Support initializing more whisper model args

faster_whisper/transcribe.py

@@ -92,6 +92,8 @@ def __init__(
         num_workers: int = 1,
         download_root: Optional[str] = None,
         local_files_only: bool = False,
+        files: dict = None,
+        **model_kwargs,
     ):
         """Initializes the Whisper model.
 
@@ -118,10 +120,18 @@ def __init__(
             are saved in the standard Hugging Face cache directory.
           local_files_only:  If True, avoid downloading the file and return the path to the
             local cached file if it exists.
+          files: Load model files from the memory. This argument is a dictionary mapping file names
+            to file contents as file-like or bytes objects. If this is set, model_path acts as an
+            identifier for this model.
         """
         self.logger = get_logger()
 
-        if os.path.isdir(model_size_or_path):
+        tokenizer_bytes, preprocessor_bytes = None, None
+        if files:
+            model_path = model_size_or_path
+            tokenizer_bytes = files.pop("tokenizer.json", None)
+            preprocessor_bytes = files.pop("preprocessor_config.json", None)
+        elif os.path.isdir(model_size_or_path):
             model_path = model_size_or_path
         else:
             model_path = download_model(
@@ -137,17 +147,20 @@ def __init__(
             compute_type=compute_type,
             intra_threads=cpu_threads,
             inter_threads=num_workers,
+            files=files,
+            **model_kwargs,
         )
 
         tokenizer_file = os.path.join(model_path, "tokenizer.json")
-        if os.path.isfile(tokenizer_file):
+        if tokenizer_bytes:
+            self.hf_tokenizer = tokenizers.Tokenizer.from_buffer(tokenizer_bytes)
+        elif os.path.isfile(tokenizer_file):
             self.hf_tokenizer = tokenizers.Tokenizer.from_file(tokenizer_file)
         else:
             self.hf_tokenizer = tokenizers.Tokenizer.from_pretrained(
                 "openai/whisper-tiny" + ("" if self.model.is_multilingual else ".en")
             )
-
-        self.feat_kwargs = self._get_feature_kwargs(model_path)
+        self.feat_kwargs = self._get_feature_kwargs(model_path, preprocessor_bytes)
         self.feature_extractor = FeatureExtractor(**self.feat_kwargs)
         self.num_samples_per_token = self.feature_extractor.hop_length * 2
         self.frames_per_second = (
@@ -165,19 +178,21 @@ def supported_languages(self) -> List[str]:
         """The languages supported by the model."""
         return list(_LANGUAGE_CODES) if self.model.is_multilingual else ["en"]
 
-    def _get_feature_kwargs(self, model_path) -> dict:
-        preprocessor_config_file = os.path.join(model_path, "preprocessor_config.json")
+    def _get_feature_kwargs(self, model_path, preprocessor_bytes=None) -> dict:
         config = {}
-        if os.path.isfile(preprocessor_config_file):
-            try:
-                with open(preprocessor_config_file, "r", encoding="utf-8") as json_file:
-                    config = json.load(json_file)
-                valid_keys = signature(FeatureExtractor.__init__).parameters.keys()
-                config = {k: v for k, v in config.items() if k in valid_keys}
-            except json.JSONDecodeError as e:
-                self.logger.warning(
-                    "Could not load preprocessor_config.json: %s", str(e)
-                )
+        try:
+            config_path = os.path.join(model_path, "preprocessor_config.json")
+            if preprocessor_bytes:
+                config = json.loads(preprocessor_bytes)
+            elif os.path.isfile(config_path):
+                with open(config_path, "r", encoding="utf-8") as file:
+                    config = json.load(file)
+            else:
+                return config
+            valid_keys = signature(FeatureExtractor.__init__).parameters.keys()
+            return {k: v for k, v in config.items() if k in valid_keys}
+        except json.JSONDecodeError as e:
+            self.logger.warning("Could not load preprocessor config: %s", e)
 
         return config