voiceclone

!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()