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.api import TextToSpeech from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir from tortoise.utils.text import split_and_recombine_text voicefixer = None def generate( text, delimiter, emotion, prompt, voice, mic_audio, 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=None ): global args global tts try: tts except NameError: raise gr.Error("TTS is still initializing...") if voice != "microphone": voices = [voice] else: voices = [] if voice == "microphone": if mic_audio is None: raise gr.Error("Please provide audio from mic when choosing `microphone` as a voice input") mic = load_audio(mic_audio, tts.input_sample_rate) voice_samples, conditioning_latents = [mic], None else: progress(0, desc="Loading voice...") voice_samples, conditioning_latents = load_voice(voice) if voice_samples is not None: sample_voice = voice_samples[0].squeeze().cpu() conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, max_chunk_size=args.cond_latent_max_chunk_size) if len(conditioning_latents) == 4: conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None) if voice != "microphone": torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth') voice_samples = None else: sample_voice = None if seed == 0: seed = None if conditioning_latents is not None and len(conditioning_latents) == 2 and cvvp_weight > 0: print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.") cvvp_weight = 0 settings = { 'temperature': float(temperature), 'top_p': float(top_p), 'diffusion_temperature': float(diffusion_temperature), 'length_penalty': float(length_penalty), 'repetition_penalty': float(repetition_penalty), 'cond_free_k': float(cond_free_k), 'num_autoregressive_samples': num_autoregressive_samples, 'sample_batch_size': args.sample_batch_size, 'diffusion_iterations': diffusion_iterations, 'voice_samples': voice_samples, 'conditioning_latents': conditioning_latents, 'use_deterministic_seed': seed, 'return_deterministic_state': True, 'k': candidates, 'diffusion_sampler': diffusion_sampler, 'breathing_room': breathing_room, 'progress': progress, 'half_p': "Half Precision" in experimental_checkboxes, 'cond_free': "Conditioning-Free" in experimental_checkboxes, 'cvvp_amount': cvvp_weight, } if delimiter == "\\n": delimiter = "\n" if delimiter != "" and delimiter in text: texts = text.split(delimiter) else: texts = split_and_recombine_text(text) full_start_time = time.time() outdir = f"./results/{voice}/" os.makedirs(outdir, exist_ok=True) audio_cache = {} resample = None # not a ternary in the event for some reason I want to rely on librosa's upsampling interpolator rather than torchaudio's, for some reason if tts.output_sample_rate != args.output_sample_rate: resampler = torchaudio.transforms.Resample( tts.output_sample_rate, args.output_sample_rate, lowpass_filter_width=16, rolloff=0.85, resampling_method="kaiser_window", beta=8.555504641634386, ) volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None idx = 0 for i, file in enumerate(os.listdir(outdir)): if file[-5:] == ".json": idx = idx + 1 if idx: idx = idx + 1 # reserve, if for whatever reason you manage to concurrently generate with open(f'{outdir}/input_{idx}.json', 'w', encoding="utf-8") as f: f.write(" ") def get_name(line=0, candidate=0, combined=False): name = f"{idx}" if combined: name = f"{name}_combined" elif len(texts) > 1: name = f"{name}_{line}" if candidates > 1: name = f"{name}_{candidate}" return name for line, cut_text in enumerate(texts): if emotion == "Custom": if prompt.strip() != "": cut_text = f"[{prompt},] {cut_text}" else: cut_text = f"[I am really {emotion.lower()},] {cut_text}" progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]' print(f"{progress.msg_prefix} Generating line: {cut_text}") start_time = time.time() gen, additionals = tts.tts(cut_text, **settings ) seed = additionals[0] run_time = time.time()-start_time print(f"Generating line took {run_time} seconds") if isinstance(gen, list): for j, g in enumerate(gen): name = get_name(line=line, candidate=j) audio_cache[name] = { 'audio': g, 'text': cut_text, 'time': run_time } else: name = get_name(line=line) audio_cache[name] = { 'audio': gen, 'text': cut_text, 'time': run_time, } for k in audio_cache: audio = audio_cache[k]['audio'].squeeze(0).cpu() if resampler is not None: audio = resampler(audio) if volume_adjust is not None: audio = volume_adjust(audio) audio_cache[k]['audio'] = audio torchaudio.save(f'{outdir}/{voice}_{k}.wav', audio, args.output_sample_rate) output_voice = None output_voices = [] for candidate in range(candidates): if len(texts) > 1: audio_clips = [] for line in range(len(texts)): name = get_name(line=line, candidate=candidate) audio = audio_cache[name]['audio'] audio_clips.append(audio) name = get_name(candidate=candidate, combined=True) audio = torch.cat(audio_clips, dim=-1) torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, args.output_sample_rate) audio = audio.squeeze(0).cpu() audio_cache[name] = { 'audio': audio, 'text': text, 'time': time.time()-full_start_time } output_voices.append(f'{outdir}/{voice}_{name}.wav') if output_voice is None: output_voice = f'{outdir}/{voice}_{name}.wav' else: name = get_name(candidate=candidate) output_voices.append(f'{outdir}/{voice}_{name}.wav') info = { 'text': text, 'delimiter': '\\n' if delimiter == "\n" else delimiter, 'emotion': emotion, 'prompt': prompt, 'voice': voice, 'mic_audio': mic_audio, 'seed': seed, 'candidates': candidates, 'num_autoregressive_samples': num_autoregressive_samples, 'diffusion_iterations': diffusion_iterations, 'temperature': temperature, 'diffusion_sampler': diffusion_sampler, 'breathing_room': breathing_room, 'cvvp_weight': cvvp_weight, 'top_p': top_p, 'diffusion_temperature': diffusion_temperature, 'length_penalty': length_penalty, 'repetition_penalty': repetition_penalty, 'cond_free_k': cond_free_k, 'experimentals': experimental_checkboxes, 'time': time.time()-full_start_time, } with open(f'{outdir}/input_{idx}.json', 'w', encoding="utf-8") as f: f.write(json.dumps(info, indent='\t') ) if voice is not None and conditioning_latents is not None: with open(f'{get_voice_dir()}/{voice}/cond_latents.pth', 'rb') as f: info['latents'] = base64.b64encode(f.read()).decode("ascii") if args.voice_fixer and voicefixer: # we could do this on the pieces before they get stiched up anyways to save some compute # but the stitching would need to read back from disk, defeating the point of caching the waveform for path in progress.tqdm(audio_cache, desc="Running voicefix..."): voicefixer.restore( input=f'{outdir}/{voice}_{k}.wav', output=f'{outdir}/{voice}_{k}.wav', #cuda=False, #mode=mode, ) if args.embed_output_metadata: for path in progress.tqdm(audio_cache, desc="Embedding metadata..."): info['text'] = audio_cache[path]['text'] info['time'] = audio_cache[path]['time'] metadata = music_tag.load_file(f"{outdir}/{voice}_{path}.wav") metadata['lyrics'] = json.dumps(info) metadata.save() #if output_voice is not None: # output_voice = (args.output_sample_rate, output_voice.numpy()) if sample_voice is not None: sample_voice = (tts.input_sample_rate, sample_voice.numpy()) print(f"Generation took {info['time']} seconds, saved to '{output_voices[0]}'\n") info['seed'] = settings['use_deterministic_seed'] del info['latents'] with open(f'./config/generate.json', 'w', encoding="utf-8") as f: f.write(json.dumps(info, indent='\t') ) stats = [ [ seed, "{:.3f}".format(info['time']) ] ] return ( sample_voice, output_voices, stats, ) 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 read_generate_settings(file, save_latents=True, save_as_temp=True): j = None latents = None if file is not None: if hasattr(file, 'name'): file = file.name if file[-4:] == ".wav": metadata = music_tag.load_file(file) if 'lyrics' in metadata: j = json.loads(str(metadata['lyrics'])) elif file[-5:] == ".json": with open(file, 'r') as f: j = json.load(f) if 'latents' in j and save_latents: latents = base64.b64decode(j['latents']) del j['latents'] if latents and save_latents: outdir=f'{get_voice_dir()}/{".temp" if save_as_temp else j["voice"]}/' 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' if "time" in j: j["time"] = "{:.3f}".format(j["time"]) return ( j, latents ) def save_latents(file): read_generate_settings(file, save_latents=True, save_as_temp=False) def import_generate_settings(file="./config/generate.json"): settings, _ = read_generate_settings(file, save_latents=False) if settings is None: return None return ( None if 'text' not in settings else settings['text'], None if 'delimiter' not in settings else settings['delimiter'], None if 'emotion' not in settings else settings['emotion'], None if 'prompt' not in settings else settings['prompt'], None if 'voice' not in settings else settings['voice'], None if 'mic_audio' not in settings else settings['mic_audio'], None if 'seed' not in settings else settings['seed'], None if 'candidates' not in settings else settings['candidates'], None if 'num_autoregressive_samples' not in settings else settings['num_autoregressive_samples'], None if 'diffusion_iterations' not in settings else settings['diffusion_iterations'], 0.8 if 'temperature' not in settings else settings['temperature'], "DDIM" if 'diffusion_sampler' not in settings else settings['diffusion_sampler'], 8 if 'breathing_room' not in settings else settings['breathing_room'], 0.0 if 'cvvp_weight' not in settings else settings['cvvp_weight'], 0.8 if 'top_p' not in settings else settings['top_p'], 1.0 if 'diffusion_temperature' not in settings else settings['diffusion_temperature'], 1.0 if 'length_penalty' not in settings else settings['length_penalty'], 2.0 if 'repetition_penalty' not in settings else settings['repetition_penalty'], 2.0 if 'cond_free_k' not in settings else settings['cond_free_k'], None if 'experimentals' not in settings else settings['experimentals'], ) def curl(url): try: req = urllib.request.Request(url, headers={'User-Agent': 'Python'}) conn = urllib.request.urlopen(req) data = conn.read() data = data.decode() data = json.loads(data) conn.close() return data except Exception as e: print(e) return None def check_for_updates(): if not os.path.isfile('./.git/FETCH_HEAD'): print("Cannot check for updates: not from a git repo") return False with open(f'./.git/FETCH_HEAD', 'r', encoding="utf-8") as f: head = f.read() match = re.findall(r"^([a-f0-9]+).