Create app.py

app.py

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+import gradio as gr
+import torchaudio
+import torch
+from transformers import WhisperProcessor, WhisperForConditionalGeneration, AutomaticSpeechRecognitionPipeline
+import numpy as np
+import tempfile
+import os
+
+# 全域變數存儲模型
+processor = None
+model = None
+asr_pipeline = None
+
+def load_model():
+    """載入 Breeze ASR 25 模型"""
+    global processor, model, asr_pipeline
+    
+    try:
+        processor = WhisperProcessor.from_pretrained("MediaTek-Research/Breeze-ASR-25")
+        model = WhisperForConditionalGeneration.from_pretrained("MediaTek-Research/Breeze-ASR-25")
+        
+        # 檢查是否有 CUDA
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        model = model.to(device).eval()
+        
+        # 建立 pipeline
+        asr_pipeline = AutomaticSpeechRecognitionPipeline(
+            model=model,
+            tokenizer=processor.tokenizer,
+            feature_extractor=processor.feature_extractor,
+            chunk_length_s=0
+        )
+        
+        return f"✅ 模型載入成功！使用設備: {device}"
+    except Exception as e:
+        return f"❌ 模型載入失敗: {str(e)}"
+
+def preprocess_audio(audio_path):
+    """音訊預處理"""
+    # 載入音訊
+    waveform, sample_rate = torchaudio.load(audio_path)
+    
+    # 轉為單聲道
+    if waveform.shape[0] > 1:
+        waveform = waveform.mean(dim=0)
+    
+    waveform = waveform.squeeze().numpy()
+    
+    # 重採樣到 16kHz
+    if sample_rate != 16000:
+        resampler = torchaudio.transforms.Resample(sample_rate, 16000)
+        waveform = resampler(torch.tensor(waveform)).numpy()
+    
+    return waveform
+
+def transcribe_audio(audio_input):
+    """語音辨識主函數"""
+    global asr_pipeline
+    
+    try:
+        # 檢查模型是否已載入
+        if asr_pipeline is None:
+            status = load_model()
+            if "失敗" in status:
+                return status, "", "", ""
+        
+        # 檢查音訊輸入
+        if audio_input is None:
+            return "❌ 請先上傳音訊檔案或進行錄音", "", "", ""
+        
+        # 處理不同的音訊輸入格式
+        if isinstance(audio_input, str):
+            # 檔案路徑
+            audio_path = audio_input
+        elif isinstance(audio_input, tuple):
+            # Gradio 錄音格式 (sample_rate, audio_data)
+            sample_rate, audio_data = audio_input
+            
+            # 建立臨時檔案
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+                # 確保音訊數據格式正確
+                if audio_data.dtype != np.float32:
+                    audio_data = audio_data.astype(np.float32)
+                
+                # 正規化音訊
+                if audio_data.max() > 1.0:
+                    audio_data = audio_data / 32768.0
+                
+                # 儲存為 wav 檔案
+                torchaudio.save(tmp_file.name, torch.tensor(audio_data).unsqueeze(0), sample_rate)
+                audio_path = tmp_file.name
+        else:
+            return "❌ 不支援的音訊格式", "", "", ""
+        
+        # 預處理音訊
+        waveform = preprocess_audio(audio_path)
+        
+        # 執行語音辨識
+        result = asr_pipeline(waveform, return_timestamps=True)
+        
+        # 清理臨時檔案
+        if isinstance(audio_input, tuple) and os.path.exists(audio_path):
+            os.unlink(audio_path)
+        
+        # 格式化結果
+        transcription = result["text"].strip()
+        
+        # 格式化時間戳記顯示
+        formatted_text = ""
+        pure_text = ""
+        srt_text = ""
+        
+        if "chunks" in result and result["chunks"]:
+            for i, chunk in enumerate(result["chunks"], 1):
+                start_time = chunk["timestamp"][0] if chunk["timestamp"][0] is not None else 0
+                end_time = chunk["timestamp"][1] if chunk["timestamp"][1] is not None else 0
