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 from tortoise.api import TextToSpeech from tortoise.utils.audio import load_audio, load_voice, load_voices from tortoise.utils.text import split_and_recombine_text def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, breathing_room, cvvp_weight, experimentals, progress=gr.Progress(track_tqdm=True)): 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] 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'./tortoise/voices/{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 start_time = time.time() settings = { 'temperature': temperature, 'length_penalty': 1.0, 'repetition_penalty': 2.0, 'top_p': .8, 'cond_free_k': 2.0, 'diffusion_temperature': 1.0, '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 experimentals, 'cond_free': "Conditioning-Free" in experimentals, '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) timestamp = int(time.time()) outdir = f"./results/{voice}/{timestamp}/" os.makedirs(outdir, exist_ok=True) audio_cache = {} 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}" print(f"[{str(line+1)}/{str(len(texts))}] Generating line: {cut_text}") gen, additionals = tts.tts(cut_text, **settings ) seed = additionals[0] if isinstance(gen, list): for j, g in enumerate(gen): audio = g.squeeze(0).cpu() audio_cache[f"candidate_{j}/result_{line}.wav"] = { 'audio': audio, 'text': cut_text, } os.makedirs(f'{outdir}/candidate_{j}', exist_ok=True) torchaudio.save(f'{outdir}/candidate_{j}/result_{line}.wav', audio, tts.output_sample_rate) else: audio = gen.squeeze(0).cpu() audio_cache[f"result_{line}.wav"] = { 'audio': audio, 'text': cut_text, } torchaudio.save(f'{outdir}/result_{line}.wav', audio, tts.output_sample_rate) output_voice = None if len(texts) > 1: for candidate in range(candidates): audio_clips = [] for line in range(len(texts)): if isinstance(gen, list): audio = audio_cache[f'candidate_{candidate}/result_{line}.wav']['audio'] else: audio = audio_cache[f'result_{line}.wav']['audio'] audio_clips.append(audio) audio = torch.cat(audio_clips, dim=-1) torchaudio.save(f'{outdir}/combined_{candidate}.wav', audio, tts.output_sample_rate) audio = audio.squeeze(0).cpu() audio_cache[f'combined_{candidate}.wav'] = { 'audio': audio, 'text': cut_text, } if output_voice is None: output_voice = audio else: if isinstance(gen, list): output_voice = gen[0] else: output_voice = gen if output_voice is not None: output_voice = (tts.output_sample_rate, output_voice.numpy()) 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, 'experimentals': experimentals, 'time': time.time()-start_time, } with open(f'{outdir}/input.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'./tortoise/voices/{voice}/cond_latents.pth', 'rb') as f: info['latents'] = base64.b64encode(f.read()).decode("ascii") if args.embed_output_metadata: for path in audio_cache: info['text'] = audio_cache[path]['text'] metadata = music_tag.load_file(f"{outdir}/{path}") metadata['lyrics'] = json.dumps(info) metadata.save() if sample_voice is not None: sample_voice = (tts.input_sample_rate, sample_voice.squeeze().cpu().numpy()) print(f"Generation took {info['time']} seconds, saved to '{outdir}'\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') ) return ( sample_voice, output_voice, seed ) 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): j = None latents = None if file is not None: if hasattr(file, 'name'): metadata = music_tag.load_file(file.name) 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='./voices/.temp/' 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 ( j, latents ) 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'], None if 'temperature' not in settings else settings['temperature'], None if 'diffusion_sampler' not in settings else settings['diffusion_sampler'], None if 'breathing_room' not in settings else settings['breathing_room'], None if 'cvvp_weight' not in settings else settings['cvvp_weight'], 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 update_voices(): return gr.Dropdown.update(choices=sorted(os.listdir("./tortoise/voices")) + ["microphone"]) def export_exec_settings( share, check_for_updates, low_vram, embed_output_metadata, latents_lean_and_mean, cond_latent_max_chunk_size, sample_batch_size, concurrency_count ): args.share = share args.low_vram = low_vram args.check_for_updates = check_for_updates 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.concurrency_count = concurrency_count settings = { 'share': args.share, 'low-vram':args.low_vram, 'check-for-updates':args.check_for_updates, '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, 'concurrency-count': args.concurrency_count, } with open(f'./config/exec.json', 'w', encoding="utf-8") as f: f.write(json.dumps(settings, indent='\t') ) def main(): with gr.Blocks() as webui: with gr.Tab("Generate"): with gr.Row(): with gr.Column(): text = gr.Textbox(lines=4, label="Prompt") 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(os.listdir("./tortoise/voices")) + ["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, ], ) with gr.Column(): selected_voice = gr.Audio(label="Source Sample") output_audio = gr.Audio(label="Output") usedSeed = gr.Textbox(label="Seed", placeholder="0", interactive=False) submit = gr.Button(value="Generate") #stop = gr.Button(value="Stop") 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") 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 ] ) with gr.Tab("Settings"): with gr.Row(): with gr.Column(): with gr.Box(): exec_arg_share = gr.Checkbox(label="Public Share Gradio", value=args.share) exec_check_for_updates = gr.Checkbox(label="Check For Updates", value=args.check_for_updates) exec_arg_low_vram = gr.Checkbox(label="Low VRAM", value=args.low_vram) exec_arg_embed_output_metadata = gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata) exec_arg_latents_lean_and_mean = gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean) exec_arg_cond_latent_max_chunk_size = gr.Number(label="Voice Latents Max Chunk Size", precision=0, value=args.cond_latent_max_chunk_size) exec_arg_sample_batch_size = gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size) exec_arg_concurrency_count = gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count) experimentals = 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") check_updates_now = gr.Button(value="Check for Updates") exec_inputs = [exec_arg_share, exec_check_for_updates, exec_arg_low_vram, exec_arg_embed_output_metadata, exec_arg_latents_lean_and_mean, exec_arg_cond_latent_max_chunk_size, exec_arg_sample_batch_size, exec_arg_concurrency_count] for i in exec_inputs: i.change( fn=export_exec_settings, inputs=exec_inputs ) check_updates_now.click(check_for_updates) input_settings = [ text, delimiter, emotion, prompt, voice, mic_audio, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, breathing_room, cvvp_weight, experimentals, ] submit_event = submit.click(generate, inputs=input_settings, outputs=[selected_voice, output_audio, usedSeed], ) copy_button.click(import_generate_settings, inputs=audio_in, # JSON elements cannt be used as inputs 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=None, inputs=None, outputs=None, cancels=[submit_event]) webui.queue(concurrency_count=args.concurrency_count).launch(share=args.share) if __name__ == "__main__": default_arguments = { 'share': False, 'check-for-updates': False, 'low-vram': False, 'sample-batch-size': None, 'embed-output-metadata': True, 'latents-lean-and-mean': True, 'cond-latent-max-chunk-size': 1000000, 'concurrency-count': 3, } 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("--check-for-updates", action='store_true', default=default_arguments['check-for-updates'], help="Checks for update on startup") 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("--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") args = parser.parse_args() args.embed_output_metadata = not args.no_embed_output_metadata 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') print("Initializating TorToiSe...") tts = TextToSpeech( minor_optimizations=not args.low_vram, ) main()