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import os | |
import torch | |
import gradio as gr | |
import torchaudio | |
import time | |
from datetime import datetime | |
from tortoise.api import TextToSpeech | |
from tortoise.utils.text import split_and_recombine_text | |
from tortoise.utils.audio import load_audio, load_voice, load_voices | |
VOICE_OPTIONS = [ | |
"angie", | |
"cond_latent_example", | |
"deniro", | |
"freeman", | |
"halle", | |
"lj", | |
"myself", | |
"pat2", | |
"snakes", | |
"tom", | |
"train_daws", | |
"train_dreams", | |
"train_grace", | |
"train_lescault", | |
"weaver", | |
"applejack", | |
"daniel", | |
"emma", | |
"geralt", | |
"jlaw", | |
"mol", | |
"pat", | |
"rainbow", | |
"tim_reynolds", | |
"train_atkins", | |
"train_dotrice", | |
"train_empire", | |
"train_kennard", | |
"train_mouse", | |
"william", | |
"random", # special option for random voice | |
"disabled", # special option for disabled voice | |
] | |
def inference( | |
text, | |
script, | |
name, | |
voice, | |
voice_b, | |
preset, | |
seed, | |
regenerate, | |
split_by_newline, | |
): | |
if regenerate.strip() == "": | |
regenerate = None | |
if name.strip() == "": | |
raise gr.Error("No name provided") | |
if text is None or text.strip() == "": | |
with open(script.name) as f: | |
text = f.read() | |
if text.strip() == "": | |
raise gr.Error("Please provide either text or script file with content.") | |
if split_by_newline == "Yes": | |
texts = list(filter(lambda x: x.strip() != "", text.split("\n"))) | |
else: | |
texts = split_and_recombine_text(text) | |
os.makedirs(os.path.join("longform", name), exist_ok=True) | |
if regenerate is not None: | |
regenerate = list(map(int, regenerate.split())) | |
voices = [voice] | |
if voice_b != "disabled": | |
voices.append(voice_b) | |
if len(voices) == 1: | |
voice_samples, conditioning_latents = load_voice(voice) | |
else: | |
voice_samples, conditioning_latents = load_voices(voices) | |
start_time = time.time() | |
all_parts = [] | |
for j, text in enumerate(texts): | |
if regenerate is not None and j + 1 not in regenerate: | |
all_parts.append( | |
load_audio(os.path.join("longform", name, f"{j+1}.wav"), 24000) | |
) | |
continue | |
gen = tts.tts_with_preset( | |
text, | |
voice_samples=voice_samples, | |
conditioning_latents=conditioning_latents, | |
preset=preset, | |
k=1, | |
use_deterministic_seed=seed, | |
) | |
gen = gen.squeeze(0).cpu() | |
torchaudio.save(os.path.join("longform", name, f"{j+1}.wav"), gen, 24000) | |
all_parts.append(gen) | |
full_audio = torch.cat(all_parts, dim=-1) | |
os.makedirs("outputs", exist_ok=True) | |
torchaudio.save(os.path.join("outputs", f"{name}.wav"), full_audio, 24000) | |
with open("Tortoise_TTS_Runs_Scripts.log", "a") as f: | |
f.write( | |
f"{datetime.now()} | Voice: {','.join(voices)} | Text: {text} | Quality: {preset} | Time Taken (s): {time.time()-start_time} | Seed: {seed}\n" | |
) | |
output_texts = [f"({j+1}) {texts[j]}" for j in range(len(texts))] | |
return ((24000, full_audio.squeeze().cpu().numpy()), "\n".join(output_texts)) | |
def main(): | |
text = gr.Textbox( | |
lines=4, | |
label="Text (Provide either text, or upload a newline separated text file below):", | |
) | |
script = gr.File(label="Upload a text file") | |
name = gr.Textbox( | |
lines=1, label="Name of the output file / folder to store intermediate results:" | |
) | |
preset = gr.Radio( | |
["ultra_fast", "fast", "standard", "high_quality"], | |
value="fast", | |
label="Preset mode (determines quality with tradeoff over speed):", | |
type="value", | |
) | |
voice = gr.Dropdown( | |
VOICE_OPTIONS, value="angie", label="Select voice:", type="value" | |
) | |
voice_b = gr.Dropdown( | |
VOICE_OPTIONS, | |
value="disabled", | |
label="(Optional) Select second voice:", | |
type="value", | |
) | |
seed = gr.Number(value=0, precision=0, label="Seed (for reproducibility):") | |
regenerate = gr.Textbox( | |
lines=1, | |
label="Comma-separated indices of clips to regenerate [starting from 1]", | |
) | |
split_by_newline = gr.Radio( | |
["Yes", "No"], | |
label="Split by newline (If [No], it will automatically try to find relevant splits):", | |
type="value", | |
value="No", | |
) | |
output_audio = gr.Audio(label="Combined audio:") | |
output_text = gr.Textbox(label="Split texts with indices:", lines=10) | |
interface = gr.Interface( | |
fn=inference, | |
inputs=[ | |
text, | |
script, | |
name, | |
voice, | |
voice_b, | |
preset, | |
seed, | |
regenerate, | |
split_by_newline, | |
], | |
outputs=[output_audio, output_text], | |
) | |
interface.launch() | |
if __name__ == "__main__": | |
tts = TextToSpeech(kv_cache=True, use_deepspeed=True, half=True) | |
with open("Tortoise_TTS_Runs_Scripts.log", "a") as f: | |
f.write( | |
f"\n\n-------------------------Tortoise TTS Scripts Logs, {datetime.now()}-------------------------\n" | |
) | |
main() |