+?https:\/\/(.+?)\/(.+?)\/(.+?)\n", head) if match is None or len(match) == 0: print("Cannot check for updates: cannot parse FETCH_HEAD") return False match = match[0] local = match[0] host = match[1] owner = match[2] repo = match[3] res = curl(f"https://{host}/api/v1/repos/{owner}/{repo}/branches/") #this only works for gitea instances if res is None or len(res) == 0: print("Cannot check for updates: cannot fetch from remote") return False remote = res[0]["commit"]["id"] if remote != local: print(f"New version found: {local[:8]} => {remote[:8]}") return True return False def reload_tts(): global tts del tts tts = setup_tortoise(restart=True) def cancel_generate(): tortoise.api.STOP_SIGNAL = True def get_voice_list(): voice_dir = get_voice_dir() return [d for d in os.listdir(voice_dir) if os.path.isdir(os.path.join(voice_dir, d))] def update_voices(): return gr.Dropdown.update(choices=sorted(get_voice_list()) + ["microphone"]) def export_exec_settings( share, listen, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, cond_latent_max_chunk_size, sample_batch_size, concurrency_count, output_sample_rate, output_volume ): args.share = share args.listen = listen args.low_vram = low_vram args.check_for_updates = check_for_updates args.models_from_local_only = models_from_local_only args.cond_latent_max_chunk_size = cond_latent_max_chunk_size args.sample_batch_size = sample_batch_size args.embed_output_metadata = embed_output_metadata args.latents_lean_and_mean = latents_lean_and_mean args.voice_fixer = voice_fixer args.concurrency_count = concurrency_count args.output_sample_rate = output_sample_rate args.output_volume = output_volume settings = { 'share': args.share, 'listen': args.listen, 'low-vram':args.low_vram, 'check-for-updates':args.check_for_updates, 'models-from-local-only':args.models_from_local_only, 'cond-latent-max-chunk-size': args.cond_latent_max_chunk_size, 'sample-batch-size': args.sample_batch_size, 'embed-output-metadata': args.embed_output_metadata, 'latents-lean-and-mean': args.latents_lean_and_mean, 'voice-fixer': args.voice_fixer, 'concurrency-count': args.concurrency_count, 'output-sample-rate': args.output_sample_rate, 'output-volume': args.output_volume, } with open(f'./config/exec.json', 'w', encoding="utf-8") as f: f.write(json.dumps(settings, indent='\t') ) def setup_args(): default_arguments = { 'share': False, 'listen': None, 'check-for-updates': False, 'models-from-local-only': False, 'low-vram': False, 'sample-batch-size': None, 'embed-output-metadata': True, 'latents-lean-and-mean': True, 'voice-fixer': True, 'cond-latent-max-chunk-size': 1000000, 'concurrency-count': 2, 'output-sample-rate': 44100, 'output-volume': 1, } if os.path.isfile('./config/exec.json'): with open(f'./config/exec.json', 'r', encoding="utf-8") as f: overrides = json.load(f) for k in overrides: default_arguments[k] = overrides[k] parser = argparse.ArgumentParser() parser.add_argument("--share", action='store_true', default=default_arguments['share'], help="Lets Gradio return a public URL to use anywhere") parser.add_argument("--listen", default=default_arguments['listen'], help="Path for Gradio to listen on") parser.add_argument("--check-for-updates", action='store_true', default=default_arguments['check-for-updates'], help="Checks for update on startup") parser.add_argument("--models-from-local-only", action='store_true', default=default_arguments['models-from-local-only'], help="Only loads models from disk, does not check for updates for models") parser.add_argument("--low-vram", action='store_true', default=default_arguments['low-vram'], help="Disables some optimizations that increases VRAM usage") parser.add_argument("--no-embed-output-metadata", action='store_false', default=not default_arguments['embed-output-metadata'], help="Disables embedding output metadata into resulting WAV files for easily fetching its settings used with the web UI (data is stored in the lyrics metadata tag)") parser.add_argument("--latents-lean-and-mean", action='store_true', default=default_arguments['latents-lean-and-mean'], help="Exports the bare essentials for latents.") parser.add_argument("--voice-fixer", action='store_true', default=default_arguments['voice-fixer'], help="Uses python module 'voicefixer' to improve audio quality, if available.") parser.add_argument("--cond-latent-max-chunk-size", default=default_arguments['cond-latent-max-chunk-size'], type=int, help="Sets an upper limit to audio chunk size when computing conditioning latents") parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets an upper limit to audio chunk size when computing conditioning latents") parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once") parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)") parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output") args = parser.parse_args() args.embed_output_metadata = not args.no_embed_output_metadata args.listen_host = None args.listen_port = None args.listen_path = None if args.listen: match = re.findall(r"^(?:(.