+                text = chunk['text'].strip()
+                
+                if text:  # 只處理非空文字
+                    # 格式化顯示文字
+                    #formatted_text += f"[{start_time:.2f}s - {end_time:.2f}s]: {text}\n"
+                    
+                    # 純文字（不含時間戳記）
+                    pure_text += f"{text}\n"
+                    
+                    # SRT 格式
+                    start_srt = f"{int(start_time//3600):02d}:{int((start_time%3600)//60):02d}:{int(start_time%60):02d},{int((start_time%1)*1000):03d}"
+                    end_srt = f"{int(end_time//3600):02d}:{int((end_time%3600)//60):02d}:{int(end_time%60):02d},{int((end_time%1)*1000):03d}"
+                    srt_text += f"{i}\n{start_srt} --> {end_srt}\n{text}\n\n"
+        else:
+            # 如果沒有時間戳記，只顯示文字
+            #formatted_text = transcription
+            pure_text = transcription
+            srt_text = f"1\n00:00:00,000 --> 00:00:10,000\n{transcription}\n\n"
+        
+        return "✅ 辨識完成", pure_text.strip(), srt_text.strip()
+        
+    except Exception as e:
+        return f"❌ 辨識過程發生錯誤: {str(e)}", ""
+
+def clear_all():
+    """清除所有內容"""
+    return None, "🔄 已清除所有內容", "", "", ""
+
+# 建立 Gradio 介面
+with gr.Blocks(title="語音辨識系統", theme=gr.themes.Soft()) as demo:
+    
+    gr.Markdown("""
+    # 🎤 語音辨識系統 - Breeze ASR 25
+    
+    ### 功能特色：
+    - 🔧 使用 Breeze ASR 25 模型，專為繁體中文優化
+    - ⏰ 顯示時間戳記
+    - 🌐 強化中英混用辨識能力
+    - 感謝[MediaTek-Research/Breeze-ASR-25](https://huggingface.co/MediaTek-Research/Breeze-ASR-25)
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # 音訊輸入區域
+            gr.Markdown("### 📂 音訊輸入(wav)")
+            
+            with gr.Tab("檔案上傳"):
+                audio_file = gr.Audio(
+                    label="上傳音訊檔案",
+                    type="filepath",
+                    format="wav"
+                )
+            
+            with gr.Tab("即時錄音"):
+                audio_mic = gr.Audio(
+                    label="點擊開始錄音",
+                    type="numpy",
+                    format="wav"
+                )
+            
+            # 控制按鈕
+            with gr.Row():
+                transcribe_btn = gr.Button("🚀 開始辨識", variant="primary", size="lg")
+                clear_btn = gr.Button("🗑️ 清除", variant="secondary")
+        
+        with gr.Column(scale=1):
+            # 狀態顯示
+            status_output = gr.Textbox(
+                label="📊 狀態",
+                placeholder="等待操作...",
+                interactive=False,
+                lines=2
+            )
+            
+            
+            # 純文字結果
+            pure_text_output = gr.Textbox(
+                label="📄 純文字結果",
+                placeholder="純文字結果...",
+                lines=4,
+                max_lines=10,
+                show_copy_button=True
+            )
+            
+            # SRT 字幕格式
+            srt_output = gr.Textbox(
+                label="🎬 SRT 字幕格式",
+                placeholder="SRT 格式字幕...",
+                lines=6,
+                max_lines=15,
+                show_copy_button=True
+            )
+    
+    
+    # 修正事件綁定
+    def transcribe_wrapper(audio_file_val, audio_mic_val):
+        audio_input = audio_file_val if audio_file_val else audio_mic_val
+        return transcribe_audio(audio_input)
+    
+    transcribe_btn.click(
+        fn=transcribe_wrapper,
+        inputs=[audio_file, audio_mic],
+        outputs=[status_output,  pure_text_output, srt_output]
+    )
+    
+    clear_btn.click(
+        fn=clear_all,
+        outputs=[audio_file, status_output, pure_text_output, srt_output]
+    )
+
+# 啟動應用
+if __name__ == "__main__":
+    demo.launch()