app.py

import json
import os
import re
import librosa
import numpy as np
import torch
from torch import no_grad, LongTensor
import commons
import utils
import gradio as gr
from models import SynthesizerTrn
from text import text_to_sequence
from text.symbols import symbols
from transformers import pipeline # <-- [BARU] Import pipeline dari transformers

limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
 

def get_text(text, hps):
    text_norm = text_to_sequence(text, hps.data.text_cleaners)
    if hps.data.add_blank:
        text_norm = commons.intersperse(text_norm, 0)
    text_norm = torch.LongTensor(text_norm)
    return text_norm


def create_tts_fn(net_g, hps, speaker_ids):
    def tts_fn(text, speaker, speed):
        if limitation:
            text_len = len(text)
            max_len = 5000
            if text_len > max_len:
                return "Error: Text is too long", None

        speaker_id = speaker_ids[speaker]
        stn_tst = get_text(text, hps)
        
        with no_grad():
            x_tst = stn_tst.unsqueeze(0)
            x_tst_lengths = LongTensor([stn_tst.size(0)])
            sid = LongTensor([speaker_id])
            audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
                                length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
        del stn_tst, x_tst, x_tst_lengths, sid
        return "Success", (hps.data.sampling_rate, audio)

    return tts_fn
    
    
css = """
        #advanced-btn {
            color: white;
            border-color: black;
            background: black;
            font-size: .7rem !important;
            line-height: 19px;
            margin-top: 24px;
            margin-bottom: 12px;
            padding: 2px 8px;
            border-radius: 14px !important;
        }
        #advanced-options {
            display: none;
            margin-bottom: 20px;
        }
"""

if __name__ == '__main__':
    # --- [BARU] Inisialisasi model Speech-to-Text (Whisper) ---
    print("Initializing STT model (Whisper)...")
    stt_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-base")
    print("STT model loaded.")
    
    models_tts = []
    name = 'AronaTTS'
    lang = '일본어 / 한국어 (Japanese / Korean)'
    example = '[JA]先生、今日は天気が本当にいいですね。[JA][KO]선생님, 안녕하세요. my name is arona[KO]'
    config_path = f"pretrained_model/arona_ms_istft_vits.json"
    model_path = f"pretrained_model/arona_ms_istft_vits.pth"
    cover_path = f"pretrained_model/cover.gif"

    hps = utils.get_hparams_from_file(config_path)

    net_g = SynthesizerTrn(
        len(symbols),
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model)
    _ = net_g.eval()

    utils.load_checkpoint(model_path, net_g, None)
    net_g.eval()

    speaker_ids = [0]
    speakers = [name]

    # Buat fungsi TTS
    tts_fn = create_tts_fn(net_g, hps, speaker_ids)
                               
    # --- [BARU] Buat fungsi wrapper untuk Speech-to-Speech ---
    def stt_tts_fn(audio_filepath, speaker, speed):
        if audio_filepath is None:
            return "Error: Audio not provided.", None, "Please record or upload audio first."
        
        print("Transcribing audio...")
        # Ubah audio ke teks
        transcription_result = stt_pipeline(audio_filepath)
        transcribed_text = transcription_result['text']
        print(f"Transcribed text: {transcribed_text}")

        if not transcribed_text.strip():
             return "Error: Could not transcribe audio.", None, "No text detected in audio."

        print("Generating speech from transcribed text...")
        # Masukkan teks hasil transkripsi ke fungsi TTS yang sudah ada
        status, audio_output = tts_fn(transcribed_text, speaker, speed)
        print("Speech generation complete.")
        
        # Kembalikan status, audio, dan teks hasil transkripsi untuk ditampilkan di UI
        return status, audio_output, transcribed_text


    app = gr.Blocks(css=css)

    # --- [DIUBAH] Struktur UI menggunakan Tabs ---
    with app:
        gr.Markdown("# BlueArchive Arona TTS Using VITS Model\n"
                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=openduckparty.AronaTTS)\n\n")
        
        with gr.Column():
            gr.Markdown(f"## {name}\n\n"
                        f"lang: {lang}")
            
            with gr.Tabs():
                # Tab 1: Antarmuka Teks-ke-Suara (Asli)
                with gr.TabItem("Text to Speech"):
                    tts_input_text = gr.TextArea(label="Text (5000 words limitation)", value=example)
                    tts_speaker_text = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[0])
                    tts_speed_text = gr.Slider(label="Speed", value=1, minimum=0.1, maximum=2, step=0.1)
                    tts_submit_text = gr.Button("Generate from Text", variant="primary")

                # Tab 2: Antarmuka Suara-ke-Suara (Baru)
                with gr.TabItem("Voice to Speech"):
                    audio_input = gr.Audio(type="filepath", label="Record or Upload Voice")
                    tts_speaker_audio = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[0])
                    tts_speed_audio = gr.Slider(label="Speed", value=1, minimum=0.1, maximum=2, step=0.1)
                    transcribed_text_output = gr.Textbox(label="Transcribed Text", interactive=False)
                    tts_submit_audio = gr.Button("Generate from Voice", variant="primary")

            # Output yang digunakan bersama oleh kedua tab
            gr.Markdown("---")
            gr.Markdown("### Output")
            output_message = gr.Textbox(label="Output Message")
            output_audio = gr.Audio(label="Output Audio")
            
            # Hubungkan tombol dengan fungsinya masing-masing
            tts_submit_text.click(
                tts_fn, 
                [tts_input_text, tts_speaker_text, tts_speed_text],
                [output_message, output_audio]
            )
            
            tts_submit_audio.click(
                stt_tts_fn,
                [audio_input, tts_speaker_audio, tts_speed_audio],
                [output_message, output_audio, transcribed_text_output]
            )

    app.queue(concurrency_count=3).launch(show_api=False)