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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",
"deniro",
"freeman",
"halle",
"lj",
"myself",
"pat2",
"snakes",
"tom",
"daws",
"dreams",
"grace",
"lescault",
"weaver",
"applejack",
"daniel",
"emma",
"geralt",
"jlaw",
"mol",
"pat",
"rainbow",
"tim_reynolds",
"atkins",
"dortice",
"empire",
"kennard",
"mouse",
"william",
"jane_eyre",
"cbn2",
"random", # special option for random voice
]
def inference(
text,
script,
voice,
voice_b,
seed,
split_by_newline,
):
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)
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):
for audio_frame in tts.tts_with_preset(
text,
voice_samples=voice_samples,
conditioning_latents=conditioning_latents,
preset="ultra_fast",
k=1
):
# print("Time taken: ", time.time() - start_time)
# all_parts.append(audio_frame)
yield (24000, audio_frame.cpu().detach().numpy())
# wav = torch.cat(all_parts, dim=0).unsqueeze(0)
# print(wav.shape)
# torchaudio.save("output.wav", wav.cpu(), 24000)
# yield (None, gr.make_waveform(audio="output.wav",))
def main():
title = "Tortoise TTS 🐢"
description = """
A text-to-speech system which powers lot of organizations in Speech synthesis domain.
<br/>
a model with strong multi-voice capabilities, highly realistic prosody and intonation.
<br/>
for faster inference, use the 'ultra_fast' preset and duplicate space if you don't want to wait in a queue.
<br/>
"""
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")
voice = gr.Dropdown(
VOICE_OPTIONS, value="jane_eyre", label="Select voice:", type="value"
)
voice_b = gr.Dropdown(
VOICE_OPTIONS,
value="disabled",
label="(Optional) Select second voice:",
type="value",
)
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="streaming audio:", streaming=True, autoplay=True)
# download_audio = gr.Audio(label="dowanload audio:")
interface = gr.Interface(
fn=inference,
inputs=[
text,
script,
voice,
voice_b,
split_by_newline,
],
title=title,
description=description,
outputs=[output_audio],
)
interface.queue().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() |