder_model_custom=embedder_model_custom, ) return f"File {input_path} inferred successfully.", output_path.replace( ".wav", f".{export_format.lower()}" ) # Batch infer def run_batch_infer_script( pitch: int, filter_radius: int, index_rate: float, volume_envelope: int, protect: float, hop_length: int, f0_method: str, input_folder: str, output_folder: str, pth_path: str, index_path: str, split_audio: bool, f0_autotune: bool, clean_audio: bool, clean_strength: float, export_format: str, upscale_audio: bool, f0_file: str, embedder_model: str, embedder_model_custom: str = None, ): 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]}", ) infer_pipeline = import_voice_converter() print(f"Inferring {input_path}...") infer_pipeline.convert_audio( pitch=pitch, filter_radius=filter_radius, index_rate=index_rate, volume_envelope=volume_envelope, protect=protect, hop_length=hop_length, f0_method=f0_method, audio_input_path=input_path, audio_output_path=output_path, model_path=pth_path, index_path=index_path, split_audio=split_audio, f0_autotune=f0_autotune, clean_audio=clean_audio, clean_strength=clean_strength, export_format=export_format, upscale_audio=upscale_audio, f0_file=f0_file, embedder_model=embedder_model, embedder_model_custom=embedder_model_custom, ) return f"Files from {input_folder} inferred successfully." # TTS def run_tts_script( tts_text: str, tts_voice: str, tts_rate: int, pitch: int, filter_radius: int, index_rate: float, volume_envelope: int, protect: float, hop_length: int, f0_method: str, output_tts_path: str, output_rvc_path: str, pth_path: str, index_path: str, split_audio: bool, f0_autotune: bool, clean_audio: bool, clean_strength: float, export_format: str, upscale_audio: bool, f0_file: str, embedder_model: str, embedder_model_custom: str = None, ): 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 = [ *map( str, [ python, tts_script_path, tts_text, tts_voice, tts_rate, output_tts_path, ], ), ] subprocess.run(command_tts) infer_pipeline = import_voice_converter() infer_pipeline.convert_audio( pitch=pitch, filter_radius=filter_radius, index_rate=index_rate, volume_envelope=volume_envelope, protect=protect, hop_length=hop_length, f0_method=f0_method, audio_input_path=output_tts_path, audio_output_path=output_rvc_path, model_path=pth_path, index_path=index_path, split_audio=split_audio, f0_autotune=f0_autotune, clean_audio=clean_audio, clean_strength=clean_strength, export_format=export_format, upscale_audio=upscale_audio, f0_file=f0_file, embedder_model=embedder_model, embedder_model_custom=embedder_model_custom, ) return f"Text {tts_text} synthesized successfully.", output_rvc_path.replace( ".wav", f".{export_format.lower()}" ) # Preprocess def run_preprocess_script( model_name: str, dataset_path: str, sample_rate: int, cpu_cores: int ): config = get_config() 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, sample_rate, per, cpu_cores, ], ), ] 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: str, rvc_version: str, f0_method: str, pitch_guidance: bool, hop_length: int, cpu_cores: int, gpu: int, sample_rate: int, embedder_model: str, embedder_model_custom: str = None, ): model_path = os.path.join(logs_path, model_name) extract = os.path.join("rvc", "train", "extract", "extract.py") command_1 = [ python, extract, *map( str, [ model_path, f0_method, hop_length, cpu_cores, gpu, rvc_version, embedder_model, embedder_model_custom, ], ), ] subprocess.run(command_1) generate_config(rvc_version, sample_rate, model_path) generate_filelist(pitch_guidance, model_path, rvc_version, sample_rate) return f"Model {model_name} extracted successfully." # Train def run_train_script( model_name: str, rvc_version: str, save_every_epoch: int, save_only_latest: bool, save_every_weights: bool, total_epoch: int, sample_rate: int, batch_size: int, gpu: int, pitch_guidance: bool, overtraining_detector: bool, overtraining_threshold: int, pretrained: bool, sync_graph: bool, index_algorithm: str, cache_data_in_gpu: bool, custom_pretrained: bool = False, g_pretrained_path: str = None, d_pretrained_path: str = None, ): if pretrained == True: from rvc.lib.tools.pretrained_selector import pretrained_selector if custom_pretrained == False: pg, pd = pretrained_selector(bool(pitch_guidance))[str(rvc_version)][ int(sample_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, [ model_name, save_every_epoch, total_epoch, pg, pd, rvc_version, gpu, batch_size, sample_rate, pitch_guidance, save_only_latest, save_every_weights, cache_data_in_gpu, overtraining_detector, overtraining_threshold, sync_graph, ], ), ] subprocess.run(command) run_index_script(model_name, rvc_version, index_algorithm) return f"Model {model_name} trained successfully." # Index def run_index_script(model_name: str, rvc_version: str, index_algorithm: str): 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, index_algorithm, ] subprocess.run(command) return f"Index file for {model_name} generated successfully." # Model extract def run_model_extract_script( pth_path: str, model_name: str, sample_rate: int, pitch_guidance: bool, rvc_version: str, epoch: int, step: int, ): extract_small_model( pth_path, model_name, sample_rate, pitch_guidance, rvc_version, epoch, step ) return f"Model {model_name} extracted successfully." # Model information def run_model_information_script(pth_path: str): print(model_information(pth_path)) # Model blender def run_model_blender_script( model_name: str, pth_path_1: str, pth_path_2: str, ratio: float ): 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: str): model_download_pipeline(model_link) return f"Model downloaded successfully." # Prerequisites def run_prerequisites_script( pretraineds_v1: bool, pretraineds_v2: bool, models: bool, exe: bool ): prequisites_download_pipeline(pretraineds_v1, pretraineds_v2, models, exe) return "Prerequisites installed successfully." # Audio analyzer def run_audio_analyzer_script( input_path: str, save_plot_path: str = "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: str, port: int): 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") pitch_description = ( "Set the pitch of the audio. Higher values result in a higher pitch." ) infer_parser.add_argument( "--pitch", type=int, help=pitch_description, choices=range(-24, 25), default=0, ) filter_radius_description = "Apply median filtering to the extracted pitch values if this value is greater than or equal to three. This can help reduce breathiness in the output audio." infer_parser.add_argument( "--filter_radius", type=int, help=filter_radius_description, choices=range(11), default=3, ) index_rate_description = "Control the influence of the index file on the output. Higher values mean stronger influence. Lower values can help reduce artifacts but may result in less accurate voice cloning." infer_parser.add_argument( "--index_rate", type=float, help=index_rate_description, choices=[(i / 10) for i in range(11)], default=0.3, ) volume_envelope_description = "Control the blending of the output's volume envelope. A value of 1 means the output envelope is fully used." infer_parser.add_argument( "--volume_envelope", type=float, help=volume_envelope_description, choices=[(i / 10) for i in range(11)], default=1, ) protect_description = "Protect consonants and breathing sounds from artifacts. A value of 0.5 offers the strongest protection, while lower values may reduce the protection level but potentially mitigate the indexing effect." infer_parser.add_argument( "--protect", type=float, help=protect_description, choices=[(i / 10) for i in range(6)], default=0.33, ) hop_length_description = "Only applicable for the Crepe pitch extraction method. Determines the time it takes for the system to react to a significant pitch change. Smaller values require more processing time but can lead to better pitch accuracy." infer_parser.add_argument( "--hop_length", type=int, help=hop_length_description, choices=range(1, 513), default=128, ) f0_method_description = "Choose the pitch extraction algorithm for the conversion. 'rmvpe' is the default and generally recommended." infer_parser.add_argument( "--f0_method", type=str, help=f0_method_description, choices=[ "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="Full path to the input audio file.", required=True, ) infer_parser.add_argument( "--output_path", type=str, help="Full path to the output audio file.", required=True, ) pth_path_description = "Full path to the RVC model file (.pth)." infer_parser.add_argument( "--pth_path", type=str, help=pth_path_description, required=True ) index_path_description = "Full path to the index file (.index)." infer_parser.add_argument( "--index_path", type=str, help=index_path_description, required=True ) split_audio_description = "Split the audio into smaller segments before inference. This can improve the quality of the output for longer audio files." infer_parser.add_argument( "--split_audio", type=lambda