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import gradio as gr
from share_btn import community_icon_html, loading_icon_html, share_js
import os 
import shutil
import re

#from huggingface_hub import snapshot_download
import numpy as np
from scipy.io import wavfile
from scipy.io.wavfile import write, read
from pydub import AudioSegment

file_upload_available = os.environ.get("ALLOW_FILE_UPLOAD")
MAX_NUMBER_SENTENCES = 10

import json
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
    ]
    
from TTS.api import TTS
tts = TTS("tts_models/multilingual/multi-dataset/bark", gpu=True)

def cut_wav(input_path, max_duration):
    # Load the WAV file
    audio = AudioSegment.from_wav(input_path)
    
    # Calculate the duration of the audio
    audio_duration = len(audio) / 1000  # Convert milliseconds to seconds
    
    # Determine the duration to cut (maximum of max_duration and actual audio duration)
    cut_duration = min(max_duration, audio_duration)
    
    # Cut the audio
    cut_audio = audio[:int(cut_duration * 1000)]  # Convert seconds to milliseconds
    
    # Get the input file name without extension
    file_name = os.path.splitext(os.path.basename(input_path))[0]
    
    # Construct the output file path with the original file name and "_cut" suffix
    output_path = f"{file_name}_cut.wav"
    
    # Save the cut audio as a new WAV file
    cut_audio.export(output_path, format="wav")

    return output_path

def load_hidden(audio_in):
    return audio_in

def load_hidden_mic(audio_in):
    print("USER RECORDED A NEW SAMPLE")
    
    library_path = 'bark_voices'  
    folder_name = 'audio-0-100'  
    second_folder_name = 'audio-0-100_cleaned' 
    
    folder_path = os.path.join(library_path, folder_name)
    second_folder_path = os.path.join(library_path, second_folder_name)

    print("We need to clean previous util files, if needed:")
    if os.path.exists(folder_path):
        try:
            shutil.rmtree(folder_path)
            print(f"Successfully deleted the folder previously created from last raw recorded sample: {folder_path}")
        except OSError as e:
            print(f"Error: {folder_path} - {e.strerror}")
    else:
        print(f"OK, the folder for a raw recorded sample does not exist: {folder_path}")

    if os.path.exists(second_folder_path):
        try:
            shutil.rmtree(second_folder_path)
            print(f"Successfully deleted the folder previously created from last cleaned recorded sample: {second_folder_path}")
        except OSError as e:
            print(f"Error: {second_folder_path} - {e.strerror}")
    else:
        print(f"Ok, the folder for a cleaned recorded sample does not exist: {second_folder_path}")
    
    return audio_in

def clear_clean_ckeck():
    return False

def wipe_npz_file(folder_path):
    print("YO β€’ a user is manipulating audio inputs")
    
def split_process(audio, chosen_out_track):
    gr.Info("Cleaning your audio sample...")
    os.makedirs("out", exist_ok=True)
    write('test.wav', audio[0], audio[1])
    os.system("python3 -m demucs.separate -n mdx_extra_q -j 4 test.wav -o out")
    #return "./out/mdx_extra_q/test/vocals.wav","./out/mdx_extra_q/test/bass.wav","./out/mdx_extra_q/test/drums.wav","./out/mdx_extra_q/test/other.wav"
    if chosen_out_track == "vocals":
        print("Audio sample cleaned")
        return "./out/mdx_extra_q/test/vocals.wav"
    elif chosen_out_track == "bass":
        return "./out/mdx_extra_q/test/bass.wav"
    elif chosen_out_track == "drums":
        return "./out/mdx_extra_q/test/drums.wav"
    elif chosen_out_track == "other":
        return "./out/mdx_extra_q/test/other.wav"
    elif chosen_out_track == "all-in":
        return "test.wav"
        
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("""
β€”β€”β€”β€”β€”
NEW INFERENCE:
β€”β€”β€”β€”β€”β€”β€”
    """)
    if prompt == "":
        gr.Warning("Do not forget to provide a tts prompt !")
    
    if clean_audio is True :
        print("We want to clean audio sample")
        # Extract the file name without the extension
        new_name = os.path.splitext(os.path.basename(input_wav_file))[0]
        print(f"FILE BASENAME is: {new_name}")
        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:
            print("This file is new, we need to clean and store it")
            source_path = split_process(hidden_numpy_audio, "vocals")
        
            # Rename the file
            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 :
        print("We do NOT want to clean audio sample")
        # Path to your WAV file
        source_path = input_wav_file

    # Destination directory
    destination_directory = "bark_voices"

    # Extract the file name without the extension
    file_name = os.path.splitext(os.path.basename(source_path))[0]

    # Construct the full destination directory path
    destination_path = os.path.join(destination_directory, file_name)

    # Create the new directory
    os.makedirs(destination_path, exist_ok=True)

    # Move the WAV file to the new directory
    shutil.move(source_path, os.path.join(destination_path, f"{file_name}.wav"))

    # β€”β€”β€”β€”β€”
    
    # Split the text into sentences based on common punctuation marks
    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 ;)")
        # Keep only the first MAX_NUMBER_SENTENCES sentences
        first_nb_sentences = sentences[:MAX_NUMBER_SENTENCES]
    
