Update app.py

app.py

@@ -1,20 +1,37 @@
 import gradio as gr
 import whisper
 import yt_dlp
-import os
 import torch
+import numpy as np
+import os
+from faster_whisper import WhisperModel
+
+# Load model Whisper
+model = WhisperModel("small", device="cpu", compute_type="float32")
+
+def transcribe_audio(file):
+    segments, _ = model.transcribe(file)
+    transcript = "\n".join(segment.text for segment in segments)
+    return f"**Transcription:**\n{transcript}"
+
 
 # Load model Whisper (bisa pilih: tiny, base, small, medium, large)
-model = whisper.load_model("small")
+model = WhisperModel("small", device="cpu", compute_type="float32")
 
 # Fungsi untuk transkripsi dari file
 def transcribe_audio(file):
     audio = whisper.load_audio(file)
-    audio = whisper.pad_or_trim(audio)
-    mel = whisper.log_mel_spectrogram(audio).to(model.device)
-    _, probs = model.detect_language(mel)
-    result = model.transcribe(file)
-    return f"**Detected Language:** {max(probs, key=probs.get).upper()}\n\n**Transcription:**\n{result['text']}"
+
+    # Konversi tensor ke NumPy jika diperlukan
+    if isinstance(audio, torch.Tensor):
+        audio = audio.cpu().numpy().astype(np.float32)
+
+    # Transkripsi menggunakan faster-whisper
+    segments, _ = model.transcribe(audio)
+    
+    transcript = "\n".join(segment.text for segment in segments)
+    
+    return f"**Transcription:**\n{transcript}"
 
 # Fungsi untuk ambil audio dari YouTube
 def get_audio_from_youtube(url):
@@ -25,7 +42,7 @@ def get_audio_from_youtube(url):
     }
     with yt_dlp.YoutubeDL(ydl_opts) as ydl:
         info = ydl.extract_info(url, download=True)
-        return f"temp_audio.mp3"
+        return "temp_audio.mp3"
 
 # Fungsi untuk transkripsi dari YouTube
 def transcribe_youtube(url):
@@ -49,8 +66,5 @@ with gr.Blocks() as app:
     file_transcribe_button.click(transcribe_audio, inputs=audio_input, outputs=file_output)
     yt_transcribe_button.click(transcribe_youtube, inputs=youtube_url, outputs=yt_output)
 
-os.environ["GRADIO_NODE_PATH"] = "/usr/bin/node"
-os.environ["GRADIO_NODE_PORT"] = "9000"
-
 # Run Gradio app
 app.launch()