x: bool(strtobool(x)), choices=[True, False], help=split_audio_description, default=False, ) f0_autotune_description = "Apply a light autotune to the inferred audio. Particularly useful for singing voice conversions." infer_parser.add_argument( "--f0_autotune", type=lambda x: bool(strtobool(x)), choices=[True, False], help=f0_autotune_description, default=False, ) clean_audio_description = "Clean the output audio using noise reduction algorithms. Recommended for speech conversions." infer_parser.add_argument( "--clean_audio", type=lambda x: bool(strtobool(x)), choices=[True, False], help=clean_audio_description, default=False, ) clean_strength_description = "Adjust the intensity of the audio cleaning process. Higher values result in stronger cleaning, but may lead to a more compressed sound." infer_parser.add_argument( "--clean_strength", type=float, help=clean_strength_description, choices=[(i / 10) for i in range(11)], default=0.7, ) export_format_description = "Select the desired output audio format." infer_parser.add_argument( "--export_format", type=str, help=export_format_description, choices=["WAV", "MP3", "FLAC", "OGG", "M4A"], default="WAV", ) embedder_model_description = ( "Choose the model used for generating speaker embeddings." ) infer_parser.add_argument( "--embedder_model", type=str, help=embedder_model_description, choices=[ "contentvec", "japanese-hubert-base", "chinese-hubert-large", "custom", ], default="contentvec", ) embedder_model_custom_description = "Specify the path to a custom model for speaker embedding. Only applicable if 'embedder_model' is set to 'custom'." infer_parser.add_argument( "--embedder_model_custom", type=str, help=embedder_model_custom_description, default=None, ) upscale_audio_description = "Upscale the input audio to a higher quality before processing. This can improve the overall quality of the output, especially for low-quality input audio." infer_parser.add_argument( "--upscale_audio", type=lambda x: bool(strtobool(x)), choices=[True, False], help=upscale_audio_description, default=False, ) f0_file_description = "Full path to an external F0 file (.f0). This allows you to use pre-computed pitch values for the input audio." infer_parser.add_argument( "--f0_file", type=str, help=f0_file_description, default=None, ) # Parser for 'batch_infer' mode batch_infer_parser = subparsers.add_parser( "batch_infer", help="Run batch inference", ) batch_infer_parser.add_argument( "--pitch", type=int, help=pitch_description, choices=range(-24, 25), default=0, ) batch_infer_parser.add_argument( "--filter_radius", type=int, help=filter_radius_description, choices=range(11), default=3, ) batch_infer_parser.add_argument( "--index_rate", type=float, help=index_rate_description, choices=[(i / 10) for i in range(11)], default=0.3, ) batch_infer_parser.add_argument( "--volume_envelope", type=float, help=volume_envelope_description, choices=[(i / 10) for i in range(11)], default=1, ) batch_infer_parser.add_argument( "--protect", type=float, help=protect_description, choices=[(i / 10) for i in range(6)], default=0.33, ) batch_infer_parser.add_argument( "--hop_length", type=int, help=hop_length_description, choices=range(1, 513), default=128, ) batch_infer_parser.add_argument( "--f0_method", type=str, help=f0_method_description, choices=[ "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="Path to the folder containing input audio files.", required=True, ) batch_infer_parser.add_argument( "--output_folder", type=str, help="Path to the folder for saving output audio files.", required=True, ) batch_infer_parser.add_argument( "--pth_path", type=str, help=pth_path_description, required=True ) batch_infer_parser.add_argument( "--index_path", type=str, help=index_path_description, required=True ) batch_infer_parser.add_argument( "--split_audio", type=lambda x: bool(strtobool(x)), choices=[True, False], help=split_audio_description, default=False, ) batch_infer_parser.add_argument( "--f0_autotune", type=lambda x: bool(strtobool(x)), choices=[True, False], help=f0_autotune_description, default=False, ) batch_infer_parser.add_argument( "--clean_audio", type=lambda x: bool(strtobool(x)), choices=[True, False], help=clean_audio_description, default=False, ) batch_infer_parser.add_argument( "--clean_strength", type=float, help=clean_strength_description, choices=[(i / 10) for i in