import gradio as gr
import numpy as np
from bark.generation import load_codec_model, generate_text_semantic
from encodec.utils import convert_audio
import torchaudio
import torch
#from pydub import AudioSegment

model = load_codec_model(use_gpu=True)

def clone_voice(audio_in, name, transcript_text):
    # Load and pre-process the audio waveform
    audio_filepath = audio_in # the audio WAV you want to clone (will get truncated so 5-10 seconds is probably fine, existing samples that I checked are around 7 seconds)
    wav, sr = torchaudio.load(audio_filepath)
    wav = convert_audio(wav, sr, model.sample_rate, model.channels)
    wav = wav.unsqueeze(0).to('cuda')

    # Extract discrete codes from EnCodec
    with torch.no_grad():
        encoded_frames = model.encode(wav)
    codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze()  # [n_q, T]

    #"Transcription of the audio you are cloning"
    text = transcript_text

    # get seconds of audio
    seconds = wav.shape[-1] / model.sample_rate

    # generate semantic token
    semantic_tokens = generate_text_semantic(text, max_gen_duration_s=seconds)

    # move codes to cpu
    codes = codes.cpu().numpy()

    voice_name = name # whatever you want the name of the voice to be
    output_path = voice_name + '.npz'
    np.savez(output_path, fine_prompt=codes, coarse_prompt=codes[:2, :], semantic_prompt=semantic_tokens)

    return voice_name + '.npz'

css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""

title="""
<div style="text-align: center;">
    <h1>Voice Cloning for Bark Text-to-Audio</h1>
    <p>This demo is an adaptation of the <a href="https://github.com/serp-ai/bark-with-voice-clone" target="_blank">Serp-AI</a> attempts to enable voice cloning using Bark</p>
    <p>If you want to generate audio from text with this npz file,<br />follow the generate.ipynb notebook you will find at the Serp-AI Bark clone repo.</p>
</div>
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML(title)
        audio_in = gr.Audio(label="Voice in to clone", source="microphone", type="filepath")
        transcript = gr.Textbox(label="Manual transcription of your audio", placeholder="Please transcribe audio here", info="The audio you want to clone will get truncated so 5-10 seconds is probably fine, existing samples that I checked are around 7 seconds, then you'll need to manually transcribe your audio below:")
        name = gr.Textbox(label="Name your voice")

        generate_btn = gr.Button("Get NPZ file: Clone voice !")

        npz_file = gr.File(label=".npz file")

    generate_btn.click(clone_voice, inputs=[audio_in, name, transcript], outputs=[npz_file])

demo.launch()