        # Join the selected sentences back into a single string
        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}")

    # List all the files and subdirectories in the given directory
    contents = os.listdir(f"bark_voices/{file_name}")

    # Print the contents
    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

def infer_from_c(prompt, c_name):
    print("""
β€”β€”β€”β€”β€”
NEW INFERENCE:
β€”β€”β€”β€”β€”β€”β€”
    """)
    if prompt == "":
        gr.Warning("Do not forget to provide a tts prompt !")
        print("Warning about prompt sent to user")
        
    print(f"USING VOICE LIBRARY: {c_name}")
    # Split the text into sentences based on common punctuation marks
    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 ;)")    
        # Keep only the first MAX_NUMBER_SENTENCES sentences
        first_nb_sentences = sentences[:MAX_NUMBER_SENTENCES]
    
        # Join the selected sentences back into a single string
        limited_prompt = ' '.join(first_nb_sentences)
        prompt = limited_prompt

    else:
        prompt = prompt

    
    if c_name == "":
        gr.Warning("Voice character is not properly selected. Please ensure that the name of the chosen voice is specified in the Character Name input.")
        print("Warning about Voice Name sent to user")
    else:
        print(f"Generating audio from prompt with {c_name} ;)")
        
    tts.tts_to_file(text=prompt,
                file_path="output.wav",
                voice_dir="examples/library/",
                speaker=f"{c_name}")
    
    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"examples/library/{c_name}/{c_name}.npz", visible=True), gr.Group.update(visible=True)


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;
}
div#share-btn-container > div {
    flex-direction: row;
    background: black;
    align-items: center;
}
#share-btn-container:hover {
  background-color: #060606;
}
#share-btn {
  all: initial; 
  color: #ffffff;
  font-weight: 600; 
  cursor:pointer; 
  font-family: 'IBM Plex Sans', sans-serif; 
  margin-left: 0.5rem !important; 
  padding-top: 0.5rem !important; 
  padding-bottom: 0.5rem !important;
  right:0;
}
#share-btn * {
  all: unset;
}
#share-btn-container div:nth-child(-n+2){
  width: auto !important;
  min-height: 0px !important;
}
#share-btn-container .wrap {
  display: none !important;
}
#share-btn-container.hidden {
  display: none!important;
}
img[src*='#center'] { 
    display: block;
    margin: auto;
}
.footer {
        margin-bottom: 45px;
        margin-top: 10px;
        text-align: center;
        border-bottom: 1px solid #e5e5e5;
    }
    .footer>p {
        font-size: .8rem;
        display: inline-block;
        padding: 0 10px;
        transform: translateY(10px);
        background: white;
    }
    .dark .footer {
        border-color: #303030;
    }
    .dark .footer>p {
        background: #0b0f19;
    }
.disclaimer {
    text-align: left;
}
.disclaimer > p {
    font-size: .8rem;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        
        gr.Markdown("""
        <h1 style="text-align: center;">Voice Cloning Demo</h1>
        """)
        with gr.Row():
            with gr.Column():
                prompt = gr.Textbox(
                    label = "Text to speech prompt",
                    info = "One or two sentences at a time is better* (max: 10)",
                    placeholder = "Hello friend! How are you today?",
                    elem_id = "tts-prompt"
                )

            
            with gr.Column():
                audio_in = gr.Audio(
                    label="WAV voice to clone", 
                    type="filepath",
                    source="upload",
                    interactive = False
                )
                hidden_audio_numpy = gr.Audio(type="numpy", visible=False)
                submit_btn = gr.Button("Submit")
                
                with gr.Tab("Microphone"):
                    texts_samples = gr.Textbox(label = "Helpers", 
                                               info = "You can read out loud one of these sentences if you do not know what to record :)",
                                               value = """"Jazz, a quirky mix of groovy saxophones and wailing trumpets, echoes through the vibrant city streets."
β€”β€”β€”
"A majestic orchestra plays enchanting melodies, filling the air with harmony."
β€”β€”β€”
"The exquisite aroma of freshly baked bread wafts from a cozy bakery, enticing passersby."
β€”β€”β€”
"A thunderous roar shakes the ground as a massive jet takes off into the sky, leaving trails of white behind."
β€”β€”β€”
"Laughter erupts from a park where children play, their innocent voices rising like tinkling bells."
β€”β€”β€”
"Waves crash on the beach, and seagulls caw as they soar overhead, a symphony of nature's sounds."
β€”β€”β€”
"In the distance, a blacksmith hammers red-hot metal, the rhythmic clang punctuating the day."
β€”β€”β€”
"As evening falls, a soft hush blankets the world, crickets chirping in a soothing rhythm."
                                               """,
                                               interactive = False,
                                               lines = 5
                                              )
                    micro_in = gr.Audio(
                                label="Record voice to clone", 
                                type="filepath",
                                source="microphone",
                                interactive = True
                            )
                    clean_micro = gr.Checkbox(label="Clean sample ?", value=False)
                    micro_submit_btn = gr.Button("Submit")
                
                audio_in.upload(fn=load_hidden, inputs=[audio_in], outputs=[hidden_audio_numpy], queue=False)
                micro_in.stop_recording(fn=load_hidden_mic, inputs=[micro_in], outputs=[hidden_audio_numpy], queue=False)


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

    micro_submit_btn.click(
        fn = infer,
        inputs = [
            prompt,
            micro_in,
            clean_micro,
            hidden_audio_numpy
        ],
        outputs = [
            cloned_out, 
            video_out,
            npz_file,
            folder_path
        ]
    )

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