forked from mrq/tortoise-tts
558 lines
23 KiB
Python
Executable File
558 lines
23 KiB
Python
Executable File
import os
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import argparse
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import time
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import json
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import base64
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import re
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import urllib.request
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import torch
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import torchaudio
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import music_tag
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import gradio as gr
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import gradio.utils
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from datetime import datetime
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from tortoise.api import TextToSpeech
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from tortoise.utils.audio import load_audio, load_voice, load_voices
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from tortoise.utils.text import split_and_recombine_text
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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)):
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if voice != "microphone":
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voices = [voice]
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else:
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voices = []
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if voice == "microphone":
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if mic_audio is None:
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raise gr.Error("Please provide audio from mic when choosing `microphone` as a voice input")
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mic = load_audio(mic_audio, tts.input_sample_rate)
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voice_samples, conditioning_latents = [mic], None
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else:
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progress(0, desc="Loading voice...")
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voice_samples, conditioning_latents = load_voice(voice)
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if voice_samples is not None:
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sample_voice = voice_samples[0]
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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)
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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if voice != "microphone":
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torch.save(conditioning_latents, f'./tortoise/voices/{voice}/cond_latents.pth')
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voice_samples = None
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else:
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sample_voice = None
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if seed == 0:
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seed = None
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if conditioning_latents is not None and len(conditioning_latents) == 2 and cvvp_weight > 0:
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print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.")
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cvvp_weight = 0
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start_time = time.time()
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settings = {
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'temperature': temperature, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
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'top_p': .8,
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'cond_free_k': 2.0, 'diffusion_temperature': 1.0,
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'num_autoregressive_samples': num_autoregressive_samples,
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'sample_batch_size': args.sample_batch_size,
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'diffusion_iterations': diffusion_iterations,
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'voice_samples': voice_samples,
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'conditioning_latents': conditioning_latents,
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'use_deterministic_seed': seed,
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'return_deterministic_state': True,
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'k': candidates,
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'diffusion_sampler': diffusion_sampler,
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'breathing_room': breathing_room,
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'progress': progress,
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'half_p': "Half Precision" in experimentals,
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'cond_free': "Conditioning-Free" in experimentals,
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'cvvp_amount': cvvp_weight,
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}
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if delimiter == "\\n":
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delimiter = "\n"
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if delimiter != "" and delimiter in text:
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texts = text.split(delimiter)
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else:
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texts = split_and_recombine_text(text)
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timestamp = int(time.time())
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outdir = f"./results/{voice}/{timestamp}/"
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os.makedirs(outdir, exist_ok=True)
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audio_cache = {}
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for line, cut_text in enumerate(texts):
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if emotion == "Custom":
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if prompt.strip() != "":
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cut_text = f"[{prompt},] {cut_text}"
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else:
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cut_text = f"[I am really {emotion.lower()},] {cut_text}"
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print(f"[{str(line+1)}/{str(len(texts))}] Generating line: {cut_text}")
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gen, additionals = tts.tts(cut_text, **settings )
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seed = additionals[0]
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if isinstance(gen, list):
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for j, g in enumerate(gen):
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audio = g.squeeze(0).cpu()
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audio_cache[f"candidate_{j}/result_{line}.wav"] = {
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'audio': audio,
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'text': cut_text,
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}
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os.makedirs(f'{outdir}/candidate_{j}', exist_ok=True)
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torchaudio.save(f'{outdir}/candidate_{j}/result_{line}.wav', audio, tts.output_sample_rate)
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else:
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audio = gen.squeeze(0).cpu()
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audio_cache[f"result_{line}.wav"] = {
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'audio': audio,
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'text': cut_text,
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}
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torchaudio.save(f'{outdir}/result_{line}.wav', audio, tts.output_sample_rate)
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output_voice = None
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if len(texts) > 1:
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for candidate in range(candidates):
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audio_clips = []
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for line in range(len(texts)):
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if isinstance(gen, list):
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audio = audio_cache[f'candidate_{candidate}/result_{line}.wav']['audio']
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else:
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audio = audio_cache[f'result_{line}.wav']['audio']
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audio_clips.append(audio)
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audio = torch.cat(audio_clips, dim=-1)
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torchaudio.save(f'{outdir}/combined_{candidate}.wav', audio, tts.output_sample_rate)
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audio = audio.squeeze(0).cpu()
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audio_cache[f'combined_{candidate}.wav'] = {
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'audio': audio,
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'text': cut_text,
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}
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if output_voice is None:
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output_voice = audio
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else:
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if isinstance(gen, list):
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output_voice = gen[0]
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else:
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output_voice = gen
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if output_voice is not None:
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output_voice = (tts.output_sample_rate, output_voice.numpy())
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info = {
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'text': text,
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'delimiter': '\\n' if delimiter == "\n" else delimiter,
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'emotion': emotion,
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'prompt': prompt,
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'voice': voice,
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'mic_audio': mic_audio,
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'seed': seed,
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'candidates': candidates,
