Create demo.py

demo.py

@@ -0,0 +1,479 @@
+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("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,
+        )