LoRa path v0 - functional

README.md

@@ -158,6 +158,7 @@ async def websocket_endpoint(websocket: WebSocket):
 | `--ssl-keyfile` | Path to the SSL private key file (for HTTPS support) | `None` |
 | `--forwarded-allow-ips` | Ip or Ips allowed to reverse proxy the whisperlivekit-server. Supported types are  IP Addresses (e.g. 127.0.0.1), IP Networks (e.g. 10.100.0.0/16), or Literals (e.g. /path/to/socket.sock) | `None` |
 | `--pcm-input` | raw PCM (s16le) data is expected as input and FFmpeg will be bypassed. Frontend will use AudioWorklet instead of MediaRecorder | `False` |
+| `--lora-path` | Path or Hugging Face repo ID for LoRA adapter weights (e.g., `qfuxa/whisper-base-french-lora`). Only works with native Whisper backend (`--backend whisper`) | `None` |
 
 | Translation options | Description | Default |
 |-----------|-------------|---------|

whisperlivekit/core.py

@@ -59,6 +59,7 @@ class TranscriptionEngine:
             "model_cache_dir": None,
             "model_dir": None,
             "model_path": None,
+            "lora_path": None,
             "lan": "auto",
             "direct_english_translation": False,
         }

whisperlivekit/local_agreement/backends.py

@@ -16,9 +16,10 @@ class ASRBase:
     sep = " "  # join transcribe words with this character (" " for whisper_timestamped,
               # "" for faster-whisper because it emits the spaces when needed)
 
-    def __init__(self, lan, model_size=None, cache_dir=None, model_dir=None, logfile=sys.stderr):
+    def __init__(self, lan, model_size=None, cache_dir=None, model_dir=None, lora_path=None, logfile=sys.stderr):
         self.logfile = logfile
         self.transcribe_kargs = {}
+        self.lora_path = lora_path
         if lan == "auto":
             self.original_language = None
         else:
@@ -58,12 +59,12 @@ class WhisperASR(ASRBase):
                         f"No supported PyTorch checkpoint found under {resolved_path}"
                     )            
             logger.debug(f"Loading Whisper model from custom path {resolved_path}")
-            return load_whisper_model(str(resolved_path))
+            return load_whisper_model(str(resolved_path), lora_path=self.lora_path)
 
         if model_size is None:
             raise ValueError("Either model_size or model_dir must be set for WhisperASR")
 
-        return load_whisper_model(model_size, download_root=cache_dir)
+        return load_whisper_model(model_size, download_root=cache_dir, lora_path=self.lora_path)
 
     def transcribe(self, audio, init_prompt=""):
         options = dict(self.transcribe_kargs)

whisperlivekit/local_agreement/whisper_online.py

@@ -77,6 +77,7 @@ def backend_factory(
             model_cache_dir,
             model_dir,
             model_path,
+            lora_path,
             direct_english_translation,
             buffer_trimming,
             buffer_trimming_sec,
@@ -138,6 +139,7 @@ def backend_factory(
             lan=lan,
             cache_dir=model_cache_dir,
             model_dir=model_override,
+            lora_path=lora_path if backend_choice == "whisper" else None,
         )
         e = time.time()
         logger.info(f"done. It took {round(e-t,2)} seconds.")

whisperlivekit/parse_args.py

@@ -106,6 +106,13 @@ def parse_args():
         default=None,
         help="Dir where Whisper model.bin and other files are saved. This option overrides --model and --model_cache_dir parameter.",
     )
+    parser.add_argument(
+        "--lora-path",
+        type=str,
+        default=None,
+        dest="lora_path",
+        help="Path or Hugging Face repo ID for LoRA adapter weights (e.g., QuentinFuxa/whisper-base-french-lora). Only works with native Whisper backend.",
+    )
     parser.add_argument(
         "--lan",
         "--language",

whisperlivekit/simul_whisper/backend.py

@@ -282,11 +282,13 @@ class SimulStreamingASR():
 
     def load_model(self):
         model_ref = str(self._resolved_model_path) if self._resolved_model_path else self.model_name
+        lora_path = getattr(self, 'lora_path', None)
         whisper_model = load_model(
             name=model_ref,
             download_root=None,
             decoder_only=self.fast_encoder,
-            custom_alignment_heads=self.custom_alignment_heads
+            custom_alignment_heads=self.custom_alignment_heads,
+            lora_path=lora_path,
         )
         warmup_audio = load_file(self.warmup_file)
         if warmup_audio is not None:

whisperlivekit/whisper/__init__.py

@@ -264,9 +264,49 @@ def _collapse_hf_module_name(module: str):
     return module
 
 
+def _resolve_lora_path(lora_path: Optional[str]) -> Optional[str]:
+    """
+    Resolve LoRA adapter path - handles both local paths and HuggingFace repo IDs.
+    
+    If lora_path is a local directory containing adapter files, returns it as-is.
+    If lora_path looks like a HuggingFace repo ID (contains '/'), downloads and caches it.
+    """
+    if not lora_path:
+        return None
+    
+    # Check if it's already a valid local path
+    if os.path.isdir(lora_path):
+        config_path = os.path.join(lora_path, "adapter_config.json")
+        if os.path.isfile(config_path):
+            return lora_path
+    
+    # Try to download from HuggingFace Hub
+    if "/" in lora_path:
+        try:
+            from huggingface_hub import snapshot_download
+            local_path = snapshot_download(
+                repo_id=lora_path,
+                allow_patterns=["adapter_config.json", "adapter_model.*"],
+            )
+            return local_path
+        except Exception as e:
+            raise FileNotFoundError(
+                f"Could not find LoRA adapter at local path or HuggingFace Hub: {lora_path}. Error: {e}"
+            )
+    
+    raise FileNotFoundError(
+        f"LoRA path '{lora_path}' is not a valid local directory or HuggingFace repo ID."
+    )
+
+
 def _apply_lora_adapter(state_dict: Dict[str, Tensor], lora_path: Optional[str]):
     if not lora_path:
         return
+    
+    # Resolve path (handles HuggingFace Hub download)
+    lora_path = _resolve_lora_path(lora_path)
+    if not lora_path:
+        return
 
     config_path = os.path.join(lora_path, "adapter_config.json")
     if not os.path.isfile(config_path):