range(11)], default=0.7, ) batch_infer_parser.add_argument( "--export_format", type=str, help=export_format_description, choices=["WAV", "MP3", "FLAC", "OGG", "M4A"], default="WAV", ) batch_infer_parser.add_argument( "--embedder_model", type=str, help=embedder_model_description, choices=[ "contentvec", "japanese-hubert-base", "chinese-hubert-large", "custom", ], default="contentvec", ) batch_infer_parser.add_argument( "--embedder_model_custom", type=str, help=embedder_model_custom_description, default=None, ) batch_infer_parser.add_argument( "--upscale_audio", type=lambda x: bool(strtobool(x)), choices=[True, False], help=upscale_audio_description, default=False, ) batch_infer_parser.add_argument( "--f0_file", type=str, help=f0_file_description, default=None, ) # Parser for 'tts' mode tts_parser = subparsers.add_parser("tts", help="Run TTS inference") tts_parser.add_argument( "--tts_text", type=str, help="Text to be synthesized", required=True ) tts_parser.add_argument( "--tts_voice", type=str, help="Voice to be used for TTS synthesis.", choices=locales, required=True, ) tts_parser.add_argument( "--tts_rate", type=int, help="Control the speaking rate of the TTS. Values range from -100 (slower) to 100 (faster).", choices=range(-100, 101), default=0, ) tts_parser.add_argument( "--pitch", type=int, help=pitch_description, choices=range(-24, 25), default=0, ) tts_parser.add_argument( "--filter_radius", type=int, help=filter_radius_description, choices=range(11), default=3, ) tts_parser.add_argument( "--index_rate", type=float, help=index_rate_description, choices=[(i / 10) for i in range(11)], default=0.3, ) tts_parser.add_argument( "--volume_envelope", type=float, help=volume_envelope_description, choices=[(i / 10) for i in range(11)], default=1, ) tts_parser.add_argument( "--protect", type=float, help=protect_description, choices=[(i / 10) for i in range(6)], default=0.33, ) tts_parser.add_argument( "--hop_length", type=int, help=hop_length_description, choices=range(1, 513), default=128, ) tts_parser.add_argument( "--f0_method", type=str, help=f0_method_description, choices=[ "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="Full path to save the synthesized TTS audio.", required=True, ) tts_parser.add_argument( "--output_rvc_path", type=str, help="Full path to save the voice-converted audio using the synthesized TTS.", required=True, ) tts_parser.add_argument( "--pth_path", type=str, help=pth_path_description, required=True ) tts_parser.add_argument( "--index_path", type=str, help=index_path_description, required=True ) tts_parser.add_argument( "--split_audio", type=lambda x: bool(strtobool(x)), choices=[True, False], help=split_audio_description, default=False, ) tts_parser.add_argument( "--f0_autotune", type=lambda x: bool(strtobool(x)), choices=[True, False], help=f0_autotune_description, default=False, ) tts_parser.add_argument( "--clean_audio", type=lambda x: bool(strtobool(x)), choices=[True, False], help=clean_audio_description, default=False, ) tts_parser.add_argument( "--clean_strength", type=float, help=clean_strength_description, choices=[(i / 10) for i in range(11)], default=0.7, ) tts_parser.add_argument( "--export_format", type=str, help=export_format_description, choices=["WAV", "MP3", "FLAC", "OGG", "M4A"], default="WAV", ) tts_parser.add_argument( "--embedder_model", type=str, help=embedder_model_description, choices=[ "contentvec", "japanese-hubert-base", "chinese-hubert-large", "custom", ], default="contentvec", ) tts_parser.add_argument( "--embedder_model_custom", type=str, help=embedder_model_custom_description, default=None, ) tts_parser.add_argument( "--upscale_audio", type=lambda x: bool(strtobool(x)), choices=[True, False], help=upscale_audio_description, default=False, ) tts_parser.add_argument( "--f0_file", type=str, help=f0_file_description, default=None, ) # Parser for 'preprocess' mode preprocess_parser = subparsers.add_parser( "preprocess", help="Preprocess a dataset for training." ) preprocess_parser.add_argument( "--model_name", type=str, help="Name of the model to be trained.", required=True ) preprocess_parser.add_argument( "--dataset_path", type=str, help="Path to the dataset directory.", required=True ) preprocess_parser.add_argument( "--sample_rate", type=int, help="Target sampling rate for the audio data.", choices=[32000, 40000, 48000], required=True, ) preprocess_parser.add_argument( "--cpu_cores", type=int, help="Number of CPU cores to use for preprocessing.", choices=range(1, 65), ) # Parser