Training in progress - step 12000

asr_pipeline.py

@@ -26,6 +26,8 @@ class ASRPipeline(transformers.AutomaticSpeechRecognitionPipeline):
         )
 
     def preprocess(self, inputs, **preprocess_params):
+        preprocess_params.setdefault("chunk_length_s", 0)
+
         # Handle dict with "array" key (from datasets)
         if isinstance(inputs, dict) and "array" in inputs:
             inputs = {
@@ -33,15 +35,10 @@ class ASRPipeline(transformers.AutomaticSpeechRecognitionPipeline):
                 "sampling_rate": inputs.get("sampling_rate", self.feature_extractor.sampling_rate),
             }
 
-        for item in super().preprocess(inputs, **preprocess_params):
-            if "is_last" not in item:
-                item["is_last"] = True
-            yield item
+        return super().preprocess(inputs, **preprocess_params)
 
     def _forward(self, model_inputs, **generate_kwargs) -> dict[str, Any]:
-        # Extract audio features and is_last flag
-        is_last = model_inputs.pop("is_last", True) if isinstance(model_inputs, dict) else True
-
+        # Extract audio features
         if isinstance(model_inputs, dict):
             input_features = model_inputs.get("input_features")
             if input_features is not None:
@@ -54,13 +51,9 @@ class ASRPipeline(transformers.AutomaticSpeechRecognitionPipeline):
             **generate_kwargs,
         )
 
-        return {"tokens": generated_ids, "is_last": is_last}
+        return {"tokens": generated_ids}
 
     def postprocess(self, model_outputs, **kwargs) -> dict[str, str]:
-        # Handle list of outputs (from chunking)
-        if isinstance(model_outputs, list):
-            model_outputs = model_outputs[0] if model_outputs else {}
-
         tokens = model_outputs.get("tokens")
         if tokens is None:
             return super().postprocess(model_outputs, **kwargs)