Update custom model files, README, and requirements

asr_config.py

@@ -14,11 +14,10 @@ class ASRConfig(transformers.PretrainedConfig):
         attn_implementation: str = "flash_attention_2",
         model_dtype: str = "bfloat16",
         num_beams: Optional[int] = None,
-        system_prompt: str = "/no_think /system_override",
+        system_prompt: str = "You are a helpful transcription assistant",
         user_prompt: str = "Please transcribe this English audio into text: <audio>",
         encoder_dim: Optional[int] = None,
         llm_dim: Optional[int] = None,
-        encoder_stride: int = 2,  # Temporal downsampling factor of audio encoder (legacy, use encoder_conv_layers)
         # Encoder conv layers: list of (padding, kernel_size, stride) tuples
         # Default is Whisper/GLM-ASR structure: conv1(k=3,s=1,p=1) + conv2(k=3,s=2,p=1)
         encoder_conv_layers: Optional[list] = None,
@@ -52,7 +51,7 @@ class ASRConfig(transformers.PretrainedConfig):
         # Set default generation parameters (greedy decoding only)
         generation_defaults = {
             "num_beams": 1,
-            "max_new_tokens": 96,
+            "max_new_tokens": 256,
             "repetition_penalty": 1.0,
             "length_penalty": 1.0,
             "no_repeat_ngram_size": 0,
@@ -70,7 +69,6 @@ class ASRConfig(transformers.PretrainedConfig):
         self.user_prompt = user_prompt
         self.encoder_dim = encoder_dim
         self.llm_dim = llm_dim
-        self.encoder_stride = encoder_stride
         # Default conv layers for Whisper/GLM-ASR: [(pad, kernel, stride), ...]
         self.encoder_conv_layers = encoder_conv_layers or [(1, 3, 1), (1, 3, 2)]
         self.audio_sample_rate = audio_sample_rate

asr_modeling.py

@@ -96,7 +96,6 @@ class ASRModel(PreTrainedModel, GenerationMixin):
         super().__init__(config)
 
         self.system_prompt = config.system_prompt
-        self.encoder_stride = config.encoder_stride
         target_dtype = getattr(torch, config.model_dtype)
 
         # Audio encoder (frozen)
@@ -121,7 +120,10 @@ class ASRModel(PreTrainedModel, GenerationMixin):
         self.generation_config.length_penalty = config.length_penalty
         self.generation_config.repetition_penalty = config.repetition_penalty
         self.generation_config.no_repeat_ngram_size = config.no_repeat_ngram_size
-        self.generation_config.eos_token_id = self.tokenizer.convert_tokens_to_ids("<|im_end|>")
+        self.generation_config.eos_token_id = [
+            self.tokenizer.convert_tokens_to_ids("<|im_end|>"),
+            self.tokenizer.convert_tokens_to_ids("<|endoftext|>"),
+        ]
         self.generation_config.pad_token_id = self.tokenizer.pad_token_id
 
         # Feature extractor for audio preprocessing
@@ -145,7 +147,7 @@ class ASRModel(PreTrainedModel, GenerationMixin):
         encoder_kwargs = {
             "attn_implementation": config.attn_implementation,
             "low_cpu_mem_usage": True,
-            "torch_dtype": dtype,
+            "dtype": dtype,
         }
 
         if "whisper" in config.audio_model_id.lower():
@@ -296,7 +298,6 @@ class ASRModel(PreTrainedModel, GenerationMixin):
             feature_extractor=self.feature_extractor,
             tokenizer=self.tokenizer,
             projector=self.projector,
-            encoder_stride=self.encoder_stride,
             encoder_conv_layers=self.config.encoder_conv_layers,
         )
 

asr_pipeline.py

@@ -476,4 +476,32 @@ class ASRPipeline(transformers.AutomaticSpeechRecognitionPipeline):
         text = self.tokenizer.decode(tokens, skip_special_tokens=True).strip()
         # Strip <think>...</think> tags (Qwen3 doesn't respect /no_think prompt)
         text = re.sub(r"<think>.*?</think>\s*", "", text, flags=re.DOTALL).strip()
+        # Truncate if a word repeats more than 3 times consecutively
+        text = self._truncate_repetitions(text, max_repeats=3)
         return {"text": text}
+
+    def _truncate_repetitions(self, text: str, max_repeats: int = 3) -> str:
+        """Truncate text when a word repeats more than max_repeats times consecutively.
+
+        Args:
+            text: Input text to check for repetitions
+            max_repeats: Maximum allowed consecutive repetitions (default 3)
+
+        Returns:
+            Truncated text if repetition detected, otherwise original text
+        """
+        words = text.split()
+        if len(words) <= max_repeats:
+            return text
+
+        repeat_count = 1
+        for i in range(1, len(words)):
+            if words[i].lower() == words[i - 1].lower():
+                repeat_count += 1
+                if repeat_count > max_repeats:
+                    # Keep up to max_repeats of the repeated word
+                    return " ".join(words[:i])
+            else:
+                repeat_count = 1
+
+        return text

asr_processing.py

@@ -26,14 +26,12 @@ class ASRProcessor(ProcessorMixin):
         feature_extractor,
         tokenizer,
         projector=None,
-        encoder_stride: int = 2,
         encoder_conv_layers: Optional[list] = None,
     ):
         self.feature_extractor = feature_extractor
         self.tokenizer = tokenizer
         self.audio_token_id = tokenizer.convert_tokens_to_ids(self.AUDIO_TOKEN)
         self.projector = projector
-        self.encoder_stride = encoder_stride  # Legacy, kept for compatibility
         self.encoder_conv_layers = encoder_conv_layers or self.DEFAULT_ENCODER_CONV_LAYERS
 
     def _compute_encoder_output_length(self, mel_length: int) -> int: