forked from mrq/tortoise-tts
942 lines
37 KiB
Python
Executable File
942 lines
37 KiB
Python
Executable File
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 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 |