refactor: move file handling logic to base strategy

app/worker/strategies/base.py

@@ -1,7 +1,11 @@
+import os
+import tempfile
 from abc import ABC
 from typing import Any, Protocol, Tuple
 from uuid import UUID
 
+import requests
+
 import app.shared.db.models as models
 
 TaskReturnValue = Tuple[models.ArtifactType, Any]
@@ -23,4 +27,25 @@ class BaseStrategy(ABC):
         raise NotImplementedError()
 
     def cleanup(self, job_id: UUID) -> None:
-        raise NotImplementedError()
+        try:
+            os.remove(self._get_tmp_file(job_id))
+        except OSError:
+            pass
+
+    def _get_tmp_file(self, job_id: UUID) -> str:
+        tmp = tempfile.gettempdir()
+        return os.path.join(tmp, str(job_id))
+
+    def _download(self, url: str, job_id: UUID) -> str:
+        # re-create folder.
+        filename = self._get_tmp_file(job_id)
+        self.cleanup(job_id)
+
+        # stream media to disk.
+        with requests.get(url, stream=True) as r:
+            r.raise_for_status()
+            with open(filename, "wb") as f:
+                for chunk in r.iter_content(chunk_size=8192):
+                    f.write(chunk)
+
+        return filename

app/worker/strategies/local.py

@@ -1,15 +1,11 @@
 import os
-import tempfile
 from asyncio.log import logger
-from os import path
 from typing import Any, Literal
 from uuid import UUID
 
-import requests
 import torch
 import whisper
 from pydantic import BaseModel
-from sqlalchemy import JSON, Column
 
 import app.shared.db.models as models
 from app.worker.strategies.base import BaseStrategy, TaskReturnValue
@@ -28,23 +24,17 @@ class DecodingOptions(BaseModel):
 class LocalStrategy(BaseStrategy):
     def __init__(self) -> None:
         if torch.cuda.is_available():
-            logger.info("initializing GPU model.")
+            logger.debug("initializing GPU model.")
             self.model = whisper.load_model(
                 os.environ["WHISPER_MODEL"], download_root="/models"
             ).cuda()
         else:
-            logger.info("initializing CPU model.")
+            logger.debug("initializing CPU model.")
             self.model = whisper.load_model(
                 os.environ["WHISPER_MODEL"], download_root="/models"
             )
 
-        logger.info("initialized local strategy.")
-
-    def cleanup(self, job_id) -> None:
-        try:
-            os.remove(self._get_tmp_file(job_id))
-        except OSError:
-            pass
+        logger.debug("initialized local strategy.")
 
     def transcribe(self, job):
         result = self._run_whisper(
@@ -65,8 +55,8 @@ class LocalStrategy(BaseStrategy):
     def detect_language(self, job) -> TaskReturnValue:
         file = self._download(job.url, job.id)
 
+        # see: https://github.com/openai/whisper/blob/248b6cb124225dd263bb9bd32d060b6517e067f8/README.md?plain=1#L114
         audio = whisper.pad_or_trim(whisper.load_audio(file))
-
         mel = whisper.log_mel_spectrogram(audio).to(self.model.device)
         _, probs = self.model.detect_language(mel)
 
@@ -75,42 +65,22 @@ class LocalStrategy(BaseStrategy):
             {"code": max(probs, key=probs.get)},
         )
 
-    def _download(self, url: str, job_id: UUID) -> str:
-        # re-create folder.
-        filename = self._get_tmp_file(job_id)
-        self.cleanup(job_id)
-
-        # stream media to disk.
-        with requests.get(url, stream=True) as r:
-            r.raise_for_status()
-            with open(filename, "wb") as f:
-                for chunk in r.iter_content(chunk_size=8192):
-                    f.write(chunk)
-
-        return filename
-
     def _run_whisper(
         self,
         filepath: str,
         task: Literal["translate", "transcribe"],
-        config: Column[JSON],
+        config: dict[str, Any],
         job_id: UUID,
     ) -> list[Any]:
-        try:
-            result = self.model.transcribe(
-                filepath,
-                # turning this off might make the transcription less accurate,
-                # but significantly reduces amount of model halucinations.
-                condition_on_previous_text=False,
-                **DecodingOptions(
-                    task=task, language=config.language if config else None
-                ).dict(),
-            )
+        result = self.model.transcribe(
+            filepath,
+            # turning this off might make the transcription less accurate,
+            # but significantly reduces amount of model halucinations.
+            condition_on_previous_text=False,
+            **DecodingOptions(
+                task=task,
+                language=models.JobConfig(**config).language if config else None,
+            ).dict(),
+        )
 
-            return result["segments"]
-        finally:
-            self.cleanup(job_id)
-
-    def _get_tmp_file(self, job_id: UUID) -> str:
-        tmp = tempfile.gettempdir()
-        return path.join(tmp, str(job_id))
+        return result["segments"]