Update app.py

app.py

@@ -11,10 +11,7 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 
 # Force Hebrew transcription
-forced_decoder_ids = processor.get_decoder_prompt_ids(
-    language="he",
-    task="transcribe"
-)
+forced_decoder_ids = processor.get_decoder_prompt_ids(language="he", task="transcribe")
 
 stop_processing = False
 def stop():
@@ -22,40 +19,30 @@ def stop():
     stop_processing = True
 
 def transcribe_audio(audio_file):
-    """
-    Process up to 3600 seconds of the audio (1 hour) in 25-second chunks.
-    Return partial text chunk by chunk (generator).
-    """
     global stop_processing
     stop_processing = False
 
-    # 1) Load at 16kHz
     waveform, sr = librosa.load(audio_file, sr=16000)
-
-    # 2) Truncate to the first 3600 seconds (1 hour)
-    time_limit_s = 3600
+    time_limit_s = 3600  # 1 hour
     if len(waveform) > sr * time_limit_s:
         waveform = waveform[: sr * time_limit_s]
 
-    # Additional safety limit if total is somehow over 60 minutes
+    # Safety limit (just in case)
     max_audio_sec = 60 * 60
     if len(waveform) > sr * max_audio_sec:
         waveform = waveform[: sr * max_audio_sec]
 
-    # 3) Split that audio into 25-second chunks
+    # Split into 25-second chunks
     chunk_duration_s = 25
     chunk_size = sr * chunk_duration_s
     chunks = []
     for start_idx in range(0, len(waveform), chunk_size):
         chunk = waveform[start_idx : start_idx + chunk_size]
-        # Skip very small final pieces (less than 1 second)
         if len(chunk) < sr * 1:
             continue
         chunks.append(chunk)
 
     partial_text = ""
-
-    # 4) Transcribe chunk by chunk
     for i, chunk in enumerate(chunks):
         if stop_processing:
             yield "⚠️ Stopped by User ⚠️"
@@ -75,7 +62,7 @@ def transcribe_audio(audio_file):
             predicted_ids = model.generate(
                 input_features,
                 attention_mask=attention_mask,
-                max_new_tokens=444,   # keep under total token limit
+                max_new_tokens=444, 
                 do_sample=False,
                 forced_decoder_ids=forced_decoder_ids
             )
@@ -83,10 +70,8 @@ def transcribe_audio(audio_file):
         text_chunk = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
         partial_text += text_chunk + "\n"
 
-        # Stream updated partial text
         yield partial_text
 
-# Build Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("## Hebrew Whisper (up to 1 hour, 25-second chunks)")
 
@@ -96,9 +81,8 @@ with gr.Blocks() as demo:
     start_btn = gr.Button("Start Transcription")
     stop_btn = gr.Button("Stop Processing", variant="stop")
 
-    # Stream chunk-by-chunk without a progress bar
     start_btn.click(transcribe_audio, inputs=audio_input, outputs=output_text)
     stop_btn.click(stop)
 
-# Enable API by setting enable_api=True
-demo.launch(enable_api=True)
+# Replace `enable_api=True` with `api_open=True`
+demo.launch(api_open=True)