import os import argparse import time import json import base64 import re import urllib.request import torch import torchaudio import music_tag import gradio as gr import gradio.utils from datetime import datetime import tortoise.api from tortoise.utils.audio import get_voice_dir, get_voices from utils import * args = setup_args() def run_generation( text, delimiter, emotion, prompt, voice, mic_audio, voice_latents_chunks, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, breathing_room, cvvp_weight, top_p, diffusion_temperature, length_penalty, repetition_penalty, cond_free_k, experimental_checkboxes, progress=gr.Progress(track_tqdm=True) ): try: sample, outputs, stats = generate( text, delimiter, emotion, prompt, voice, mic_audio, voice_latents_chunks, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, breathing_room, cvvp_weight, top_p, diffusion_temperature, length_penalty, repetition_penalty, cond_free_k, experimental_checkboxes, progress ) except Exception as e: message = str(e) if message == "Kill signal detected": reload_tts() raise gr.Error(message) return ( outputs[0], gr.update(value=sample, visible=sample is not None), gr.update(choices=outputs, value=outputs[0], visible=len(outputs) > 1, interactive=True), gr.update(visible=len(outputs) > 1), gr.update(value=stats, visible=True), ) def update_presets(value): PRESETS = { 'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False}, 'Fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80}, 'Standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200}, 'High Quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400}, } if value in PRESETS: preset = PRESETS[value] return (gr.update(value=preset['num_autoregressive_samples']), gr.update(value=preset['diffusion_iterations'])) else: return (gr.update(), gr.update()) def get_training_configs(): configs = [] for i, file in enumerate(sorted(os.listdir(f"./training/"))): if file[-5:] != ".yaml" or file[0] == ".": continue configs.append(f"./training/{file}") return configs def update_training_configs(): return gr.update(choices=get_training_list()) history_headers = { "Name": "", "Samples": "num_autoregressive_samples", "Iterations": "diffusion_iterations", "Temp.": "temperature", "Sampler": "diffusion_sampler", "CVVP": "cvvp_weight", "Top P": "top_p", "Diff. Temp.": "diffusion_temperature", "Len Pen": "length_penalty", "Rep Pen": "repetition_penalty", "Cond-Free K": "cond_free_k", "Time": "time", } def history_view_results( voice ): results = [] files = [] outdir = f"./results/{voice}/" for i, file in enumerate(sorted(os.listdir(outdir))): if file[-4:] != ".wav": continue metadata, _ = read_generate_settings(f"{outdir}/{file}", read_latents=False) if metadata is None: continue values = [] for k in history_headers: v = file if k != "Name": v = metadata[headers[k]] values.append(v) files.append(file) results.append(values) return ( results, gr.Dropdown.update(choices=sorted(files)) ) def import_voices_proxy(files, name, progress=gr.Progress(track_tqdm=True)): import_voices(files, name, progress) return gr.update() def read_generate_settings_proxy(file, saveAs='.temp'): j, latents = read_generate_settings(file) if latents: outdir = f'{get_voice_dir()}/{saveAs}/' os.makedirs(outdir, exist_ok=True) with open(f'{outdir}/cond_latents.pth', 'wb') as f: f.write(latents) latents = f'{outdir}/cond_latents.pth' return ( gr.update(value=j, visible=j is not None), gr.update(visible=j is not None), gr.update(value=latents, visible=latents is not None), None if j is None else j['voice'] ) def prepare_dataset_proxy( voice, language, progress=gr.Progress(track_tqdm=True) ): return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, progress=progress ) def save_training_settings_proxy( iterations, batch_size, learning_rate, print_rate, save_rate, voice ): name = f"{voice}-finetune" dataset_name = f"{voice}-train" dataset_path = f"./training/{voice}/train.txt" validation_name = f"{voice}-val" validation_path = f"./training/{voice}/train.txt" with open(dataset_path, 'r', encoding="utf-8") as f: lines = len(f.readlines()) if batch_size > lines: print("Batch size is larger than your dataset, clamping...") batch_size = lines out_name = f"{voice}/train.yaml" return save_training_settings(iterations, batch_size, learning_rate, print_rate, save_rate, name, dataset_name, dataset_path, validation_name, validation_path, out_name ) def update_voices(): return ( gr.Dropdown.update(choices=get_voice_list()), gr.Dropdown.update(choices=get_voice_list()), gr.Dropdown.update(choices=get_voice_list("./results/")), ) def history_copy_settings( voice, file ): return import_generate_settings( f"./results/{voice}/{file}" ) def update_model_settings( autoregressive_model, whisper_model ): if args.autoregressive_model != autoregressive_model: update_autoregressive_model(autoregressive_model) args.whisper_model = whisper_model save_args_settings() def setup_gradio(): global args global ui if not args.share: def noop(function, return_value=None): def wrapped(*args, **kwargs): return return_value return wrapped gradio.utils.version_check = noop(gradio.utils.version_check) gradio.utils.initiated_analytics = noop(gradio.utils.initiated_analytics) gradio.utils.launch_analytics = noop(gradio.utils.launch_analytics) gradio.utils.integration_analytics = noop(gradio.utils.integration_analytics) gradio.utils.error_analytics = noop(gradio.utils.error_analytics) gradio.utils.log_feature_analytics = noop(gradio.utils.log_feature_analytics) #gradio.utils.get_local_ip_address = noop(gradio.utils.get_local_ip_address, 'localhost') if args.models_from_local_only: os.environ['TRANSFORMERS_OFFLINE']='1' with gr.Blocks() as ui: with gr.Tab("Generate"): with gr.Row(): with gr.Column(): text = gr.Textbox(lines=4, label="Prompt") with gr.Row(): with gr.Column(): delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n") emotion = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"], value="Custom", label="Emotion", type="value", interactive=True ) prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)") voice = gr.Dropdown(get_voice_list(), label="Voice", type="value") mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath" ) refresh_voices = gr.Button(value="Refresh Voice List") voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=64, value=1, step=1) recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents") with gr.Column(): candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates") seed = gr.Number(value=0, precision=0, label="Seed") preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value" ) num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples") diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations") temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature") breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size") diffusion_sampler = gr.Radio( ["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"], value="P", label="Diffusion Samplers", type="value" ) show_experimental_settings = gr.Checkbox(label="Show Experimental Settings") reset_generation_settings_button = gr.Button(value="Reset to Default") with gr.Column(visible=False) as col: experimental_column = col experimental_checkboxes = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags") cvvp_weight = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight") top_p = gr.Slider(value=0.8, minimum=0, maximum=1, label="Top P") diffusion_temperature = gr.Slider(value=1.0, minimum=0, maximum=1, label="Diffusion Temperature") length_penalty = gr.Slider(value=1.0, minimum=0, maximum=8, label="Length Penalty") repetition_penalty = gr.Slider(value=2.0, minimum=0, maximum=8, label="Repetition Penalty") cond_free_k = gr.Slider(value=2.0, minimum=0, maximum=4, label="Conditioning-Free K") with gr.Column(): submit = gr.Button(value="Generate") stop = gr.Button(value="Stop") generation_results = gr.Dataframe(label="Results", headers=["Seed", "Time"], visible=False) source_sample = gr.Audio(label="Source Sample", visible=False) output_audio = gr.Audio(label="Output") candidates_list = gr.Dropdown(label="Candidates", type="value", visible=False) output_pick = gr.Button(value="Select Candidate", visible=False) with gr.Tab("History"): with gr.Row(): with gr.Column(): history_info = gr.Dataframe(label="Results", headers=list(history_headers.keys())) with gr.Row(): with gr.Column(): history_voices = gr.Dropdown(choices=get_voice_list("./results/"), label="Voice", type="value") history_view_results_button = gr.Button(value="View Files") with gr.Column(): history_results_list = gr.Dropdown(label="Results",type="value", interactive=True) history_view_result_button = gr.Button(value="View File") with gr.Column(): history_audio = gr.Audio() history_copy_settings_button = gr.Button(value="Copy Settings") with gr.Tab("Utilities"): with gr.Row(): with gr.Column(): audio_in = gr.Files(type="file", label="Audio Input", file_types=["audio"]) import_voice_name = gr.Textbox(label="Voice Name") import_voice_button = gr.Button(value="Import Voice") with gr.Column(): metadata_out = gr.JSON(label="Audio Metadata", visible=False) copy_button = gr.Button(value="Copy Settings", visible=False) latents_out = gr.File(type="binary", label="Voice Latents", visible=False) with gr.Tab("Training"): with gr.Tab("Prepare Dataset"): with gr.Row(): with gr.Column(): dataset_settings = [ gr.Dropdown( get_voice_list(), label="Dataset Source", type="value" ), gr.Textbox(label="Language", placeholder="English") ] prepare_dataset_button = gr.Button(value="Prepare") with gr.Column(): prepare_dataset_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8) with gr.Tab("Generate Configuration"): with gr.Row(): with gr.Column(): training_settings = [ gr.Slider(label="Iterations", minimum=0, maximum=5000, value=500), gr.Slider(label="Batch Size", minimum=2, maximum=128, value=64), gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6), gr.Number(label="Print Frequency", value=50), gr.Number(label="Save Frequency", value=50), ] dataset_list = gr.Dropdown( get_dataset_list(), label="Dataset", type="value" ) training_settings = training_settings + [ dataset_list ] refresh_dataset_list = gr.Button(value="Refresh Dataset List") """ training_settings = training_settings + [ gr.Textbox(label="Training Name", placeholder="finetune"), gr.Textbox(label="Dataset Name", placeholder="finetune"), gr.Textbox(label="Dataset Path", placeholder="./training/finetune/train.txt"), gr.Textbox(label="Validation Name", placeholder="finetune"), gr.Textbox(label="Validation Path", placeholder="./training/finetune/train.txt"), ] """ with gr.Column(): save_yaml_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8) save_yaml_button = gr.Button(value="Save Training Configuration") with gr.Tab("Run Training"): with gr.Row(): with gr.Column(): training_configs = gr.Dropdown(label="Training Configuration", choices=get_training_list()) refresh_configs = gr.Button(value="Refresh Configurations") start_training_button = gr.Button(value="Train") stop_training_button = gr.Button(value="Stop") with gr.Column(): training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8) with gr.Tab("Settings"): with gr.Row(): exec_inputs = [] with gr.Column(): exec_inputs = exec_inputs + [ gr.Textbox(label="Listen", value=args.listen, placeholder="127.0.0.1:7860/"), gr.Checkbox(label="Public Share Gradio", value=args.share), gr.Checkbox(label="Check For Updates", value=args.check_for_updates), gr.Checkbox(label="Only Load Models Locally", value=args.models_from_local_only), gr.Checkbox(label="Low VRAM", value=args.low_vram), gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata), gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean), gr.Checkbox(label="Voice Fixer", value=args.voice_fixer), gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda), gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents), gr.Checkbox(label="Defer TTS Load", value=args.defer_tts_load), gr.Textbox(label="Device Override", value=args.device_override), ] with gr.Column(): exec_inputs = exec_inputs + [ gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size), gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count), gr.Number(label="Ouptut Sample Rate", precision=0, value=args.output_sample_rate), gr.Slider(label="Ouptut Volume", minimum=0, maximum=2, value=args.output_volume), ] autoregressive_model_dropdown = gr.Dropdown(get_autoregressive_models(), label="Autoregressive Model", value=args.autoregressive_model) whisper_model_dropdown = gr.Dropdown(["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large"], label="Whisper Model", value=args.whisper_model) save_settings_button = gr.Button(value="Save Settings") gr.Button(value="Check for Updates").click(check_for_updates) gr.Button(value="(Re)Load TTS").click(reload_tts) for i in exec_inputs: i.change( fn=update_args, inputs=exec_inputs ) # console_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8) input_settings = [ text, delimiter, emotion, prompt, voice, mic_audio, voice_latents_chunks, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, breathing_room, cvvp_weight, top_p, diffusion_temperature, length_penalty, repetition_penalty, cond_free_k, experimental_checkboxes, ] history_view_results_button.click( fn=history_view_results, inputs=history_voices, outputs=[ history_info, history_results_list, ] ) history_view_result_button.click( fn=lambda voice, file: f"./results/{voice}/{file}", inputs=[ history_voices, history_results_list, ], outputs=history_audio ) audio_in.upload( fn=read_generate_settings_proxy, inputs=audio_in, outputs=[ metadata_out, copy_button, latents_out, import_voice_name ] ) import_voice_button.click( fn=import_voices_proxy, inputs=[ audio_in, import_voice_name, ], outputs=import_voice_name #console_output ) show_experimental_settings.change( fn=lambda x: gr.update(visible=x), inputs=show_experimental_settings, outputs=experimental_column ) preset.change(fn=update_presets, inputs=preset, outputs=[ num_autoregressive_samples, diffusion_iterations, ], ) recompute_voice_latents.click(compute_latents, inputs=[ voice, voice_latents_chunks, ], outputs=voice, ) prompt.change(fn=lambda value: gr.update(value="Custom"), inputs=prompt, outputs=emotion ) mic_audio.change(fn=lambda value: gr.update(value="microphone"), inputs=mic_audio, outputs=voice ) refresh_voices.click(update_voices, inputs=None, outputs=[ voice, dataset_settings[0], history_voices ] ) output_pick.click( lambda x: x, inputs=candidates_list, outputs=output_audio, ) submit.click( lambda: (gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)), outputs=[source_sample, candidates_list, output_pick, generation_results], ) submit_event = submit.click(run_generation, inputs=input_settings, outputs=[output_audio, source_sample, candidates_list, output_pick, generation_results], ) copy_button.click(import_generate_settings, inputs=audio_in, # JSON elements cannot be used as inputs outputs=input_settings ) reset_generation_settings_button.click( fn=reset_generation_settings, inputs=None, outputs=input_settings ) history_copy_settings_button.click(history_copy_settings, inputs=[ history_voices, history_results_list, ], outputs=input_settings ) refresh_configs.click( lambda: gr.update(choices=get_training_list()), inputs=None, outputs=training_configs ) start_training_button.click(run_training, inputs=training_configs, outputs=training_output #console_output ) stop_training_button.click(stop_training, inputs=None, outputs=training_output #console_output ) prepare_dataset_button.click( prepare_dataset_proxy, inputs=dataset_settings, outputs=prepare_dataset_output #console_output ) refresh_dataset_list.click( lambda: gr.update(choices=get_dataset_list()), inputs=None, outputs=dataset_list, ) save_yaml_button.click(save_training_settings_proxy, inputs=training_settings, outputs=save_yaml_output #console_output ) save_settings_button.click(update_model_settings, inputs=[ autoregressive_model_dropdown, whisper_model_dropdown, ], outputs=None ) if os.path.isfile('./config/generate.json'): ui.load(import_generate_settings, inputs=None, outputs=input_settings) if args.check_for_updates: ui.load(check_for_updates) stop.click(fn=cancel_generate, inputs=None, outputs=None, cancels=[submit_event]) ui.queue(concurrency_count=args.concurrency_count) webui = ui return webui