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import os |
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import re |
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import random |
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from scipy.io.wavfile import write |
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from scipy.io.wavfile import read |
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import numpy as np |
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import gradio as gr |
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import yt_dlp |
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import subprocess |
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from original import * |
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import shutil, glob, subprocess |
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from easyfuncs import download_from_url, CachedModels, whisperspeak, whisperspeak_on, stereo_process, sr_process |
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os.makedirs("dataset",exist_ok=True) |
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os.makedirs("audios",exist_ok=True) |
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model_library = CachedModels() |
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def roformer_separator(roformer_audio, roformer_output_format="wav", roformer_overlap="4", roformer_segment_size="256"): |
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files_list = [] |
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files_list.clear() |
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directory = "./audios" |
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random_id = str(random.randint(10000, 99999)) |
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pattern = f"{random_id}" |
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os.makedirs("outputs", exist_ok=True) |
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write(f'{random_id}.wav', roformer_audio[0], roformer_audio[1]) |
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full_roformer_model = "model_bs_roformer_ep_317_sdr_12.9755.ckpt" |
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prompt = f"audio-separator {random_id}.wav --model_filename {full_roformer_model} --output_dir=./outputs --output_format={roformer_output_format} --normalization=0.9 --mdxc_overlap={roformer_overlap} --mdxc_segment_size={roformer_segment_size}" |
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os.system(prompt) |
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for file in os.listdir(directory): |
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if re.search(pattern, file): |
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files_list.append(os.path.join(directory, file)) |
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stem1_file = files_list[0] |
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stem2_file = files_list[1] |
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return stem1_file, stem2_file |
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with gr.Blocks(title="🔊 Nex RVC Mobile",theme=gr.themes.Base()) as app: |
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gr.Markdown("# Nex RVC MOBILE GUI") |
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with gr.Tabs(): |
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voice_model = gr.Dropdown(label="AI Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True) |
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refresh_button = gr.Button("Search Again", variant="primary") |
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with gr.TabItem("Inference"): |
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with gr.Row(): |
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spk_item = gr.Slider( |
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minimum=0, |
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maximum=2333, |
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step=1, |
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label="Speaker ID", |
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value=0, |
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visible=False, |
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interactive=True, |
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) |
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vc_transform0 = gr.Number( |
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label="Pitch", |
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value=0 |
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) |
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but0 = gr.Button(value="Convert", variant="primary") |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Tabs(): |
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with gr.TabItem("Upload"): |
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dropbox = gr.File(label="Drop your audio here & hit the Reload button.") |
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with gr.TabItem("Record"): |
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record_button=gr.Microphone(label="OR Record audio.", type="filepath") |
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with gr.TabItem("UVR", visible=False): |
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with gr.Row(): |
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roformer_audio = gr.Audio( |
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label = "Input Audio", |
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type = "numpy", |
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interactive = True |
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) |
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with gr.Accordion("Separation by Link", open=False): |
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with gr.Row(): |
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roformer_link = gr.Textbox( |
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label = "Link", |
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placeholder = "Paste the link here", |
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interactive = True |
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) |
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with gr.Row(): |
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gr.Markdown("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)") |
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with gr.Row(): |
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roformer_download_button = gr.Button( |
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"Download!", |
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variant = "primary" |
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) |
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roformer_download_button.click(download_audio, [roformer_link], [roformer_audio]) |
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with gr.Row(): |
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roformer_button = gr.Button("Separate!", variant = "primary") |
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with gr.Column(): |
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roformer_stem1 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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label = "Stem 1", |
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type = "filepath" |
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) |
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roformer_stem2 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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label = "Stem 2", |
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type = "filepath" |
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) |
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with gr.Row(): |
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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')] |
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input_audio0 = gr.Dropdown( |
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label="Input Path", |
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value=paths_for_files('audios')[0] if len(paths_for_files('audios')) > 0 else '', |
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choices=paths_for_files('audios'), |
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allow_custom_value=True |
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) |
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with gr.Row(): |
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input_player = gr.Audio(label="Input",type="numpy",interactive=False) |
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input_audio0.change( |
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inputs=[input_audio0], |
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outputs=[input_player], |
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fn=lambda path: {"value":path,"__type__":"update"} if os.path.exists(path) else None |
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) |
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record_button.stop_recording( |
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fn=lambda audio:audio, |
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inputs=[record_button], |
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outputs=[input_audio0]) |
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dropbox.upload( |
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fn=lambda audio:audio.name, |
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inputs=[dropbox], |
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outputs=[input_audio0]) |
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roformer_button.click(roformer_separator, [roformer_audio], [roformer_stem1, roformer_stem2]) |
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with gr.Column(): |
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with gr.Accordion("Change Index", open=False): |
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file_index2 = gr.Dropdown( |
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label="Change Index", |
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choices=sorted(index_paths), |
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interactive=True, |