for 'extract' mode extract_parser = subparsers.add_parser( "extract", help="Extract features from a dataset." ) extract_parser.add_argument( "--model_name", type=str, help="Name of the model.", required=True ) extract_parser.add_argument( "--rvc_version", type=str, help="Version of the RVC model ('v1' or 'v2').", choices=["v1", "v2"], default="v2", ) extract_parser.add_argument( "--f0_method", type=str, help="Pitch extraction method to use.", choices=[ "crepe", "crepe-tiny", "rmvpe", ], default="rmvpe", ) extract_parser.add_argument( "--pitch_guidance", type=lambda x: bool(strtobool(x)), choices=[True, False], help="Enable or disable pitch guidance during feature extraction.", default=True, ) extract_parser.add_argument( "--hop_length", type=int, help="Hop length for feature extraction. Only applicable for Crepe pitch extraction.", choices=range(1, 513), default=128, ) extract_parser.add_argument( "--cpu_cores", type=int, help="Number of CPU cores to use for feature extraction (optional).", choices=range(1, 65), default=None, ) extract_parser.add_argument( "--gpu", type=int, help="GPU device to use for feature extraction (optional).", default="-", ) extract_parser.add_argument( "--sample_rate", type=int, help="Target sampling rate for the audio data.", choices=[32000, 40000, 48000], required=True, ) extract_parser.add_argument( "--embedder_model", type=str, help=embedder_model_description, choices=[ "contentvec", "japanese-hubert-base", "chinese-hubert-large", "custom", ], default="contentvec", ) extract_parser.add_argument( "--embedder_model_custom", type=str, help=embedder_model_custom_description, default=None, ) # Parser for 'train' mode train_parser = subparsers.add_parser("train", help="Train an RVC model.") train_parser.add_argument( "--model_name", type=str, help="Name of the model to be trained.", required=True ) train_parser.add_argument( "--rvc_version", type=str, help="Version of the RVC model to train ('v1' or 'v2').", choices=["v1", "v2"], default="v2", ) train_parser.add_argument( "--save_every_epoch", type=int, help="Save the model every specified number of epochs.", choices=range(1, 101), required=True, ) train_parser.add_argument( "--save_only_latest", type=lambda x: bool(strtobool(x)), choices=[True, False], help="Save only the latest model checkpoint.", default=False, ) train_parser.add_argument( "--save_every_weights", type=lambda x: bool(strtobool(x)), choices=[True, False], help="Save model weights every epoch.", default=True, ) train_parser.add_argument( "--total_epoch", type=int, help="Total number of epochs to train for.", choices=range(1, 10001), default=1000, ) train_parser.add_argument( "--sample_rate", type=int, help="Sampling rate of the training data.", choices=[32000, 40000, 48000], required=True, ) train_parser.add_argument( "--batch_size", type=int, help="Batch size for training.", choices=range(1, 51), default=8, ) train_parser.add_argument( "--gpu", type=str, help="GPU device to use for training (e.g., '0').", default="0", ) train_parser.add_argument( "--pitch_guidance", type=lambda x: bool(strtobool(x)), choices=[True, False], help="Enable or disable pitch guidance during training.", default=True, ) train_parser.add_argument( "--pretrained", type=lambda x: bool(strtobool(x)), choices=[True, False], help="Use a pretrained model for initialization.", default=True, ) train_parser.add_argument( "--custom_pretrained", type=lambda x: bool(strtobool(x)), choices=[True, False], help="Use a custom pretrained model.", default=False, ) train_parser.add_argument( "--g_pretrained_path", type=str, nargs="?", default=None, help="Path to the pretrained generator model file.", ) train_parser.add_argument( "--d_pretrained_path", type=str, nargs="?", default=None, help="Path to the pretrained discriminator model file.", ) train_parser.add_argument( "--overtraining_detector", type=lambda x: bool(strtobool(x)), choices=[True, False], help="Enable overtraining detection.", default=False, ) train_parser.add_argument( "--overtraining_threshold", type=int, help="Threshold for overtraining detection.", choices=range(1, 101), default=50, ) train_parser.add_argument( "--sync_graph", type=lambda x: bool(strtobool(x)), choices=[True, False], help="Enable graph synchronization for distributed training.", default=False, ) train_parser.add_argument( "--cache_data_in_gpu", type=lambda x: bool(strtobool(x)), choices=[True, False], help="Cache training data in GPU