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'num_autoregressive_samples': num_autoregressive_samples,
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'diffusion_iterations': diffusion_iterations,
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'temperature': temperature,
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'diffusion_sampler': diffusion_sampler,
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'breathing_room': breathing_room,
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'cvvp_weight': cvvp_weight,
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'experimentals': experimentals,
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'time': time.time()-start_time,
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}
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with open(f'{outdir}/input.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(info, indent='\t') )
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if voice is not None and conditioning_latents is not None:
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with open(f'./tortoise/voices/{voice}/cond_latents.pth', 'rb') as f:
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info['latents'] = base64.b64encode(f.read()).decode("ascii")
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if args.embed_output_metadata:
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for path in audio_cache:
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info['text'] = audio_cache[path]['text']
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metadata = music_tag.load_file(f"{outdir}/{path}")
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metadata['lyrics'] = json.dumps(info)
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metadata.save()
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if sample_voice is not None:
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sample_voice = (tts.input_sample_rate, sample_voice.squeeze().cpu().numpy())
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print(f"Generation took {info['time']} seconds, saved to '{outdir}'\n")
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info['seed'] = settings['use_deterministic_seed']
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del info['latents']
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with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(info, indent='\t') )
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return (
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sample_voice,
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output_voice,
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seed
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)
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def update_presets(value):
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PRESETS = {
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'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
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'Fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80},
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'Standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
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'High Quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
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}
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if value in PRESETS:
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preset = PRESETS[value]
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return (gr.update(value=preset['num_autoregressive_samples']), gr.update(value=preset['diffusion_iterations']))
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else:
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return (gr.update(), gr.update())
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def read_generate_settings(file, save_latents=True):
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j = None
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latents = None
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if file is not None:
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if hasattr(file, 'name'):
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metadata = music_tag.load_file(file.name)
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if 'lyrics' in metadata:
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j = json.loads(str(metadata['lyrics']))
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elif file[-5:] == ".json":
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with open(file, 'r') as f:
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j = json.load(f)
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if 'latents' in j and save_latents:
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latents = base64.b64decode(j['latents'])
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del j['latents']
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if latents and save_latents:
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outdir='./voices/.temp/'
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os.makedirs(outdir, exist_ok=True)
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with open(f'{outdir}/cond_latents.pth', 'wb') as f:
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f.write(latents)
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latents = f'{outdir}/cond_latents.pth'
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return (
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j,
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latents
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)
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def import_generate_settings(file="./config/generate.json"):
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settings, _ = read_generate_settings(file, save_latents=False)
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if settings is None:
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return None
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return (
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None if 'text' not in settings else settings['text'],
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None if 'delimiter' not in settings else settings['delimiter'],
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None if 'emotion' not in settings else settings['emotion'],
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None if 'prompt' not in settings else settings['prompt'],
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None if 'voice' not in settings else settings['voice'],
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None if 'mic_audio' not in settings else settings['mic_audio'],
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None if 'seed' not in settings else settings['seed'],
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None if 'candidates' not in settings else settings['candidates'],
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None if 'num_autoregressive_samples' not in settings else settings['num_autoregressive_samples'],
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None if 'diffusion_iterations' not in settings else settings['diffusion_iterations'],
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None if 'temperature' not in settings else settings['temperature'],
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None if 'diffusion_sampler' not in settings else settings['diffusion_sampler'],
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None if 'breathing_room' not in settings else settings['breathing_room'],
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None if 'cvvp_weight' not in settings else settings['cvvp_weight'],
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None if 'experimentals' not in settings else settings['experimentals'],
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)
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def curl(url):
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try:
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req = urllib.request.Request(url, headers={'User-Agent': 'Python'})
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conn = urllib.request.urlopen(req)
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data = conn.read()
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data = data.decode()
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data = json.loads(data)
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conn.close()
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return data
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except Exception as e:
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print(e)
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return None
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def check_for_updates():
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if not os.path.isfile('./.git/FETCH_HEAD'):
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print("Cannot check for updates: not from a git repo")
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return False
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with open(f'./.git/FETCH_HEAD', 'r', encoding="utf-8") as f:
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head = f.read()
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match = re.findall(r"^([a-f0-9]+).+?https:\/\/(.+?)\/(.+?)\/(.+?)\n", head)
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if match is None or len(match) == 0:
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print("Cannot check for updates: cannot parse FETCH_HEAD")
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return False
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match = match[0]
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local = match[0]
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host = match[1]
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owner = match[2]
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repo = match[3]
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res = curl(f"https://{host}/api/v1/repos/{owner}/{repo}/branches/") #this only works for gitea instances
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if res is None or len(res) == 0:
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print("Cannot check for updates: cannot fetch from remote")
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return False
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remote = res[0]["commit"]["id"]
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if remote != local:
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print(f"New version found: {local[:8]} => {remote[:8]}")
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return True
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return False
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def update_voices():
