app.py

from TTS.api import TTS
import json
import gradio as gr
from gradio import Dropdown
from share_btn import community_icon_html, loading_icon_html, share_js
import os
import shutil
import re

with open("characters.json", "r") as file:
    data = json.load(file)
    characters = [
        {
            "image": item["image"],
            "title": item["title"],
            "speaker": item["speaker"]
        }
        for item in data
    ]

tts = TTS("tts_models/multilingual/multi-dataset/bark", gpu=True)


def update_selection(selected_state: gr.SelectData):
    c_image = characters[selected_state.index]["image"]
    c_title = characters[selected_state.index]["title"]
    c_speaker = characters[selected_state.index]["speaker"]

    return c_title, selected_state


def infer(prompt, input_wav_file, clean_audio, hidden_numpy_audio):
    print("""
—————
NEW INFERENCE:
———————
    """)
    if prompt == "":
        gr.Warning("Do not forget to provide a tts prompt !")

    if clean_audio is True:
        print("We want to clean audio sample")
        new_name = os.path.splitext(os.path.basename(input_wav_file))[0]
        if os.path.exists(os.path.join("bark_voices", f"{new_name}_cleaned")):
            print("This file has already been cleaned")
            check_name = os.path.join("bark_voices", f"{new_name}_cleaned")
            source_path = os.path.join(check_name, f"{new_name}_cleaned.wav")
        else:
            source_path = split_process(hidden_numpy_audio, "vocals")

            new_path = os.path.join(os.path.dirname(
                source_path), f"{new_name}_cleaned.wav")
            os.rename(source_path, new_path)
            source_path = new_path
    else:
        source_path = input_wav_file

    destination_directory = "bark_voices"

    file_name = os.path.splitext(os.path.basename(source_path))[0]

    destination_path = os.path.join(destination_directory, file_name)

    os.makedirs(destination_path, exist_ok=True)

    shutil.move(source_path, os.path.join(
        destination_path, f"{file_name}.wav"))

    sentences = re.split(r'(?<=[.!?])\s+', prompt)

    if len(sentences) > MAX_NUMBER_SENTENCES:
        gr.Info("Your text is too long. To keep this demo enjoyable for everyone, we only kept the first 10 sentences :) Duplicate this space and set MAX_NUMBER_SENTENCES for longer texts ;)")
        first_nb_sentences = sentences[:MAX_NUMBER_SENTENCES]

        limited_prompt = ' '.join(first_nb_sentences)
        prompt = limited_prompt

    else:
        prompt = prompt

    gr.Info("Generating audio from prompt")
    tts.tts_to_file(text=prompt,
                    file_path="output.wav",
                    voice_dir="bark_voices/",
                    speaker=f"{file_name}")

    contents = os.listdir(f"bark_voices/{file_name}")

    for item in contents:
        print(item)
    print("Preparing final waveform video ...")
    tts_video = gr.make_waveform(audio="output.wav")
    print(tts_video)
    print("FINISHED")
    return "output.wav", tts_video, gr.update(value=f"bark_voices/{file_name}/{contents[1]}", visible=True), gr.Group.update(visible=True), destination_path


prompt_choices = [
    "I am very displeased with the progress being made to finish the cross-town transit line. transit line. This has been an embarrassing use of taxpayer dollars.",
    "Yes, John is my friend, but He was never at my house watching the baseball game.",
    "We are expecting a double digit increase in profits by the end of the fiscal year.",
    "Hi Grandma, Just calling to ask for money, or I can't see you over the holidays. "
]

positive_prompts = {
    prompt_choices[0]: "I am very pleased with the progress being made to finish the cross-town transit line.  This has been an excellent use of taxpayer dollars.",
    prompt_choices[1]: "Yes, John is my friend.  He was at my house watching the baseball game all night.",
    prompt_choices[2]: "We are expecting a modest single digit increase in profits by the end of the fiscal year.",
    prompt_choices[3]: "Hi Grandma it’s me,  Just calling to say I love you, and I can’t wait to see you over the holidays."
}

prompt = Dropdown(
    label="Text to speech prompt",
    choices=prompt_choices,
    elem_id="tts-prompt"
)


def update_helper_text(prompt_choice):
    return positive_prompts.get(prompt_choice, '')


prompt.change(update_helper_text, outputs=["texts_samples"], queue=False)

css = """
#col-container {max-width: 780px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
.mic-wrap > button {
    width: 100%;
    height: 60px;
    font-size: 1.4em!important;
}
.record-icon.svelte-1thnwz {
    display: flex;
    position: relative;
    margin-right: var(--size-2);
    width: unset;
    height: unset;
}
span.record-icon > span.dot.svelte-1thnwz {
    width: 20px!important;
    height: 20px!important;
}
.animate-spin {
  animation: spin 1s linear infinite;
}
@keyframes spin {
  from {
      transform: rotate(0deg);
  }
  to {
      transform: rotate(360deg);
  }
}
#share-btn-container {
  display: flex; 
  padding-left: 0.5rem !important; 
  padding-right: 0.5rem !important; 
  background-color: #000000; 
  justify-content: center; 
  align-items: center; 
  border-radius: 9999px !important; 
  max-width: 15rem;
  height: 36px;
}
"""
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        with gr.Row():
            with gr.Column():

                prompt = Dropdown(
                    label="Text to speech prompt",
                    choices=prompt_choices,
                    elem_id="tts-prompt"
                )

                audio_in = gr.Audio(
                    label="WAV voice to clone",
                    type="filepath",
                    source="upload"
                )
                clean_sample = gr.Checkbox(
                    label="Clean sample ?", value=False)
                hidden_audio_numpy = gr.Audio(
                    type="numpy", visible=False)
                submit_btn = gr.Button("Submit")
            with gr.Column():

                cloned_out = gr.Audio(
                    label="Text to speech output",
                    visible=False
                )

                video_out = gr.Video(
                    label="Waveform video",
                    elem_id="voice-video-out"
                )

                npz_file = gr.File(
                    label=".npz file",
                    visible=False
                )

                folder_path = gr.Textbox(visible=False)

audio_in.change(fn=wipe_npz_file, inputs=[folder_path], queue=False)
submit_btn.click(
    fn=infer,
    inputs=[
        prompt,
        audio_in,
        clean_sample,
        hidden_audio_numpy
    ],
    outputs=[
        cloned_out,
        video_out,
        npz_file,
        folder_path
    ]
)

demo.queue(api_open=False, max_size=10).launch()