memory.", default=False, ) # Parser for 'index' mode index_parser = subparsers.add_parser( "index", help="Generate an index file for an RVC model." ) index_parser.add_argument( "--model_name", type=str, help="Name of the model.", required=True ) index_parser.add_argument( "--rvc_version", type=str, help="Version of the RVC model ('v1' or 'v2').", choices=["v1", "v2"], default="v2", ) index_parser.add_argument( "--index_algorithm", type=str, choices=["Auto", "Faiss", "KMeans"], help="Choose the method for generating the index file.", default="Auto", required=False, ) # Parser for 'model_extract' mode model_extract_parser = subparsers.add_parser( "model_extract", help="Extract a specific epoch from a trained model." ) model_extract_parser.add_argument( "--pth_path", type=str, help="Path to the main .pth model file.", required=True ) model_extract_parser.add_argument( "--model_name", type=str, help="Name of the model.", required=True ) model_extract_parser.add_argument( "--sample_rate", type=int, help="Sampling rate of the extracted model.", choices=[32000, 40000, 48000], required=True, ) model_extract_parser.add_argument( "--pitch_guidance", type=lambda x: bool(strtobool(x)), choices=[True, False], help="Enable or disable pitch guidance for the extracted model.", required=True, ) model_extract_parser.add_argument( "--rvc_version", type=str, help="Version of the extracted RVC model ('v1' or 'v2').", choices=["v1", "v2"], default="v2", ) model_extract_parser.add_argument( "--epoch", type=int, help="Epoch number to extract from the model.", choices=range(1, 10001), required=True, ) model_extract_parser.add_argument( "--step", type=str, help="Step number to extract from the model (optional).", required=False, ) # Parser for 'model_information' mode model_information_parser = subparsers.add_parser( "model_information", help="Display information about a trained model." ) model_information_parser.add_argument( "--pth_path", type=str, help="Path to the .pth model file.", required=True ) # Parser for 'model_blender' mode model_blender_parser = subparsers.add_parser( "model_blender", help="Fuse two RVC models together." ) model_blender_parser.add_argument( "--model_name", type=str, help="Name of the new fused model.", required=True ) model_blender_parser.add_argument( "--pth_path_1", type=str, help="Path to the first .pth model file.", required=True, ) model_blender_parser.add_argument( "--pth_path_2", type=str, help="Path to the second .pth model file.", required=True, ) model_blender_parser.add_argument( "--ratio", type=float, help="Ratio for blending the two models (0.0 to 1.0).", choices=[(i / 10) for i in range(11)], default=0.5, ) # Parser for 'tensorboard' mode subparsers.add_parser( "tensorboard", help="Launch TensorBoard for monitoring training progress." ) # Parser for 'download' mode download_parser = subparsers.add_parser( "download", help="Download a model from a provided link." ) download_parser.add_argument( "--model_link", type=str, help="Direct link to the model file.", required=True ) # Parser for 'prerequisites' mode prerequisites_parser = subparsers.add_parser( "prerequisites", help="Install prerequisites for RVC." ) prerequisites_parser.add_argument( "--pretraineds_v1", type=lambda x: bool(strtobool(x)), choices=[True, False], default=True, help="Download pretrained models for RVC v1.", ) prerequisites_parser.add_argument( "--pretraineds_v2", type=lambda x: bool(strtobool(x)), choices=[True, False], default=True, help="Download pretrained models for RVC v2.", ) prerequisites_parser.add_argument( "--models", type=lambda x: bool(strtobool(x)), choices=[True, False], default=True, help="Download additional models.", ) prerequisites_parser.add_argument( "--exe", type=lambda x: bool(strtobool(x)), choices=[True, False], default=True, help="Download required executables.", ) # Parser for 'audio_analyzer' mode audio_analyzer = subparsers.add_parser( "audio_analyzer", help="Analyze an audio file." ) audio_analyzer.add_argument( "--input_path", type=str, help="Path to the input audio file.", required=True ) # Parser for 'api' mode api_parser = subparsers.add_parser("api", help="Start the RVC API server.") api_parser.add_argument( "--host", type=str, help="Host address for the API server.", default="127.0.0.1" ) api_parser.add_argument( "--port", type=int, help="Port for the API server.", 