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value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else '' |
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) |
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index_rate1 = gr.Slider( |
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minimum=0, |
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maximum=1, |
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label="Index Strength", |
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value=0.5, |
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interactive=True, |
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) |
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output_player = gr.Audio(label="Output",interactive=False) |
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with gr.Accordion("General Settings", open=False): |
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f0method0 = gr.Radio( |
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label="Method", |
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choices=["pm"], |
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value="pm", |
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interactive=False, |
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visible=False, |
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) |
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filter_radius0 = gr.Slider( |
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minimum=0, |
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maximum=7, |
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label="Breathiness Reduction (Harvest only)", |
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value=3, |
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step=1, |
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interactive=True, |
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) |
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resample_sr0 = gr.Slider( |
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minimum=0, |
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maximum=48000, |
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label="Resample", |
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value=0, |
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step=1, |
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interactive=True, |
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visible=False |
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) |
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rms_mix_rate0 = gr.Slider( |
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minimum=0, |
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maximum=1, |
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label="Volume Normalization", |
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value=0, |
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interactive=True, |
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) |
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protect0 = gr.Slider( |
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minimum=0, |
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maximum=0.5, |
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label="Breathiness Protection (0 is enabled, 0.5 is disabled)", |
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value=0.33, |
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step=0.01, |
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interactive=True, |
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) |
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if voice_model != None: |
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try: vc.get_vc(voice_model.value,protect0,protect0) |
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except: pass |
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file_index1 = gr.Textbox( |
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label="Index Path", |
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interactive=True, |
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visible=False |
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) |
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refresh_button.click( |
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fn=change_choices, |
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inputs=[], |
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outputs=[voice_model, file_index2], |
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api_name="infer_refresh", |
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) |
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refresh_button.click( |
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fn=lambda:{"choices":paths_for_files('audios'),"__type__":"update"}, |
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inputs=[], |
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outputs = [input_audio0], |
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) |
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refresh_button.click( |
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fn=lambda:{"value":paths_for_files('audios')[0],"__type__":"update"} if len(paths_for_files('audios')) > 0 else {"value":"","__type__":"update"}, |
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inputs=[], |
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outputs = [input_audio0], |
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) |
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with gr.Row(): |
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f0_file = gr.File(label="F0 Path", visible=False) |
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with gr.Row(): |
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vc_output1 = gr.Textbox(label="Information", placeholder="Welcome!",visible=False) |
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but0.click( |
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vc.vc_single, |
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[ |
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spk_item, |
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input_audio0, |
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vc_transform0, |
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f0_file, |
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f0method0, |
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file_index1, |
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file_index2, |
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index_rate1, |
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filter_radius0, |
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resample_sr0, |
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rms_mix_rate0, |
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protect0, |
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], |
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[vc_output1, output_player], |
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api_name="infer_convert", |
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) |
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voice_model.change( |
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fn=vc.get_vc, |
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inputs=[voice_model, protect0, protect0], |
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outputs=[spk_item, protect0, protect0, file_index2, file_index2], |
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api_name="infer_change_voice", |
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) |
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with gr.TabItem("Download Models"): |
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with gr.Row(): |
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url_input = gr.Textbox(label="URL to model (i.e. from huggingface)", value="",placeholder="https://...", scale=6) |
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name_output = gr.Textbox(label="Save as (if from hf, you may leave it blank)", value="",placeholder="MyModel",scale=2) |
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url_download = gr.Button(value="Download Model",scale=2) |
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url_download.click( |
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inputs=[url_input,name_output], |
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outputs=[url_input], |
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fn=download_from_url, |
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) |
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with gr.Row(): |
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model_browser = gr.Dropdown(choices=list(model_library.models.keys()),label="OR Search Models (Quality UNKNOWN)",scale=5) |
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download_from_browser = gr.Button(value="Get",scale=2) |
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download_from_browser.click( |
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inputs=[model_browser], |
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outputs=[model_browser], |
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fn=lambda model: download_from_url(model_library.models[model],model), |
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) |
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with gr.TabItem("Train"): |
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with gr.Row(): |
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with gr.Column(): |
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training_name = gr.Textbox(label="Name your model", value="My-Voice",placeholder="My-Voice") |
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np7 = gr.Slider( |
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minimum=0, |
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maximum=config.n_cpu, |
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step=1, |
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label="Number of CPU processes used to extract pitch features", |
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value=1, |
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interactive=False, |
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visible=False |
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) |
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sr2 = gr.Radio( |
