Auto convert hf custom whisper to ct2(faster-whisper)

Signed-off-by: makaveli10 <vineet.suryan@collabora.com>

requirements/server.txt

@@ -11,6 +11,7 @@ evaluate
 numpy<2
 openai-whisper==20240930
 tokenizers==0.20.3
+transformers[torch]
 
 # openvino
 librosa

whisper_live/backend/faster_whisper_backend.py

@@ -1,8 +1,11 @@
+import os
 import json
 import logging
 import threading
 import time
 import torch
+import ctranslate2
+from huggingface_hub import snapshot_download
 
 from whisper_live.transcriber.transcriber_faster_whisper import WhisperModel
 from whisper_live.backend.base import ServeClientBase
@@ -118,38 +121,51 @@ class ServeClientFasterWhisper(ServeClientBase):
 
     def create_model(self, device):
         """
-        Instantiates a new model, sets it as the transcriber.
+        Instantiates a new model, sets it as the transcriber. If model is a huggingface model_id
+        then it is automatically converted to ctranslate2(faster_whisper) format.
         """
+        model_ref = self.model_size_or_path
+
+        if model_ref in self.model_sizes:
+            model_to_load = model_ref
+        else:
+            logging.info(f"Model not in model_sizes")
+            if os.path.isdir(model_ref) and ctranslate2.contains_model(model_ref):
+                model_to_load = model_ref
+            else:
+                local_snapshot = snapshot_download(
+                    repo_id = model_ref,
+                    repo_type = "model",
+                )
+                if ctranslate2.contains_model(local_snapshot):
+                    model_to_load = local_snapshot
+                else:
+                    cache_root = os.path.expanduser("~/.cache/whisper-live/whisper-ct2-models/")
+                    os.makedirs(cache_root, exist_ok=True)
+                    safe_name = model_ref.replace("/", "--")
+                    ct2_dir = os.path.join(cache_root, safe_name)
+
+                    if not ctranslate2.contains_model(ct2_dir):
+                        logging.info(f"Converting '{model_ref}' to CTranslate2 @ {ct2_dir}")
+                        ct2_converter = ctranslate2.converters.TransformersConverter(
+                            local_snapshot, 
+                            copy_files=["tokenizer.json", "preprocessor_config.json"]
+                        )
+                        ct2_converter.convert(
+                            output_dir=ct2_dir,
+                            quantization=self.compute_type,
+                            force=False,  # skip if already up-to-date
+                        )
+                    model_to_load = ct2_dir
+
+        logging.info(f"Loading model: {model_to_load}")
         self.transcriber = WhisperModel(
-            self.model_size_or_path,
+            model_to_load,
             device=device,
             compute_type=self.compute_type,
             local_files_only=False,
         )
 
-    def check_valid_model(self, model_size):
-        """
-        Check if it's a valid whisper model size.
-
-        Args:
-            model_size (str): The name of the model size to check.
-
-        Returns:
-            str: The model size if valid, None otherwise.
-        """
-        if model_size not in self.model_sizes:
-            self.websocket.send(
-                json.dumps(
-                    {
-                        "uid": self.client_uid,
-                        "status": "ERROR",
-                        "message": f"Invalid model size {model_size}. Available choices: {self.model_sizes}"
-                    }
-                )
-            )
-            return None
-        return model_size
-
     def set_language(self, info):
         """
         Updates the language attribute based on the detected language information.