Refactoring

whispering/cli.py

@@ -206,7 +206,7 @@ def get_opts() -> argparse.Namespace:
 def get_wshiper(*, opts) -> WhisperStreamingTranscriber:
     config = WhisperConfig(
         model_name=opts.model,
-        language=opts.language,
+        language=opts.language if opts.language == MULTI_LANGUAGE else None,
         device=opts.device,
     )
 

whispering/schema.py

@@ -13,7 +13,7 @@ MULTI_LANGUAGE: Final[str] = "multi"
 class WhisperConfig(BaseModel):
     model_name: str
     device: str
-    language: str
+    language: Optional[str]
     fp16: bool = True
 
     @root_validator

whispering/transcriber.py

@@ -18,7 +18,7 @@ from whisper.decoding import DecodingOptions, DecodingResult
 from whisper.tokenizer import get_tokenizer
 from whisper.utils import exact_div
 
-from whispering.schema import MULTI_LANGUAGE, Context, ParsedChunk, WhisperConfig
+from whispering.schema import Context, ParsedChunk, WhisperConfig
 from whispering.vad import VAD
 
 logger = getLogger(__name__)
@@ -43,7 +43,7 @@ class WhisperStreamingTranscriber:
         self.model: Final[Whisper] = load_model(config.model_name, device=config.device)
         self.tokenizer = get_tokenizer(
             self.model.is_multilingual,
-            language=config.language if config.language != MULTI_LANGUAGE else None,
+            language=config.language,
             task="transcribe",
         )
         self._set_dtype(config.fp16)
@@ -65,41 +65,23 @@ class WhisperStreamingTranscriber:
         patience: Optional[float],
         best_of: Optional[int],
     ) -> DecodingOptions:
-        if self.config.language != MULTI_LANGUAGE:
-            return DecodingOptions(
-                task="transcribe",
-                language=self.config.language,
-                temperature=t,
-                sample_len=None,
-                best_of=best_of,
-                beam_size=beam_size,
-                patience=patience,
-                length_penalty=None,
-                prompt=prompt,
-                prefix=None,
-                suppress_blank=True,
-                suppress_tokens="-1",
-                without_timestamps=False,
-                max_initial_timestamp=1.0,
-                fp16=self.fp16,
-            )
-        else:
-            return DecodingOptions(
-                task="transcribe",
-                temperature=t,
-                sample_len=None,
-                best_of=best_of,
-                beam_size=beam_size,
-                patience=patience,
-                length_penalty=None,
-                prompt=prompt,
-                prefix=None,
-                suppress_blank=True,
-                suppress_tokens="-1",
-                without_timestamps=False,
-                max_initial_timestamp=1.0,
-                fp16=self.fp16,
-            )
+        return DecodingOptions(
+            task="transcribe",
+            language=self.config.language,
+            temperature=t,
+            sample_len=None,
+            best_of=best_of,
+            beam_size=beam_size,
+            patience=patience,
+            length_penalty=None,
+            prompt=prompt,
+            prefix=None,
+            suppress_blank=True,
+            suppress_tokens="-1",
+            without_timestamps=False,
+            max_initial_timestamp=1.0,
+            fp16=self.fp16,
+        )
 
     def _decode_with_fallback(
         self,