Spaces:
Runtime error
Runtime error
| from original import * | |
| import shutil, glob | |
| from easyfuncs import download_from_url, CachedModels, whisperspeak, whisperspeak_on, stereo_process, sr_process | |
| os.makedirs("dataset",exist_ok=True) | |
| os.makedirs("audios",exist_ok=True) | |
| model_library = CachedModels() | |
| with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue="zinc")) as app: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.HTML("<img src='file/a.png' alt='image'>") | |
| with gr.Column(): | |
| gr.HTML("<a href='https://ko-fi.com/rejekts' target='_blank'><img src='file/kofi_button.png' alt='🤝 Support Me'></a>") | |
| 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.Row(): | |
| with gr.Column(): | |
| with gr.Tabs(): | |
| with gr.TabItem("Upload"): | |
| dropbox = gr.File(label="Drop your audio here & hit the Reload button.") | |
| with gr.TabItem("Record"): | |
| record_button=gr.Microphone(label="OR Record audio.", type="filepath") | |
| with gr.TabItem("TTS (experimental)", visible=False if whisperspeak_on is None else True): | |
| with gr.Row(): | |
| tts_text = gr.Textbox(label="Text to Speech", placeholder="Enter text to convert to speech") | |
| with gr.Row(): | |
| tts_lang = gr.Radio(choices=["en","es","it","pt"],label="",value="en") | |
| with gr.Row(): | |
| tts_button = gr.Button(value="Speak", variant="primary") | |
| 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(): | |
| input_player = gr.Audio(label="Input",type="numpy") | |
| input_audio0.change( | |
| inputs=[input_audio0], | |
| outputs=[input_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]) | |
| tts_button.click( | |
| fn=whisperspeak, | |
| inputs=[tts_text,tts_lang], | |
| outputs=[input_audio0], | |
| show_progress=True) | |
| tts_button.click( | |
| fn=lambda: {"choices":paths_for_files('audios'),"__type__":"update"}, | |
| inputs=[], | |
| 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, | |
| ) | |
| output_player = gr.Audio(label="Output",interactive=False) | |
| 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: | |
| try: vc.get_vc(voice_model.value,protect0,protect0) | |
| except: pass | |
| with gr.Accordion("Processing Tools (Experimental)", open=True): | |
| audio_choice = gr.Radio(choices=["Input", "Output"], value="Output", label="Source",interactive=True) | |
| with gr.Column(): | |
| stereo_button = gr.Button(value="Stereo", variant="primary") | |
| stereo_button.click( | |
| fn=stereo_process, | |
| inputs=[input_player,output_player,audio_choice], | |
| outputs=[output_player], | |
| preprocess=True, | |
| ) | |
| with gr.Column(): | |
| sr_button = gr.Button(value="SuperResolution", variant="primary") | |
| sr_button.click( | |
| fn=sr_process, | |
| inputs=[input_player,output_player,audio_choice], | |
| outputs=[output_player], | |
| preprocess=True, | |
| ) | |
| 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, output_player], | |
| 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=False | |
| ) | |
| 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, | |
| ) | |
| dataset_folder = gr.Textbox( | |
| label="dataset folder", value='dataset' | |
| ) | |
| easy_uploader = gr.Files(label="Drop your audio files here",file_types=['audio']) | |
| but1 = gr.Button("1. Process", variant="primary") | |
| 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 | |
| ) | |
| but1.click( | |
| preprocess_dataset, | |
| [dataset_folder, training_name, sr2, np7], | |
| [info1], | |
| api_name="train_preprocess", | |
| ) | |
| 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, | |
| ) | |
| but2 = gr.Button("2. Extract Features", variant="primary") | |
| info2 = gr.Textbox(label="Information", value="", max_lines=8) | |
| f0method8.change( | |
| fn=change_f0_method, | |
| inputs=[f0method8], | |
| outputs=[gpus_rmvpe], | |
| ) | |
| but2.click( | |
| extract_f0_feature, | |
| [ | |
| gpus6, | |
| np7, | |
| f0method8, | |
| if_f0_3, | |
| training_name, | |
| version19, | |
| gpus_rmvpe, | |
| ], | |
| [info2], | |
| api_name="train_extract_f0_feature", | |
| ) | |
| 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, | |
| ) | |
| 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) | |
| 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(max_size=20).launch(share=True,allowed_paths=["a.png","kofi_button.png"],show_error=True) | |
| else: | |
| app.queue(max_size=1022).launch( | |
| server_name="0.0.0.0", | |
| inbrowser=not config.noautoopen, | |
| server_port=config.listen_port, | |
| quiet=True, | |
| ) | |