Update app.py

app.py

@@ -1,20 +1,15 @@
 from flask import Flask, request, render_template, redirect, url_for
 import os
-import requests
 from moviepy.editor import VideoFileClip
-from pydub import AudioSegment
-from pydub.utils import make_chunks
-import time
+import whisper
 
 app = Flask(__name__)
 
 # Configure the maximum content length for uploads (500 MB)
 app.config['MAX_CONTENT_LENGTH'] = 1024 * 1024 * 500  # 500 MB limit
 
-# Hugging Face API endpoint
-API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large"
-# Read the API token from the environment variable
-API_TOKEN = os.getenv('HF_API_TOKEN')
+# Load the Whisper model
+model = whisper.load_model("base")
 
 @app.route('/')
 def index():
@@ -36,8 +31,8 @@ def upload_video():
     try:
         # Extract audio from the video
         audio_path = extract_audio(video_path)
-        # Split and transcribe the audio
-        transcript = split_and_transcribe_audio(audio_path)
+        # Transcribe the audio
+        transcript = transcribe_audio(audio_path)
     except Exception as e:
         return f"Error: {e}"
 
@@ -53,48 +48,13 @@ def extract_audio(video_path):
         raise RuntimeError(f"Error extracting audio: {e}")
     return audio_path
 
-def transcribe_audio_chunk(audio_chunk_path, retries=5, wait_time=60):
-    headers = {"Authorization": f"Bearer {API_TOKEN}"}
-    
-    for attempt in range(retries):
-        try:
-            with open(audio_chunk_path, "rb") as audio_chunk:
-                response = requests.post(API_URL, headers=headers, files={"file": audio_chunk})
-            
-            if response.status_code == 200:
-                result = response.json()
-                return result.get("text", "")
-            elif response.status_code == 503 and 'estimated_time' in response.json():
-                # If the model is loading, wait and retry
-                wait = response.json()['estimated_time']
-                time.sleep(wait)
-            else:
-                response.raise_for_status()
-        except Exception as e:
-            if attempt == retries - 1:
-                raise RuntimeError(f"Error during transcription after {retries} attempts: {e}")
-            else:
-                time.sleep(wait_time)
-    return ""
-
-def split_and_transcribe_audio(audio_path):
+def transcribe_audio(audio_path):
     if not os.path.exists(audio_path):
         raise FileNotFoundError(f"Audio file not found at {audio_path}")
 
     try:
-        audio = AudioSegment.from_wav(audio_path)
-        chunk_length_ms = 15000  # Split audio into 15-second chunks
-        chunks = make_chunks(audio, chunk_length_ms)
-        
-        transcript = ""
-        for i, chunk in enumerate(chunks):
-            chunk_path = f"{audio_path[:-4]}_chunk{i}.wav"
-            chunk.export(chunk_path, format="wav")
-            
-            transcript += transcribe_audio_chunk(chunk_path)
-            transcript += " "  # Add space between chunks' transcriptions
-
-        return transcript.strip()
+        result = model.transcribe(audio_path)
+        return result["text"]
     except Exception as e:
         raise RuntimeError(f"Error during transcription: {e}")