from original import * import shutil, glob from infer_rvc_python import BaseLoader from easyfuncs import download_from_url, CachedModels os.makedirs("dataset",exist_ok=True) model_library = CachedModels() # Initialize the converter converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None) import gradio as gr import os from infer_rvc_python import BaseLoader # Initialize the converter converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None) def apply_conversion(audio_files, file_model, file_index, pitch_lvl, pitch_algo): converter.apply_conf( tag=file_model, file_model=file_model, pitch_algo=pitch_algo, pitch_lvl=int(pitch_lvl), # pitch_lvl should be an integer file_index=file_index, index_influence=0.66, respiration_median_filtering=3, envelope_ratio=0.25, consonant_breath_protection=0.33 ) speakers_list = [file_model] # It should be a list if multiple speakers are possible result = converter( audio_files, speakers_list, overwrite=False, parallel_workers=4 ) output_path = "output_audio.wav" # Assuming `result` is an array of audio data, save it to a file result[0].export(output_path, format="wav") # This is an example, modify as needed for your data type return output_path with gr.Blocks(title="Easy 🔊 GUI",theme="Hev832/Applio") as app: with gr.Row(): gr.HTML("image") with gr.Tabs(): with gr.TabItem("Inference"): with gr.Row(): voice_model = gr.Dropdown(label="Model Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True) refresh_button = gr.Button("Refresh", variant="primary") spk_item = gr.Slider( minimum=0, maximum=2333, step=1, label="Speaker ID", value=0, visible=False, interactive=True, ) vc_transform0 = gr.Number( label="Pitch", value=0 ) but0 = gr.Button(value="Convert", variant="primary") with gr.TabItem("Inference v2!"): audio_files_input = gr.Audio(label="your audios") file_model_input = gr.Dropdown(label="Model Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True) file_index_input = gr.Dropdown(label="Change Index",choices=sorted(index_paths),interactive=True,value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else '') pitch_lvl_input = gr.Number(label="Pitch",value=0) pitch_algo_input = gr.Dropdown(["pm", "harvest", "crepe", "rmvpe", "rmvpe+"], label="Pitch Algorithm") submit_button = gr.Button("Convert Audio") output_Audio = gr.Audio(label="Conversion Result") submit_button.click( apply_conversion, inputs=[audio_files_input, file_model_input, file_index_input, pitch_lvl_input, pitch_algo_input], outputs=output_Audio ) with gr.Row(): with gr.Column(): with gr.Row(): dropbox = gr.File(label="Drop your audio here & hit the Reload button.") with gr.Row(): record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath") with gr.Row(): paths_for_files = lambda path:[os.path.abspath(os.path.join(path, f)) for f in os.listdir(path) if os.path.splitext(f)[1].lower() in ('.mp3', '.wav', '.flac', '.ogg')] input_audio0 = gr.Dropdown( label="Input Path", value=paths_for_files('audios')[0] if len(paths_for_files('audios')) > 0 else '', choices=paths_for_files('audios'), # Only show absolute paths for audio files ending in .mp3, .wav, .flac or .ogg allow_custom_value=True ) with gr.Row(): audio_player = gr.Audio() input_audio0.change( inputs=[input_audio0], outputs=[audio_player], fn=lambda path: {"value":path,"__type__":"update"} if os.path.exists(path) else None ) record_button.stop_recording( fn=lambda audio:audio, #TODO save wav lambda inputs=[record_button], outputs=[input_audio0]) dropbox.upload( fn=lambda audio:audio.name, inputs=[dropbox], outputs=[input_audio0]) with gr.Column(): with gr.Accordion("Change Index", open=False): file_index2 = gr.Dropdown( label="Change Index", choices=sorted(index_paths), interactive=True, value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else '' ) index_rate1 = gr.Slider( minimum=0, maximum=1, label="Index Strength", value=0.5, interactive=True, ) vc_output2 = gr.Audio(label="Output") with gr.Accordion("General Settings", open=False): f0method0 = gr.Radio( label="Method", choices=["pm", "harvest", "crepe", "rmvpe"] if config.dml == False else ["pm", "harvest", "rmvpe"], value="rmvpe", interactive=True, ) filter_radius0 = gr.Slider( minimum=0, maximum=7, label="Breathiness Reduction (Harvest only)", value=3, step=1, interactive=True, ) resample_sr0 = gr.Slider( minimum=0, maximum=48000, label="Resample", value=0, step=1, interactive=True, visible=False ) rms_mix_rate0 = gr.Slider( minimum=0, maximum=1, label="Volume Normalization", value=0, interactive=True, ) protect0 = gr.Slider( minimum=0, maximum=0.5, label="Breathiness Protection (0 is enabled, 0.5 is disabled)", value=0.33, step=0.01, interactive=True, ) if voice_model != None: vc.get_vc(voice_model.value,protect0,protect0) file_index1 = gr.Textbox( label="Index Path", interactive=True, visible=False#Not used here ) refresh_button.click( fn=change_choices, inputs=[], outputs=[voice_model, file_index2], api_name="infer_refresh", ) refresh_button.click( fn=lambda:{"choices":paths_for_files('audios'),"__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac' inputs=[], outputs = [input_audio0], ) refresh_button.click( fn=lambda:{"value":paths_for_files('audios')[0],"__type__":"update"} if len(paths_for_files('audios')) > 0 else {"value":"","__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac' inputs=[], outputs = [input_audio0], ) with gr.Row(): f0_file = gr.File(label="F0 Path", visible=False) with gr.Row(): vc_output1 = gr.Textbox(label="Information", placeholder="Welcome!",visible=False) but0.click( vc.vc_single, [ spk_item, input_audio0, vc_transform0, f0_file, f0method0, file_index1, file_index2, index_rate1, filter_radius0, resample_sr0, rms_mix_rate0, protect0, ], [vc_output1, vc_output2], api_name="infer_convert", ) voice_model.change( fn=vc.get_vc, inputs=[voice_model, protect0, protect0], outputs=[spk_item, protect0, protect0, file_index2, file_index2], api_name="infer_change_voice", ) with gr.TabItem("Download Models"): with gr.Row(): url_input = gr.Textbox(label="URL to model", value="",placeholder="https://...", scale=6) name_output = gr.Textbox(label="Save as", value="",placeholder="MyModel",scale=2) url_download = gr.Button(value="Download Model",scale=2) url_download.click( inputs=[url_input,name_output], outputs=[url_input], fn=download_from_url, ) with gr.Row(): model_browser = gr.Dropdown(choices=list(model_library.models.keys()),label="OR Search Models (Quality UNKNOWN)",scale=5) download_from_browser = gr.Button(value="Get",scale=2) download_from_browser.click( inputs=[model_browser], outputs=[model_browser], fn=lambda model: download_from_url(model_library.models[model],model), ) with gr.TabItem("Train"): with gr.Row(): with gr.Column(): training_name = gr.Textbox(label="Name your model", value="My-Voice",placeholder="My-Voice") np7 = gr.Slider( minimum=0, maximum=config.n_cpu, step=1, label="Number of CPU processes used to extract pitch features", value=int(np.ceil(config.n_cpu / 1.5)), interactive=True, ) sr2 = gr.Radio( label="Sampling Rate", choices=["40k", "32k"], value="32k", interactive=True, visible=True ) if_f0_3 = gr.Radio( label="Will your model be used for singing? If not, you can ignore this.", choices=[True, False], value=True, interactive=True, visible=False ) version19 = gr.Radio( label="Version", choices=["v1", "v2"], value="v2", interactive=True, visible=False, # this is default ) dataset_folder = gr.Textbox( label="dataset folder", value='dataset' ) easy_uploader = gr.Files(label="Drop your audio files here",file_types=['audio']) #info1 = gr.Textbox(label="Information", value="",visible=True) easy_uploader.upload(inputs=[dataset_folder],outputs=[],fn=lambda folder:os.makedirs(folder,exist_ok=True)) easy_uploader.upload( fn=lambda files,folder: [shutil.copy2(f.name,os.path.join(folder,os.path.split(f.name)[1])) for f in files] if folder != "" else gr.Warning('Please enter a folder name for your dataset'), inputs=[easy_uploader, dataset_folder], outputs=[]) gpus6 = gr.Textbox( label="Enter the GPU numbers to use separated by -, (e.g. 0-1-2)", value=gpus, interactive=True, visible=F0GPUVisible, ) gpu_info9 = gr.Textbox( label="GPU Info", value=gpu_info, visible=F0GPUVisible ) spk_id5 = gr.Slider( minimum=0, maximum=4, step=1, label="Speaker ID", value=0, interactive=True, visible=False ) f0method8.change( fn=change_f0_method, inputs=[f0method8], outputs=[gpus_rmvpe], ) with gr.Column(): f0method8 = gr.Radio( label="F0 extraction method", choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], value="rmvpe_gpu", interactive=True, ) gpus_rmvpe = gr.Textbox( label="GPU numbers to use separated by -, (e.g. 0-1-2)", value="%s-%s" % (gpus, gpus), interactive=True, visible=F0GPUVisible, ) with gr.Column(): total_epoch11 = gr.Slider( minimum=2, maximum=1000, step=1, label="Epochs (more epochs may improve quality but takes longer)", value=150, interactive=True, ) but1 = gr.Button("1. Process", variant="primary") but2 = gr.Button("2. Extract Features", variant="primary") but4 = gr.Button("3. Train Index", variant="primary") but3 = gr.Button("4. Train Model", variant="primary") info3 = gr.Textbox(label="Information", value="", max_lines=10) with gr.Accordion(label="General Settings", open=False): gpus16 = gr.Textbox( label="GPUs separated by -, (e.g. 0-1-2)", value="0", interactive=True, visible=True ) save_epoch10 = gr.Slider( minimum=1, maximum=50, step=1, label="Weight Saving Frequency", value=25, interactive=True, ) batch_size12 = gr.Slider( minimum=1, maximum=40, step=1, label="Batch Size", value=default_batch_size, interactive=True, ) if_save_latest13 = gr.Radio( label="Only save the latest model", choices=["yes", "no"], value="yes", interactive=True, visible=False ) if_cache_gpu17 = gr.Radio( label="If your dataset is UNDER 10 minutes, cache it to train faster", choices=["yes", "no"], value="no", interactive=True, ) if_save_every_weights18 = gr.Radio( label="Save small model at every save point", choices=["yes", "no"], value="yes", interactive=True, ) with gr.Accordion(label="Change pretrains", open=False): pretrained = lambda sr, letter: [os.path.abspath(os.path.join('assets/pretrained_v2', file)) for file in os.listdir('assets/pretrained_v2') if file.endswith('.pth') and sr in file and letter in file] pretrained_G14 = gr.Dropdown( label="pretrained G", # Get a list of all pretrained G model files in assets/pretrained_v2 that end with .pth choices = pretrained(sr2.value, 'G'), value=pretrained(sr2.value, 'G')[0] if len(pretrained(sr2.value, 'G')) > 0 else '', interactive=True, visible=True ) pretrained_D15 = gr.Dropdown( label="pretrained D", choices = pretrained(sr2.value, 'D'), value= pretrained(sr2.value, 'D')[0] if len(pretrained(sr2.value, 'G')) > 0 else '', visible=True, interactive=True ) with gr.Row(): download_model = gr.Button('5.Download Model') with gr.Row(): model_files = gr.Files(label='Your Model and Index file can be downloaded here:') download_model.click( fn=lambda name: os.listdir(f'assets/weights/{name}') + glob.glob(f'logs/{name.split(".")[0]}/added_*.index'), inputs=[training_name], outputs=[model_files, info3]) with gr.Row(): sr2.change( change_sr2, [sr2, if_f0_3, version19], [pretrained_G14, pretrained_D15], ) version19.change( change_version19, [sr2, if_f0_3, version19], [pretrained_G14, pretrained_D15, sr2], ) if_f0_3.change( change_f0, [if_f0_3, sr2, version19], [f0method8, pretrained_G14, pretrained_D15], ) with gr.Row(): but5 = gr.Button("1 Click Training", variant="primary", visible=False) but1.click( preprocess_dataset, [dataset_folder, training_name, sr2, np7], [info3], api_name="train_preprocess", ) but2.click( extract_f0_feature, [ gpus6, np7, f0method8, if_f0_3, training_name, version19, gpus_rmvpe, ], info3, api_name="train_extract_f0_feature", ) but3.click( click_train, [ training_name, sr2, if_f0_3, spk_id5, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17, if_save_every_weights18, version19, ], info3, api_name="train_start", ) but4.click(train_index, [training_name, version19], info3) but5.click( train1key, [ training_name, sr2, if_f0_3, dataset_folder, spk_id5, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17, if_save_every_weights18, version19, gpus_rmvpe, ], info3, api_name="train_start_all", ) if config.iscolab: app.queue(concurrency_count=511, max_size=1022).launch(share=True) else: app.queue(concurrency_count=511, max_size=1022).launch( server_name="0.0.0.0", inbrowser=not config.noautoopen, server_port=config.listen_port, quiet=True, )