+?):(\d+))?(\/.+?)?$", args.listen)[0] args.listen_host = match[0] if match[0] != "" else "127.0.0.1" args.listen_port = match[1] if match[1] != "" else None args.listen_path = match[2] if match[2] != "" else "/" if args.listen_port is not None: args.listen_port = int(args.listen_port) return args def setup_tortoise(restart=False): global args global tts global voicefixer if args.voice_fixer and not restart: try: from voicefixer import VoiceFixer print("Initializating voice-fixer") voicefixer = VoiceFixer() print("initialized voice-fixer") except Exception as e: pass print("Initializating TorToiSe...") tts = TextToSpeech(minor_optimizations=not args.low_vram) print("TorToiSe initialized, ready for generation.") return tts def setup_gradio(): global args 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 webui: 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( sorted(get_voice_list()) + ["microphone"], label="Voice", type="value", ) mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath", ) refresh_voices = gr.Button(value="Refresh Voice List") refresh_voices.click(update_voices, inputs=None, 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 ) 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", ) preset.change(fn=update_presets, inputs=preset, outputs=[ num_autoregressive_samples, diffusion_iterations, ], ) show_experimental_settings = gr.Checkbox(label="Show Experimental Settings") 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") show_experimental_settings.change( fn=lambda x: gr.update(visible=x), inputs=show_experimental_settings, outputs=experimental_column ) 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(): 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", } history_info = gr.Dataframe(label="Results", headers=list(headers.keys())) with gr.Row(): with gr.Column(): history_voices = gr.Dropdown( sorted(get_voice_list()) + ["microphone"], 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") def history_view_results( voice ): results = [] files = [] outdir = f"./results/{voice}/" for i, file in enumerate(os.listdir(outdir)): if file[-4:] != ".wav": continue metadata, _ = read_generate_settings(f"{outdir}/{file}", save_latents=False) if metadata is None: continue values = [] for k in 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)) ) 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 ) with gr.Tab("Utilities"): with gr.Row(): with gr.Column(): audio_in = gr.File(type="file", label="Audio Input", file_types=["audio"]) copy_button = gr.Button(value="Copy Settings") import_voice = gr.Button(value="Import Voice") with gr.Column(): metadata_out = gr.JSON(label="Audio Metadata") latents_out = gr.File(type="binary", label="Voice Latents") audio_in.upload( fn=read_generate_settings, inputs=audio_in, outputs=[ metadata_out, latents_out ] ) import_voice.click( fn=save_latents, inputs=audio_in, ) 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.Button(value="Check for Updates").click(check_for_updates) gr.Button(value="Reload TTS").click(reload_tts) with gr.Column(): exec_inputs = exec_inputs + [ gr.Number(label="Voice Latents Max Chunk Size", precision=0, value=args.cond_latent_max_chunk_size), 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), ] for i in exec_inputs: i.change( fn=export_exec_settings, inputs=exec_inputs ) input_settings = [ text, delimiter, emotion, prompt, voice, mic_audio, 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, ] # YUCK def run_generation( text, delimiter, emotion, prompt, voice, mic_audio, 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, 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), ) 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 ) def history_copy_settings( voice, file ): settings = import_generate_settings( f"./results/{voice}/{file}" ) return settings history_copy_settings_button.click(history_copy_settings, inputs=[ history_voices, history_results_list, ], outputs=input_settings ) if os.path.isfile('./config/generate.json'): webui.load(import_generate_settings, inputs=None, outputs=input_settings) if args.check_for_updates: webui.load(check_for_updates) stop.click(fn=cancel_generate, inputs=None, outputs=None, cancels=[submit_event]) webui.queue(concurrency_count=args.concurrency_count) return webui