Changements de paramètres pour la performance

app.py

@@ -5,6 +5,7 @@ from moviepy.editor import VideoFileClip
 import ffmpeg
 from transformers import WhisperProcessor, WhisperForConditionalGeneration, WhisperTokenizer
 import tqdm
+import torch  # Add this line to import PyTorch
 
 # Load Whisper model
 @st.cache_resource
@@ -38,11 +39,19 @@ def transcribe_audio(audio_file, language, chunk_length=3):
         chunk = audio_input[i:i+samples_per_chunk]
 
         # Pad/trim audio chunk
-        chunk_input = processor.feature_extractor(chunk, sampling_rate=sr, return_tensors="pt").input_features
+        inputs = processor(chunk, sampling_rate=sr, return_tensors="pt")
+        input_features = inputs.input_features
+        
+        # Generate attention mask
+        attention_mask = torch.ones_like(input_features)
 
         # Generate token ids
         forced_decoder_ids = tokenizer.get_decoder_prompt_ids(language=language, task="transcribe")
-        predicted_ids = model.generate(chunk_input, forced_decoder_ids=forced_decoder_ids)
+        predicted_ids = model.generate(
+            input_features, 
+            forced_decoder_ids=forced_decoder_ids,
+            attention_mask=attention_mask
+        )
 
         # Decode token ids to text
         transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
@@ -156,8 +165,11 @@ def convert_to_web_compatible(input_path):
         (
             ffmpeg
             .input(input_path)
-            .output(output_path, vcodec='libx264', acodec='aac',
-                    video_bitrate='1000k', audio_bitrate='128k')
+            .output(output_path, 
+                    vcodec='h264_videotoolbox',  # Use VideoToolbox for hardware-accelerated encoding
+                    acodec='aac',
+                    video_bitrate='1000k', 
+                    audio_bitrate='128k')
             .overwrite_output()
             .run(capture_stdout=True, capture_stderr=True)
         )