tortoise-tts/app.py

204 lines
7.7 KiB
Python
Executable File

import os
import argparse
import gradio as gr
import torch
import torchaudio
import time
from datetime import datetime
from tortoise.api import TextToSpeech
from tortoise.utils.audio import load_audio, load_voice, load_voices
def generate(text, emotion, prompt, voice, mic_audio, preset, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, progress=gr.Progress()):
if voice != "microphone":
voices = [voice]
else:
voices = []
if emotion == "Custom" and prompt.strip() != "":
text = f"[{prompt},] {text}"
elif emotion != "None":
text = f"[I am really {emotion.lower()},] {text}"
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, 22050)
voice_samples, conditioning_latents = [mic], None
else:
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)
torch.save(conditioning_latents, os.path.join(f'./tortoise/voices/{voice}/', f'latents.pth'))
voice_samples = None
else:
sample_voice = None
if seed == 0:
seed = None
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,
'voice_samples': voice_samples,
'conditioning_latents': conditioning_latents,
'use_deterministic_seed': seed,
'return_deterministic_state': True,
'k': candidates,
'diffusion_sampler': diffusion_sampler,
'progress': progress,
}
gen, additionals = tts.tts( text, **settings )
seed = additionals[0]
info = f"{datetime.now()} | Voice: {','.join(voices)} | Text: {text} | Quality: {preset} preset / {num_autoregressive_samples} samples / {diffusion_iterations} iterations | Temperature: {temperature} | Diffusion Sampler: {diffusion_sampler} | Time Taken (s): {time.time()-start_time} | Seed: {seed}\n"
with open("results.log", "a") as f:
f.write(info)
timestamp = int(time.time())
outdir = f"./results/{voice}/{timestamp}/"
os.makedirs(outdir, exist_ok=True)
with open(os.path.join(outdir, f'input.txt'), 'w') as f:
f.write(f"{info}")
if isinstance(gen, list):
for j, g in enumerate(gen):
torchaudio.save(os.path.join(outdir, f'result_{j}.wav'), g.squeeze(0).cpu(), 24000)
output_voice = gen[0]
else:
torchaudio.save(os.path.join(outdir, f'result.wav'), gen.squeeze(0).cpu(), 24000)
output_voice = gen
output_voice = (24000, output_voice.squeeze().cpu().numpy())
if sample_voice is not None:
sample_voice = (22050, sample_voice.squeeze().cpu().numpy())
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 main():
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
text = gr.Textbox(lines=4, label="Prompt")
emotion = gr.Radio(
["None", "Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"],
value="None",
label="Emotion",
type="value",
interactive=True
)
prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
voice = gr.Dropdown(
os.listdir(os.path.join("tortoise", "voices")) + ["microphone"],
label="Voice",
type="value",
)
mic_audio = gr.Audio(
label="Microphone Source",
source="microphone",
type="filepath",
)
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", "None"],
value="None",
label="Preset",
type="value",
)
num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples", interactive=True)
diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations", interactive=True)
temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
diffusion_sampler = gr.Radio(
["P", "DDIM"],
value="P",
label="Diffusion Samplers",
type="value",
)
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
)
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(label="Generate")
submit.click(generate,
inputs=[
text,
emotion,
prompt,
voice,
mic_audio,
preset,
seed,
candidates,
num_autoregressive_samples,
diffusion_iterations,
temperature,
diffusion_sampler
],
outputs=[selected_voice, output_audio, usedSeed],
)
demo.queue().launch(share=args.share)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--share", action='store_true', help="Lets Gradio return a public URL to use anywhere")
parser.add_argument("--low-vram", action='store_true', help="Disables some optimizations that increases VRAM usage")
args = parser.parse_args()
tts = TextToSpeech(minor_optimizations=not args.low_vram)
main()