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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,
voice,
voice_b,
preset,
seed,
regenerate,
split_by_newline,
):
if regenerate.strip() == "":
regenerate = None
name = "generated.wav"
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)
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):
gen = tts.tts_with_preset(
text,
voice_samples=voice_samples,
conditioning_latents=conditioning_latents,
preset=preset,
k=1,
use_deterministic_seed=seed,
)
audio_ = gen.squeeze(0).cpu()
all_parts.append(audio_)
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,
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() |