new structure for transcription engine

- found problems with mps
- integrated jinja2 templates for rendering (.tex?)
- raw ui & bad structure

transcription/engines/base_engine.py

@@ -0,0 +1,30 @@
+import torch
+from abc import ABC, abstractmethod
+
+class BaseEngine(ABC):
+    def __init__(
+        self,
+        modelName: str,
+        language: str,
+        dType: torch.dtype,
+        device: str
+    ):
+        self.modelName = modelName
+        self.device = device
+        self.language = language
+        self.dType = dType
+    
+    @abstractmethod
+    def loadModel(self) -> None:
+        pass
+
+    @abstractmethod
+    def unloadModel(self) -> None:
+        pass
+
+    @abstractmethod
+    def transcribeBatch(
+        self, 
+        batch
+    ) -> str:
+        pass

transcription/engines/whisper.py

@@ -0,0 +1,65 @@
+# from logging import Logger
+import time
+import torch
+import gc
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+
+from transcription.engines.base_engine import BaseEngine
+
+class WhisperEngine(BaseEngine):
+    def loadModel(self) -> None:
+        self.processor = WhisperProcessor.from_pretrained(self.modelName)
+        self.model = WhisperForConditionalGeneration.from_pretrained(
+            self.modelName,
+            torch_dtype = self.dType # check twice
+        ).to(self.device) # ??? recheck
+    
+    def unloadModel(self) -> None:
+        self.model = None
+        self.processor = None
+
+        # TODO: MPS?
+        if self.device == "cuda": 
+            torch.cuda.empty_cache()
+    
+    def transcribeBatch(
+        self,
+        batch,
+    ) -> str:
+        assert self.processor is not None
+        assert self.model is not None
+
+        inputs = self.processor(
+            batch, 
+            sampling_rate=16000,
+            return_tensors="pt",
+            padding=True,
+        )
+        
+        input_features = inputs.input_features.to(self.device).to(self.dType)
+
+        with torch.no_grad():
+            predicted_ids = self.model.generate(
+                input_features,
+                language=self.language,
+                task="transcribe",
+                temperature=0.0,
+            )
+        
+        batchText = self.processor.batch_decode(
+            predicted_ids, 
+            skip_special_tokens=True,
+        )
+
+        inputs = None
+        input_features = None
+        predicted_ids = None
+        gc.collect()
+
+        # maybe do here something with MPS?
+        if self.device.startswith("cuda"):
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+
+        return " ".join(batchText)
+