fix: cant proceed to launch

Dockerfile

@@ -1,23 +1,17 @@
-# Используем образ ROCm с предустановленным PyTorch
 FROM rocm/pytorch:rocm6.4.1_ubuntu22.04_py3.10_pytorch_release_2.6.0
 
-# Устанавливаем рабочую директорию в контейнере
 WORKDIR /app
 
-# Устанавливаем системные зависимости
 RUN apt-get update && apt-get install -y \
     ffmpeg \
     python3-pip \
     && rm -rf /var/lib/apt/lists/*
 
-# Устанавливаем зависимости Python
 COPY requirements.txt .
 RUN pip install --no-cache-dir --default-timeout=100 -r requirements.txt
 
-# Копируем остальные файлы приложения
 COPY . .
 
-# Открываем порт, на котором будет работать приложение
 EXPOSE 9854
 
 # Устанавливаем переменные окружения для ROCm

app.py

@@ -18,10 +18,23 @@ logger = logging.getLogger(__name__)
 
 app = FastAPI()
 
+# Load model on startup
+model = None
+
+
+@app.on_event("startup")
+def load_model():
+    global model
+    logger.info("Loading whisper model...")
+    model = whisper.load_model("medium", device="cuda")
+    logger.info("Whisper model loaded.")
+
+
 # API key header
 api_key_header = APIKeyHeader(name="x-api-key")
 
-def get_keys(): # не бейте меня за это
+
+def get_keys():  # не бейте меня за это
     keys_file = "keys.txt"
     if not os.path.exists(keys_file):
         # Create a new keys file with a default key
@@ -40,6 +53,7 @@ def get_keys(): # не бейте меня за это
             raise ValueError("No keys found in keys.txt")
         return keys
 
+
 def convert_audio(input_path: str, output_path: str, speed: float = 1.25):
     """
     Convert audio to compatible format and speed up
@@ -61,6 +75,7 @@ def convert_audio(input_path: str, output_path: str, speed: float = 1.25):
         logger.error(f"FFmpeg conversion failed: {e.stderr.decode()}")
         return False
 
+
 class TranscriptionMetrics:
     def __init__(self):
         self.start_time = time.time()
@@ -83,6 +98,7 @@ class TranscriptionMetrics:
             "text_length": self.text_length
         }
 
+
 def get_audio_duration(file_path: str) -> float:
     """Get audio duration using ffprobe"""
     cmd = [
@@ -98,23 +114,24 @@ def get_audio_duration(file_path: str) -> float:
     except:
         return 0.0
 
+
 @app.post("/transcribe")
 async def transcribe_audio(
-    file: UploadFile = File(...),
-    token: str = Depends(api_key_header),
-    model_name: str = "medium",
-    verbose: Optional[bool] = None,
-    temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
-    compression_ratio_threshold: Optional[float] = 2.4,
-    logprob_threshold: Optional[float] = -1.0,
-    no_speech_threshold: Optional[float] = 0.6,
-    condition_on_previous_text: bool = True,
-    initial_prompt: Optional[str] = None,
-    word_timestamps: bool = False,
-    prepend_punctuations: str = "\"'\"¿([{-",
-    append_punctuations: str = "\"\'.。,，!！?？:：\")]}、",
-    clip_timestamps: Union[str, List[float]] = "0",
-    hallucination_silence_threshold: Optional[float] = None
+        file: UploadFile = File(...),
+        token: str = Depends(api_key_header),
+        model_name: str = "medium",
+        verbose: Optional[bool] = None,
+        temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
+        compression_ratio_threshold: Optional[float] = 2.4,
+        logprob_threshold: Optional[float] = -1.0,
+        no_speech_threshold: Optional[float] = 0.6,
+        condition_on_previous_text: bool = True,
+        initial_prompt: Optional[str] = None,
+        word_timestamps: bool = False,
+        prepend_punctuations: str = "\"'\"¿([{-",
+        append_punctuations: str = "\"\'.。,，!！?？:：\")]}、",
+        clip_timestamps: Union[str, List[float]] = "0",
+        hallucination_silence_threshold: Optional[float] = None
 ):
     # Token validation
     if token not in get_keys():
@@ -140,10 +157,6 @@ async def transcribe_audio(
         # Get audio duration before speed up
         original_duration = get_audio_duration(temp_input_path)
 
-        # Load model
-        logger.debug(f"Loading model: {model_name}")
-        model = whisper.load_model(model_name, device="cuda")
-
         # Transcribe
         logger.info("Starting transcription")
         result = model.transcribe(
@@ -162,7 +175,6 @@ async def transcribe_audio(
             hallucination_silence_threshold=hallucination_silence_threshold
         )
 
-
         # Calculate metrics
         metrics.stop(result["text"], original_duration)
         logger.info(f"Transcription metrics: {metrics.get_metrics()}")
@@ -183,10 +195,11 @@ async def transcribe_audio(
         if os.path.exists(temp_output_path):
             os.remove(temp_output_path)
 
+
 def main():
     import uvicorn
     get_keys()
-    uvicorn.run(app, host="0.0.0.0")
+
 
 if __name__ == "__main__":
     main()

docker-compose.yml

@@ -3,8 +3,6 @@ services:
     build: .
     ports:
       - "9854:9854"
-    volumes:
-      - ./keys.txt:/app/keys.txt
     devices:
       - "/dev/kfd:/dev/kfd"
       - "/dev/dri:/dev/dri"