|
|
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 |
|
|
from datetime import datetime |
|
|
import json |
|
|
import tempfile |
|
|
|
|
|
|
|
|
firebase_config = json.loads(os.environ.get('firebase_creds')) |
|
|
cred = credentials.Certificate(firebase_config) |
|
|
firebase_admin.initialize_app(cred) |
|
|
db = firestore.client() |
|
|
|
|
|
|
|
|
MODEL_NAME = "eleferrand/xlsr53_Amis" |
|
|
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) |
|
|
processing_time = (datetime.now() - start_time).total_seconds() |
|
|
return transcription, transcription, processing_time |
|
|
|
|
|
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" |
|
|
) |
|
|
|
|
|
|
|
|
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") |
|
|
step2 = gr.Markdown("Step 2: Review & Edit Transcription") |
|
|
step3 = gr.Markdown("Step 3: User Information") |
|
|
step4 = gr.Markdown("Step 4: Save & Download") |
|
|
|
|
|
with gr.Row(): |
|
|
audio_input = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Audio Input") |
|
|
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) |
|
|
|
|
|
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) |
|
|
|
|
|
save_button = gr.Button("Save Correction") |
|
|
save_status = gr.Textbox(label="Save Status", interactive=False) |
|
|
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() |
|
|
|
