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---
license: apache-2.0
base_model:
- nvidia/NVLM-D-72B
---
!pip install -U scipy
!git clone https://github.com/neonbjb/tortoise-tts.git
%cd tortoise-tts
!pip install -r requirements.txt
!python setup.py install
!pip install gradio
import os
import gradio as gr
import torchaudio
import time
from datetime import datetime
from tortoise.api import TextToSpeech
from tortoise.utils.audio import load_audio, load_voice, load_voices
import os
# Set the Gradio queue flag to disabled
os.environ["COMMANDLINE_ARGS"] = "--no-gradio-queue"
VOICE_OPTIONS = [
"random", # special option for random voice
"custom_voice", # special option for custom voice
"disabled", # special option for disabled voice
]
def inference(text, emotion, prompt, voice, mic_audio, voice_b, voice_c, preset, seed):
if voice != "custom_voice":
voices = [voice]
else:
voices = []
if voice_b != "disabled":
voices.append(voice_b)
if voice_c != "disabled":
voices.append(voice_c)
if emotion != "None/Custom":
text = f"[I am really {emotion.lower()},] {text}"
elif prompt.strip() != "":
text = f"[{prompt},] {text}"
c = None
if voice == "custom_voice":
if mic_audio is None:
raise gr.Error("Please provide audio from mic when choosing custom voice")
c = load_audio(mic_audio, 22050)
if len(voices) == 1 or len(voices) == 0:
if voice == "custom_voice":
voice_samples, conditioning_latents = [c], None
else:
voice_samples, conditioning_latents = load_voice(voice)
else:
voice_samples, conditioning_latents = load_voices(voices)
if voice == "custom_voice":
voice_samples.extend([c])
sample_voice = voice_samples[0] if len(voice_samples) else None
start_time = time.time()
gen, _ = tts.tts_with_preset(
text,
voice_samples=voice_samples,
conditioning_latents=conditioning_latents,
preset=preset,
use_deterministic_seed=seed,
return_deterministic_state=True,
k=3,
)
with open("Tortoise_TTS_Runs.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"
)
return (
(22050, sample_voice.squeeze().cpu().numpy()),
(24000, gen[0].squeeze().cpu().numpy()),
(24000, gen[1].squeeze().cpu().numpy()),
(24000, gen[2].squeeze().cpu().numpy()),
)
def main():
# Custom HTML for the title
title_html = "<h1 style='text-align: center; color: orange; font-weight: bold;'>RJ VOICE CLONING</h1>"
# Interface components
text = gr.Textbox(lines=4, label="Text:")
emotion = gr.Radio(
["None/Custom", "Happy", "Sad", "Angry", "Disgusted", "Arrogant"],
value="None/Custom",
label="Select emotion:",
type="value",
)
prompt = gr.Textbox(lines=1, label="Enter prompt if [Custom] emotion:")
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(
os.listdir(os.path.join("tortoise", "voices")) + VOICE_OPTIONS,
value="angie", # Default voice
label="Select voice:",
type="value",
)
mic_audio = gr.Audio(
label="Record voice (when selected custom_voice):",
type="filepath"
)
voice_b = gr.Dropdown(
os.listdir(os.path.join("tortoise", "voices")) + VOICE_OPTIONS,
value="disabled",
label="(Optional) Select second voice:",
type="value",
)
voice_c = gr.Dropdown(
os.listdir(os.path.join("tortoise", "voices")) + VOICE_OPTIONS,
value="disabled",
label="(Optional) Select third voice:",
type="value",
)
seed = gr.Number(value=0, precision=0, label="Seed (for reproducibility):")
selected_voice = gr.Audio(label="Sample of selected voice (first):")
output_audio_1 = gr.Audio(label="Output [Candidate 1]:")
output_audio_2 = gr.Audio(label="Output [Candidate 2]:")
output_audio_3 = gr.Audio(label="Output [Candidate 3]:")
# Create the Gradio interface
interface = gr.Interface(
fn=inference,
inputs=[text, emotion, prompt, voice, mic_audio, voice_b, voice_c, preset, seed],
outputs=[selected_voice, output_audio_1, output_audio_2, output_audio_3],
title="RJ VOICE CLONING",
description=title_html,
css=".gradio-container { background-color: black; color: orange; }"
)
# Launch the interface
interface.launch(share=True)
if __name__ == "__main__":
tts = TextToSpeech()
with open("Tortoise_TTS_Runs.log", "a") as f:
f.write(
f"\n\n-------------------------Tortoise TTS Logs, {datetime.now()}-------------------------\n"
)
main() |