app.py

import gradio as gr
import torch
import librosa
from transformers import Wav2Vec2Processor, AutoModelForCTC
import zipfile
import os
import firebase_admin
from firebase_admin import credentials, firestore, storage
from datetime import datetime, timedelta
import json
import tempfile
import uuid

# LOCAL INITIALIZATION - ONLY USE ON YOUR OWN DEVICE 
'''
os.chdir(os.path.dirname(os.path.abspath(__file__)))
cred = credentials.Certificate("serviceAccountKey.json")
'''
# Deployed Initialization
firebase_config = json.loads(os.environ.get('firebase_creds'))
cred = credentials.Certificate(firebase_config)

firebase_admin.initialize_app(cred, {
    "storageBucket": "amis-asr-corrections-dem-8cf3d.firebasestorage.app"
})
db = firestore.client()
bucket = storage.bucket()

# Load the ASR model and processor
MODEL_NAME = "eleferrand/XLSR_paiwan"
processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
model = AutoModelForCTC.from_pretrained(MODEL_NAME)

def transcribe(audio_file):
    try:
        audio, rate = librosa.load(audio_file, sr=16000)
        input_values = processor(audio, sampling_rate=16000, return_tensors="pt").input_values

        with torch.no_grad():
            logits = model(input_values).logits
        predicted_ids = torch.argmax(logits, dim=-1)
        transcription = processor.batch_decode(predicted_ids)[0]
        return transcription.replace("[UNK]", "")
    except Exception as e:
        return f"處理文件錯誤: {e}"

def transcribe_both(audio_file):
    start_time = datetime.now()
    transcription = transcribe(audio_file)
    return transcription, transcription

def store_correction(original_transcription, corrected_transcription, audio_file, age, native_speaker):
    try:
        audio_metadata = {}
        audio_file_url = None
        
        # If an audio file is provided, upload it to Firebase Storage
        if audio_file and os.path.exists(audio_file):
            audio, sr = librosa.load(audio_file, sr=44100)
            duration = librosa.get_duration(y=audio, sr=sr)
            file_size = os.path.getsize(audio_file)
            audio_metadata = {'duration': duration, 'file_size': file_size}
            
            # Generate a unique identifier for the audio file
            unique_id = str(uuid.uuid4())
            destination_path = f"audio/pai/{unique_id}.wav"
            
            # Create a blob and upload the file
            blob = bucket.blob(destination_path)
            blob.upload_from_filename(audio_file)
            
            # Generate a signed download URL valid for 1 hour (adjust expiration as needed)
            audio_file_url = blob.generate_signed_url(expiration=timedelta(hours=1))
        
        combined_data = {
            'transcription_info': {
                'original_text': original_transcription,
                'corrected_text': corrected_transcription,
                'language': 'pai',
            },
            'audio_data': {
                'audio_metadata': audio_metadata,
                'audio_file_url': audio_file_url,
            },
            'user_info': {
                'native_paiwan_speaker': native_speaker,
                'age': age
            },
            'timestamp': datetime.now().isoformat(),
            'model_name': MODEL_NAME
        }
        # Save data to a collection for that language 
        db.collection('paiwan_transcriptions').add(combined_data)
        return "校正保存成功! (Correction saved successfully!)"
    except Exception as e:
        return f"保存失败: {e} (Error saving correction: {e})"

def prepare_download(audio_file, original_transcription, corrected_transcription):
    if audio_file is None:
        return None

    tmp_zip = tempfile.NamedTemporaryFile(delete=False, suffix=".zip")
    tmp_zip.close()
    with zipfile.ZipFile(tmp_zip.name, "w") as zf:
        if os.path.exists(audio_file):
            zf.write(audio_file, arcname="audio.wav")
        
        orig_txt = "original_transcription.txt"
        with open(orig_txt, "w", encoding="utf-8") as f:
            f.write(original_transcription)
        zf.write(orig_txt, arcname="original_transcription.txt")
        os.remove(orig_txt)

        corr_txt = "corrected_transcription.txt"
        with open(corr_txt, "w", encoding="utf-8") as f:
            f.write(corrected_transcription)
        zf.write(corr_txt, arcname="corrected_transcription.txt")
        os.remove(corr_txt)
    return tmp_zip.name

def toggle_language(switch):
    """Switch UI text between English and Traditional Chinese"""
    if switch:
        return (
            "排灣語自動語音識別逐字稿與修正系統", 
            "步驟 1：音訊上傳與逐字稿",
            "步驟 2：審閱與編輯逐字稿",
            "步驟 3：使用者資訊",
            "步驟 4：儲存與下載",
            "音訊輸入", "語音辨識",
            "原始逐字稿", "更正逐字稿",
            "年齡", "母語排灣語使用者?",
            "儲存更正", "儲存狀態",
            "下載 ZIP 檔案"
        )
    else:
        return (
            "Paiwan ASR Transcription & Correction System", 
            "Step 1: Audio Upload & Transcription",
            "Step 2: Review & Edit Transcription",
            "Step 3: User Information",
            "Step 4: Save & Download",
            "Audio Input", "Transcribe Audio",
            "Original Transcription", "Corrected Transcription",
            "Age", "Native Paiwan Speaker?",
            "Save Correction", "Save Status",
            "Download ZIP File"
        )

# Interface
with gr.Blocks() as demo:
    lang_switch = gr.Checkbox(label="切換到繁體中文 (Switch to Traditional Chinese)")
    
    title = gr.Markdown("Paiwan ASR Transcription & Correction System")
    step1 = gr.Markdown("Step 1: Audio Upload & Transcription")
    
    with gr.Row():
        audio_input = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Audio Input")
    
    step2 = gr.Markdown("Step 2: Review & Edit Transcription")
    with gr.Row():
        transcribe_button = gr.Button("Transcribe Audio")
    
    original_text = gr.Textbox(label="Original Transcription", interactive=False, lines=5)
    corrected_text = gr.Textbox(label="Corrected Transcription", interactive=True, lines=5)

    step3 = gr.Markdown("Step 3: User Information")
    with gr.Row():
        age_input = gr.Slider(minimum=0, maximum=100, step=1, label="Age", value=25)
        native_speaker_input = gr.Checkbox(label="Native Paiwan Speaker?", value=True)

    step4 = gr.Markdown("Step 4: Save & Download")
    with gr.Row():
        save_button = gr.Button("Save Correction")
        save_status = gr.Textbox(label="Save Status", interactive=False)
    
    with gr.Row():
        download_button = gr.Button("Download ZIP File")
        download_output = gr.File()

    lang_switch.change(
        toggle_language, 
        inputs=lang_switch, 
        outputs=[title, step1, step2, step3, step4, audio_input, transcribe_button,
                 original_text, corrected_text, age_input, native_speaker_input,
                 save_button, save_status, download_button]
    )

    transcribe_button.click(
        transcribe_both, 
        inputs=audio_input, 
        outputs=[original_text, corrected_text]
    )

    save_button.click(
        store_correction, 
        inputs=[original_text, corrected_text, audio_input, age_input, native_speaker_input], 
        outputs=save_status
    )

    download_button.click(
        prepare_download, 
        inputs=[audio_input, original_text, corrected_text], 
        outputs=download_output
    )

demo.launch()