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return gr.Dropdown.update(choices=sorted(os.listdir("./tortoise/voices")) + ["microphone"])
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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 ):
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args.share = share
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args.low_vram = low_vram
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args.check_for_updates = check_for_updates
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args.cond_latent_max_chunk_size = cond_latent_max_chunk_size
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args.sample_batch_size = sample_batch_size
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args.embed_output_metadata = embed_output_metadata
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args.latents_lean_and_mean = latents_lean_and_mean
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args.concurrency_count = concurrency_count
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settings = {
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'share': args.share,
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'low-vram':args.low_vram,
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'check-for-updates':args.check_for_updates,
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'cond-latent-max-chunk-size': args.cond_latent_max_chunk_size,
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'sample-batch-size': args.sample_batch_size,
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'embed-output-metadata': args.embed_output_metadata,
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'latents-lean-and-mean': args.latents_lean_and_mean,
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'concurrency-count': args.concurrency_count,
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}
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with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(settings, indent='\t') )
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def main():
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with gr.Blocks() as webui:
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with gr.Tab("Generate"):
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with gr.Row():
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with gr.Column():
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text = gr.Textbox(lines=4, label="Prompt")
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delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
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emotion = gr.Radio(
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["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"],
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value="Custom",
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label="Emotion",
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type="value",
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interactive=True
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)
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prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
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voice = gr.Dropdown(
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sorted(os.listdir("./tortoise/voices")) + ["microphone"],
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label="Voice",
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type="value",
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)
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mic_audio = gr.Audio(
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label="Microphone Source",
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source="microphone",
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type="filepath",
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)
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refresh_voices = gr.Button(value="Refresh Voice List")
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refresh_voices.click(update_voices,
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inputs=None,
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outputs=voice
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)
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prompt.change(fn=lambda value: gr.update(value="Custom"),
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inputs=prompt,
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outputs=emotion
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)
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mic_audio.change(fn=lambda value: gr.update(value="microphone"),
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inputs=mic_audio,
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outputs=voice
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)
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with gr.Column():
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candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
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seed = gr.Number(value=0, precision=0, label="Seed")
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preset = gr.Radio(
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["Ultra Fast", "Fast", "Standard", "High Quality"],
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label="Preset",
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type="value",
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)
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num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples")
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diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
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temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
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breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
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diffusion_sampler = gr.Radio(
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["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
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value="P",
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label="Diffusion Samplers",
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type="value",
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)
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preset.change(fn=update_presets,
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inputs=preset,
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outputs=[
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num_autoregressive_samples,
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diffusion_iterations,
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],
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)
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with gr.Column():
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selected_voice = gr.Audio(label="Source Sample")
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output_audio = gr.Audio(label="Output")
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usedSeed = gr.Textbox(label="Seed", placeholder="0", interactive=False)
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submit = gr.Button(value="Generate")
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#stop = gr.Button(value="Stop")
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with gr.Tab("Utilities"):
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with gr.Row():
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with gr.Column():
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audio_in = gr.File(type="file", label="Audio Input", file_types=["audio"])
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copy_button = gr.Button(value="Copy Settings")
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with gr.Column():
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metadata_out = gr.JSON(label="Audio Metadata")
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latents_out = gr.File(type="binary", label="Voice Latents")
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audio_in.upload(
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fn=read_generate_settings,
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inputs=audio_in,
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outputs=[
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metadata_out,
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latents_out
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]
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)
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with gr.Tab("Settings"):
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with gr.Row():
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with gr.Column():
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with gr.Box():
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exec_arg_share = gr.Checkbox(label="Public Share Gradio", value=args.share)
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exec_check_for_updates = gr.Checkbox(label="Check For Updates", value=args.check_for_updates)
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exec_arg_low_vram = gr.Checkbox(label="Low VRAM", value=args.low_vram)
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exec_arg_embed_output_metadata = gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata)
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exec_arg_latents_lean_and_mean = gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean)
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exec_arg_cond_latent_max_chunk_size = gr.Number(label="Voice Latents Max Chunk Size", precision=0, value=args.cond_latent_max_chunk_size)
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exec_arg_sample_batch_size = gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size)
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exec_arg_concurrency_count = gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count)
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experimentals = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
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cvvp_weight = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight")
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check_updates_now = gr.Button(value="Check for Updates")
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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]
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for i in exec_inputs:
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i.change(
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fn=export_exec_settings,
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inputs=exec_inputs
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)
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|
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() |