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( pitch=args.pitch, filter_radius=args.filter_radius, index_rate=args.index_rate, volume_envelope=args.volume_envelope, protect=args.protect, hop_length=args.hop_length, f0_method=args.f0_method, input_path=args.input_path, output_path=args.output_path, pth_path=args.pth_path, index_path=args.index_path, split_audio=args.split_audio, f0_autotune=args.f0_autotune, clean_audio=args.clean_audio, clean_strength=args.clean_strength, export_format=args.export_format, embedder_model=args.embedder_model, embedder_model_custom=args.embedder_model_custom, upscale_audio=args.upscale_audio, f0_file=args.f0_file, ) elif args.mode == "batch_infer": run_batch_infer_script( pitch=args.pitch, filter_radius=args.filter_radius, index_rate=args.index_rate, volume_envelope=args.volume_envelope, protect=args.protect, hop_length=args.hop_length, f0_method=args.f0_method, input_folder=args.input_folder, output_folder=args.output_folder, pth_path=args.pth_path, index_path=args.index_path, split_audio=args.split_audio, f0_autotune=args.f0_autotune, clean_audio=args.clean_audio, clean_strength=args.clean_strength, export_format=args.export_format, embedder_model=args.embedder_model, embedder_model_custom=args.embedder_model_custom, upscale_audio=args.upscale_audio, f0_file=args.f0_file, ) elif args.mode == "tts": run_tts_script( tts_text=args.tts_text, tts_voice=args.tts_voice, tts_rate=args.tts_rate, pitch=args.pitch, filter_radius=args.filter_radius, index_rate=args.index_rate, volume_envelope=args.volume_envelope, protect=args.protect, hop_length=args.hop_length, f0_method=args.f0_method, input_path=args.input_path, output_path=args.output_path, pth_path=args.pth_path, index_path=args.index_path, split_audio=args.split_audio, f0_autotune=args.f0_autotune, clean_audio=args.clean_audio, clean_strength=args.clean_strength, export_format=args.export_format, embedder_model=args.embedder_model, embedder_model_custom=args.embedder_model_custom, upscale_audio=args.upscale_audio, f0_file=args.f0_file, ) elif args.mode == "preprocess": run_preprocess_script( model_name=args.model_name, dataset_path=args.dataset_path, sample_rate=args.sample_rate, cpu_cores=args.cpu_cores, ) elif args.mode == "extract": run_extract_script( model_name=args.model_name, rvc_version=args.rvc_version, f0_method=args.f0_method, pitch_guidance=args.pitch_guidance, hop_length=args.hop_length, cpu_cores=args.cpu_cores, gpu=args.gpu, sample_rate=args.sample_rate, embedder_model=args.embedder_model, embedder_model_custom=args.embedder_model_custom, ) elif args.mode == "train": run_train_script( model_name=args.model_name, rvc_version=args.rvc_version, save_every_epoch=args.save_every_epoch, save_only_latest=args.save_only_latest, save_every_weights=args.save_every_weights, total_epoch=args.total_epoch, sample_rate=args.sample_rate, batch_size=args.batch_size, gpu=args.gpu, pitch_guidance=args.pitch_guidance, overtraining_detector=args.overtraining_detector, overtraining_threshold=args.overtraining_threshold, pretrained=args.pretrained, custom_pretrained=args.custom_pretrained, sync_graph=args.sync_graph, cache_data_in_gpu=args.cache_data_in_gpu, g_pretrained_path=args.g_pretrained_path, d_pretrained_path=args.d_pretrained_path, ) elif args.mode == "index": run_index_script( model_name=args.model_name, rvc_version=args.rvc_version, index_algorithm=args.index_algorithm, ) elif args.mode == "model_extract": run_model_extract_script( pth_path=args.pth_path, model_name=args.model_name, sample_rate=args.sample_rate, pitch_guidance=args.pitch_guidance, rvc_version=args.rvc_version, epoch=args.epoch, step=args.step, ) elif args.mode == "model_information": run_model_information_script( pth_path=args.pth_path, ) elif args.mode == "model_blender": run_model_blender_script( model_name=args.model_name, pth_path_1=args.pth_path_1, pth_path_2=args.pth_path_2, ratio=args.ratio, ) elif args.mode == "tensorboard": run_tensorboard_script() elif args.mode == "download": run_download_script( model_link=args.model_link, ) elif args.mode == "prerequisites": run_prerequisites_script( pretraineds_v1=args.pretraineds_v1, pretraineds_v2=args.pretraineds_v2, models=args.models, exe=args.exe, ) elif args.mode == "audio_analyzer": run_audio_analyzer_script( input_path=args.input_path, ) elif args.mode == "api": run_api_script( ip=args.host, port=args.port, ) except Exception as error: print(f"An error occurred during execution: {error}") import traceback traceback.print_exc() if __name__ == "__main__": main()