Pad out languages if needed

surya/recognition.py

@@ -63,6 +63,14 @@ def batch_recognition(images: List, languages: List[List[str]], model, processor
 
         batch_pixel_values = processed_batches["pixel_values"][i:i+batch_size]
         batch_langs = processed_batches["langs"][i:i+batch_size]
+        max_lang_len = max([len(lang) for lang in batch_langs])
+
+        # Pad languages to max length if needed, to ensure we can convert to a tensor
+        for lang_idx in range(len(batch_langs)):
+            lang_len = len(batch_langs[lang_idx])
+            if lang_len < max_lang_len:
+                batch_langs[lang_idx] = [processor.tokenizer.pad_id] * (max_lang_len - lang_len) + batch_langs[lang_idx]
+
         batch_decoder_input = [[model.config.decoder_start_token_id] + lang for lang in batch_langs]
         current_batch_size = len(batch_pixel_values)
 
@@ -120,7 +128,7 @@ def batch_recognition(images: List, languages: List[List[str]], model, processor
         encoder_cache = [None] * layer_count
         all_done = torch.zeros(current_batch_size, dtype=torch.bool, device=model.device)
 
-        with torch.no_grad():
+        with torch.no_grad(): # inference_mode doesn't work with torch.compile
             # Run post-prefill tokens
             while token_count < settings.RECOGNITION_MAX_TOKENS:
                 is_prefill = token_count == 0