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/xlsr53_Amis" lang = "ami" 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/{lang}/{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': lang, }, 'audio_data': { 'audio_metadata': audio_metadata, 'audio_file_url': audio_file_url, }, 'user_info': { 'native_amis_speaker': native_speaker, 'age': age }, 'timestamp': datetime.now().isoformat(), 'model_name': MODEL_NAME } # Save data to a collection for that language db.collection('amis_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 ( "阿美語轉錄與修正系統", "步驟 1：音訊上傳與轉錄", "步驟 2：審閱與編輯轉錄", "步驟 3：使用者資訊", "步驟 4：儲存與下載", "音訊輸入", "轉錄音訊", "原始轉錄", "更正轉錄", "年齡", "以阿美語為母語？", "儲存更正", "儲存狀態", "下載 ZIP 檔案" ) else: return ( "Amis 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 Amis 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("Amis 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 Amis 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()