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label="Sampling Rate", |
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choices=["40k", "32k"], |
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value="32k", |
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interactive=True, |
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visible=True |
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) |
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if_f0_3 = gr.Radio( |
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label="Will your model be used for singing? If not, you can ignore this.", |
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choices=[True, False], |
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value=True, |
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interactive=True, |
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visible=False |
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) |
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version19 = gr.Radio( |
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label="Version", |
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choices=["v1", "v2"], |
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value="v2", |
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interactive=True, |
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visible=False, |
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) |
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dataset_folder = gr.Textbox( |
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label="dataset folder", value='dataset' |
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) |
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easy_uploader = gr.Files(label="Drop your audio files here",file_types=['audio']) |
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easy_uploader.upload(inputs=[dataset_folder],outputs=[],fn=lambda folder:os.makedirs(folder,exist_ok=True)) |
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easy_uploader.upload( |
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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'), |
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inputs=[easy_uploader, dataset_folder], |
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outputs=[]) |
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gpus6 = gr.Textbox( |
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label="Enter the GPU numbers to use separated by -, (e.g. 0-1-2)", |
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value="", |
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interactive=True, |
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visible=False, |
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) |
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gpu_info9 = gr.Textbox( |
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label="GPU Info", value=gpu_info, visible=False |
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) |
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spk_id5 = gr.Slider( |
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minimum=0, |
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maximum=4, |
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step=1, |
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label="Speaker ID", |
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value=0, |
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interactive=True, |
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visible=False |
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) |
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with gr.Column(): |
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f0method8 = gr.Radio( |
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label="F0 extraction method", |
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choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], |
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value="pm", |
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interactive=False, |
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visible=False |
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) |
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gpus_rmvpe = gr.Textbox( |
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label="GPU numbers to use separated by -, (e.g. 0-1-2)", |
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value="", |
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interactive=False, |
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visible=False, |
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) |
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f0method8.change( |
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fn=change_f0_method, |
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inputs=[f0method8], |
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outputs=[gpus_rmvpe], |
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) |
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with gr.Column(): |
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total_epoch11 = gr.Slider( |
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minimum=5, |
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maximum=1000, |
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step=5, |
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label="Epochs (more epochs may improve quality but takes longer)", |
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value=100, |
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interactive=True, |
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) |
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with gr.Accordion(label="General Settings", open=False): |
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gpus16 = gr.Textbox( |
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label="GPUs separated by -, (e.g. 0-1-2)", |
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value="0", |
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interactive=True, |
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visible=False, |
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) |
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save_epoch10 = gr.Slider( |
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minimum=1, |
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maximum=50, |
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step=1, |
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label="Weight Saving Frequency", |
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value=25, |
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interactive=True, |
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visible=False |
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) |
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batch_size12 = gr.Slider( |
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minimum=1, |
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maximum=40, |
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step=1, |
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label="Batch Size", |
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value=1, |
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interactive=True, |
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visible=False |
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) |
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if_save_latest13 = gr.Radio( |
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label="Only save the latest model", |
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choices=["yes", "no"], |
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value="yes", |
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interactive=True, |
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visible=False |
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) |
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if_cache_gpu17 = gr.Radio( |
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label="If your dataset is UNDER 10 minutes, cache it to train faster", |
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choices=["yes", "no"], |
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value="no", |
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interactive=True, |
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visible=True |
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) |
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if_save_every_weights18 = gr.Radio( |
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label="Save small model at every save point", |
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choices=["yes", "no"], |
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value="yes", |
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interactive=False, |
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visible=False |
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) |
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with gr.Accordion(label="Change pretrains", open=False): |
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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] |
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pretrained_G14 = gr.Dropdown( |
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label="pretrained G", |
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choices = pretrained(sr2.value, 'G'), |
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value=pretrained(sr2.value, 'G')[0] if len(pretrained(sr2.value, 'G')) > 0 else '', |
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interactive=True, |
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visible=True |
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) |
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pretrained_D15 = gr.Dropdown( |
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label="pretrained D", |
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choices = pretrained(sr2.value, 'D'), |
|
|
|
value= pretrained(sr2.value, 'D')[0] if len(pretrained(sr2.value, 'G')) > 0 else '', |
|
|
|
visible=True, |
|
|
|
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.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(max_size=20).launch(share=True,allowed_paths=["a.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, |
|
|
|
) |
