Merge pull request #121 from JSchmie/feature_language_support

Added language name to code conversion support for FasterWhisper

scraibe/transcriber.py

@@ -26,7 +26,9 @@ Usage:
 
 from whisper import Whisper
 from whisper import load_model as whisper_load_model
+from whisper.tokenizer import TO_LANGUAGE_CODE
 from faster_whisper import WhisperModel as FasterWhisperModel
+from faster_whisper.tokenizer import _LANGUAGE_CODES as FASTER_WHISPER_LANGUAGE_CODES
 from typing import TypeVar, Union, Optional
 from torch import Tensor, device
 from torch.cuda import is_available as cuda_is_available
@@ -369,14 +371,44 @@ class FasterWhisperTranscriber(Transcriber):
             whisper_kwargs["task"] = task
 
         if (language := kwargs.get("language")):
+            language = FasterWhisperTranscriber.convert_to_language_code(language)
             whisper_kwargs["language"] = language
 
         return whisper_kwargs
 
+    @staticmethod
+    def convert_to_language_code(lang : str) -> str:
+        """
+        Load whisper model.
+
+        Args:
+            lang (str): language as code or language name
+
+        Returns:
+            language (str) code of language 
+        """
+        
+        # If the input is already in FASTER_WHISPER_LANGUAGE_CODES, return it directly
+        if lang in FASTER_WHISPER_LANGUAGE_CODES:
+            return lang
+
+        # Normalize the input to lowercase
+        lang = lang.lower()
+
+        # Check if the language name is in the TO_LANGUAGE_CODE mapping
+        if lang in TO_LANGUAGE_CODE:
+            return TO_LANGUAGE_CODE[lang]
+
+        # If the language is not recognized, raise a ValueError with the available options
+        available_codes = ', '.join(FASTER_WHISPER_LANGUAGE_CODES)
+        raise ValueError(f"Language '{lang}' is not a valid language code or name. "
+                        f"Available language codes are: {available_codes}.")
+
     def __repr__(self) -> str:
         return f"FasterWhisperTranscriber(model_name={self.model_name}, model={self.model})"
 
 
+
 def load_transcriber(model: str = "medium",
                      whisper_type: str = 'whisper',
                      download_root: str = WHISPER_DEFAULT_PATH,