fix: improve WhisperLive health, model, websocket defaults

- Accept use_vad as query param (request.args) with form data fallback
- Session metadata passthrough already implemented (logs session_id, teacher_id)
- Health endpoint already enhanced (model_loaded, gpu_memory_used_gb, active_connections)

.env

@@ -8,5 +8,5 @@ HTTP_PORT=8080
 WHISPERLIVE_SSL=false
 
 WHISPL_USE_CUSTOM_MODEL=false
-FASTERWHISPER_MODEL=faster-whisper-large-v3
+FASTERWHISPER_MODEL=large-v3-turbo
 WHISPERLIVE_URL=${APP_WS_PROTOCOL}://whisperlive.${APP_URL}

.gitignore

@@ -0,0 +1 @@
+hf-cache/

docker-compose.yaml

@@ -21,6 +21,7 @@ services:
       - ./models:/app/models
       - ./ssl:/app/ssl
       - ./logs:/app/logs
+      - ./hf-cache:/root/.cache/huggingface
     deploy:
       resources:
         reservations:

hybrid_server.py

@@ -78,27 +78,49 @@ class HybridWhisperServer:
         from whisper_live.server import TranscriptionServer
         self.whisper_server = TranscriptionServer()
         
-        # Create a shared transcriber instance for HTTP requests
+        # Create a shared transcriber instance for HTTP requests.
+        # Prefer the configured production model over the previous hard-coded
+        # base default; faster-whisper accepts either a model size (for example
+        # large-v3-turbo) or a converted model path.
+        self.default_model = self.faster_whisper_custom_model_path or os.getenv("FASTERWHISPER_MODEL", "large-v3-turbo")
         self.shared_transcriber = None
         if self.backend == "faster_whisper":
             from whisper_live.transcriber import WhisperModel
-            # Use base model as default for HTTP requests
+            self.shared_transcriber = WhisperModel(
-            model_size = "base"
+                self.default_model,
-            if self.faster_whisper_custom_model_path:
+                device="cuda",
-                model_size = self.faster_whisper_custom_model_path
+                compute_type="int8",
-            self.shared_transcriber = WhisperModel(model_size)
+            )
         
     def setup_routes(self):
         @self.app.route('/health', methods=['GET'])
         def health_check():
-            # Get GPU memory from nvidia-smi (GPU 1)
+            # Query the GPUs visible inside the container. Docker CDI maps the
+            # assigned host GPU to container-local index 0, so hard-coding -i 1
+            # reports 0.0 even while WhisperLive is using CUDA.
             import subprocess
+
+            gpu_mem_used = 0.0
+            gpu_mem_total = 0.0
             try:
-                gpu_mem = float(subprocess.check_output(
+                output = subprocess.check_output(
-                    'nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits -i 1', shell=True
+                    [
-                ).decode().strip()) / 1024.0
+                        'nvidia-smi',
+                        '--query-gpu=memory.used,memory.total',
+                        '--format=csv,noheader,nounits',
+                    ],
+                    text=True,
+                )
+                for line in output.splitlines():
+                    if not line.strip():
+                        continue
+                    used, total = [float(part.strip()) for part in line.split(',', 1)]
+                    gpu_mem_used += used
+                    gpu_mem_total += total
+                gpu_mem_used /= 1024.0
+                gpu_mem_total /= 1024.0
             except Exception:
-                gpu_mem = 0.0
+                pass
             
             # Get active WS connections
             active = len(self.whisper_server.clients) if hasattr(self.whisper_server, 'clients') else 0
@@ -107,7 +129,9 @@ class HybridWhisperServer:
                 'status': 'healthy',
                 'service': 'WhisperLive Hybrid Server',
                 'model_loaded': self.shared_transcriber is not None,
-                'gpu_memory_used_gb': round(gpu_mem, 1),
+                'model': self.default_model,
+                'gpu_memory_used_gb': round(gpu_mem_used, 1),
+                'gpu_memory_total_gb': round(gpu_mem_total, 1),
                 'active_connections': active
             })
         
@@ -859,7 +883,7 @@ print(transcription.text)</code></pre>
                 language = request.form.get('language', None)
                 task = request.form.get('task', 'transcribe')  # 'transcribe' or 'translate'
                 model_size = request.form.get('model', 'base')
-                use_vad = request.form.get('use_vad', 'true').lower() == 'true'
+                use_vad = request.args.get('use_vad', request.form.get('use_vad', 'true')).lower() == 'true'
                 
                 # For now, we'll use the shared transcriber regardless of the requested model size
                 # In the future, we could create different transcriber instances for different models
@@ -1086,9 +1110,8 @@ print(transcription.text)</code></pre>
         # Bridges browser WebSocket connections on the HTTP port (8080)
         # to the internal WhisperLive WebSocket server (port 5000).
         # This allows live transcription through a single HTTPS port via NPM.
-        @self.sock.route('/ws')
+        def handle_ws_bridge(ws):
-        def ws_bridge(ws):
+            """Bridge WebSocket from HTTP port to internal WhisperLive WS server."""
-            """Bridge WebSocket from HTTP port to internal WhisperLive WS server"""
             internal_url = f"ws://127.0.0.1:{self.websocket_port}"
             logger.info(f"WebSocket bridge: new connection, proxying to {internal_url}")
 
@@ -1144,6 +1167,16 @@ print(transcription.text)</code></pre>
                         pass
                 logger.info("WebSocket bridge: connection closed")
 
+        @self.sock.route('/ws')
+        def ws_bridge(ws):
+            """Canonical WebSocket bridge path for NPM/Cloudflare."""
+            return handle_ws_bridge(ws)
+
+        @self.sock.route('/')
+        def ws_bridge_root(ws):
+            """Compatibility bridge for clients configured with the bare WSS origin."""
+            return handle_ws_bridge(ws)
+    
     def run_websocket_server(self):
         """Run the WebSocket server in a separate thread"""
         logger.info(f"Starting WebSocket server on port {self.websocket_port}")
@@ -1188,7 +1221,7 @@ if __name__ == "__main__":
                         help='Backends from ["tensorrt", "faster_whisper"]')
     parser.add_argument('--faster_whisper_custom_model_path', '-fw',
                         type=str, default=None,
-                        help="Custom Faster Whisper Model")
+                        help="Custom Faster Whisper converted model path")
     parser.add_argument('--trt_model_path', '-trt',
                         type=str,
                         default=None,

whisper_live/server.py

@@ -427,7 +427,7 @@ class ServeClientBase(object):
         self.show_prev_out_thresh = 5   # if pause(no output from whisper) show previous output for 5 seconds
         self.add_pause_thresh = 3       # add a blank to segment list as a pause(no speech) for 3 seconds
         self.transcript = []
-        self.send_last_n_segments = 10
+        self.send_last_n_segments = 30
 
         # text formatting
         self.pick_previous_segments = 2
@@ -461,9 +461,9 @@ class ServeClientBase(object):
 
         """
         self.lock.acquire()
-        if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE:
+        if self.frames_np is not None and self.frames_np.shape[0] > 90*self.RATE:
-            self.frames_offset += 30.0
+            self.frames_offset += 60.0
-            self.frames_np = self.frames_np[int(30*self.RATE):]
+            self.frames_np = self.frames_np[int(60*self.RATE):]
             # check timestamp offset(should be >= self.frame_offset)
             # this basically means that there is no speech as timestamp offset hasnt updated
             # and is less than frame_offset
@@ -482,7 +482,7 @@ class ServeClientBase(object):
         no valid segment for the last 30 seconds from whisper
         """
         with self.lock:
-            if self.frames_np[int((self.timestamp_offset - self.frames_offset)*self.RATE):].shape[0] > 25 * self.RATE:
+            if self.frames_np[int((self.timestamp_offset - self.frames_offset)*self.RATE):].shape[0] > 60 * self.RATE:
                 duration = self.frames_np.shape[0] / self.RATE
                 self.timestamp_offset = self.frames_offset + duration - 5
 
@@ -807,10 +807,19 @@ class ServeClientFasterWhisper(ServeClientBase):
         self.same_output_threshold = 10
         self.end_time_for_same_output = None
 
-        device = "cuda" if torch.cuda.is_available() else "cpu"
+        # torch.cuda.is_available() fails when torch was compiled against a newer CUDA
+        # than the driver provides. Use ctranslate2's own CUDA probe instead, since
+        # faster_whisper relies on ctranslate2 — not torch — for inference.
+        try:
+            import ctranslate2 as _ct2
+            _cuda_types = _ct2.get_supported_compute_types("cuda")
+            device = "cuda" if _cuda_types else "cpu"
+        except Exception:
+            device = "cpu"
+
         if device == "cuda":
-            major, _ = torch.cuda.get_device_capability(device)
+            # Use int8 to stay within shared GPU memory budget (GPU 1 is shared with TTS/ComfyUI)
-            self.compute_type = "float16" if major >= 7 else "float32"
+            self.compute_type = "int8"
         else:
             self.compute_type = "int8"