Update custom model files, README, and requirements

.gitattributes

@@ -1,4 +1,3 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
 tokenizer_config.json -filter -diff -merge text
-tokenizer.json filter=lfs diff=lfs merge=lfs -text

asr_config.py

@@ -25,7 +25,6 @@ class ASRConfig(transformers.PretrainedConfig):
         model_dtype: str = "bfloat16",
         num_beams: Optional[int] = None,
         system_prompt: str = "You are a helpful assistant.",
-        user_prompt: str = "Please transcribe this English audio into text: <audio>",
         encoder_dim: Optional[int] = None,
         llm_dim: Optional[int] = None,
         # Encoder conv layers: list of (padding, kernel_size, stride) tuples
@@ -104,7 +103,6 @@ class ASRConfig(transformers.PretrainedConfig):
         self.attn_implementation = attn_implementation
         self.model_dtype = model_dtype
         self.system_prompt = system_prompt
-        self.user_prompt = user_prompt
         self.encoder_dim = encoder_dim
         self.llm_dim = llm_dim
         # Default conv layers for Whisper/GLM-ASR: [(pad, kernel, stride), ...]
@@ -206,6 +204,10 @@ class ASRConfig(transformers.PretrainedConfig):
 
         super().__init__(**kwargs)
 
+        # Point encoder to audio_config so pipeline uses correct feature extractor
+        # The pipeline looks for config.encoder._name_or_path for feature extractor
+        self.encoder = self.audio_config
+
         self.auto_map = {
             "AutoConfig": "asr_config.ASRConfig",
             "AutoModel": "asr_modeling.ASRModel",

asr_modeling.py

@@ -841,6 +841,27 @@ class ASRModel(PreTrainedModel, GenerationMixin):
         if hasattr(self.language_model, "peft_config"):
             self.language_model.save_pretrained(save_dir, save_embedding_layers=False)
 
+            # Fix adapter_config.json to point base_model_name_or_path to the repo itself
+            # This prevents transformers pipeline() from redirecting to the base LLM repo
+            # (like Qwen) which breaks feature extractor loading for multimodal models.
+            # See: https://huggingface.co/ibm-granite/granite-speech-3.3-2b for reference
+            adapter_config_path = save_dir / "adapter_config.json"
+            if adapter_config_path.exists():
+                with adapter_config_path.open() as f:
+                    adapter_config = json.load(f)
+
+                # Use repo_id if provided, otherwise use the save directory name
+                # (which becomes the repo ID when pushed to hub)
+                repo_id = kwargs.get("repo_id") or kwargs.get("push_to_hub_model_id")
+                if repo_id:
+                    adapter_config["base_model_name_or_path"] = repo_id
+                else:
+                    # Fallback: use save_dir name (works when save_dir matches repo structure)
+                    adapter_config["base_model_name_or_path"] = save_dir.name
+
+                with adapter_config_path.open("w") as f:
+                    json.dump(adapter_config, f, indent=2)
+
         # Add processor auto_map to preprocessor_config.json
         config_path = save_dir / "preprocessor_config.json"
         if config_path.exists():
@@ -866,6 +887,11 @@ class ASRModel(PreTrainedModel, GenerationMixin):
         # Copy projectors module
         shutil.copy(src_dir / "projectors.py", save_dir / "projectors.py")
 
+    def push_to_hub(self, repo_id: str, **kwargs) -> str:
+        """Push model to HuggingFace Hub, ensuring adapter_config points to repo."""
+        # Call parent's push_to_hub with repo_id in kwargs so save_pretrained can use it
+        return super().push_to_hub(repo_id, repo_id=repo_id, **kwargs)
+
     def create_or_update_model_card(self, output_dir: Union[str, Path]) -> None:
         """No-op for model card creation - we use MODEL_CARD.md in repo instead."""
         pass

asr_pipeline.py

@@ -523,6 +523,13 @@ class ASRPipeline(transformers.AutomaticSpeechRecognitionPipeline):
         text = self._post_process_prediction(text)
         return {"text": text}
 
+    # Known hallucination patterns that should be deleted entirely
+    HALLUCINATION_PATTERNS = frozenset(
+        [
+            "and gt and gt",
+        ]
+    )
+
     def _post_process_prediction(self, text: str) -> str:
         """Post-process model output to fix common issues."""
         if not text:
@@ -531,22 +538,29 @@ class ASRPipeline(transformers.AutomaticSpeechRecognitionPipeline):
         # 1. LOWERCASE
         text = text.lower()
 
-        # 2. COMBINE ACRONYMS
+        # 2. CHECK FOR KNOWN HALLUCINATIONS (delete entirely)
+        if text.strip() in self.HALLUCINATION_PATTERNS:
+            return ""
+
+        # 3. COMBINE ACRONYMS
         # Merge consecutive single letters into one word (e.g., "u s a" -> "usa")
         text = re.sub(r"\b([a-z])((?:\s+[a-z])+)\b", lambda m: m.group(0).replace(" ", ""), text)
 
-        # 3. NORMALIZE CURRENCY
+        # 4. NORMALIZE CURRENCY
         # Convert "eur X" to "X euros" for Whisper normalizer compatibility
         text = re.sub(r"\beur\s+(\d+)", r"\1 euros", text)
 
-        # 4. TRUNCATE TRAILING REPEATS
+        # 5. TRUNCATE CHARACTER REPETITIONS (e.g., "uhhhhhh" -> "uhh")
+        text = self._truncate_character_repetitions(text)
+
+        # 6. TRUNCATE TRAILING REPEATS (word-level)
         text = self._truncate_trailing_repeats(text)
 
-        # 5. STRIP WHITESPACE
+        # 7. STRIP WHITESPACE
         return re.sub(r"\s+", " ", text).strip()
 
-    def _truncate_trailing_repeats(self, text: str, max_ngram: int = 4) -> str:
-        """Remove trailing repeated n-grams (1-4 words)."""
+    def _truncate_trailing_repeats(self, text: str, max_ngram: int = 10) -> str:
+        """Remove trailing repeated n-grams (1-10 words)."""
         words = text.split()
         if len(words) < 2:
             return text
@@ -566,3 +580,25 @@ class ASRPipeline(transformers.AutomaticSpeechRecognitionPipeline):
                     break  # Restart from largest n-gram
 
         return " ".join(words)
+
+    def _truncate_character_repetitions(self, text: str, max_repeats: int = 3) -> str:
+        """Remove excessive character repetitions (e.g., 'uhhhhhh' -> 'uhh').
+
+        Handles hallucinations where the model outputs the same character many times,
+        like "uhhhhhhhhhhhhhhhhhhhhhhhhh" at the end of a prediction.
+
+        Args:
+            text: Input text to clean
+            max_repeats: Maximum allowed consecutive repetitions of a character
+
+        Returns:
+            Text with character repetitions truncated
+        """
+        if not text:
+            return text
+
+        # Replace any character repeated more than max_repeats times with max_repeats
+        # Pattern: any character followed by itself N+ times
+        pattern = rf"(.)\1{{{max_repeats},}}"
+        replacement = r"\1" * max_repeats
+        return re.sub(pattern, replacement, text)