diff --git a/app.py b/app.py
index 4893492..54a955c 100644
--- a/app.py
+++ b/app.py
@@ -2,7 +2,7 @@ import logging
 import os
 import subprocess
 import tempfile
-from typing import Optional
+from typing import Optional, Union, List, Tuple
 from enum import Enum
 
 import whisper
@@ -102,9 +102,23 @@ async def transcribe_audio(
     token: str = Depends(api_key_header),
     model_name: Optional[str] = Query(None, description="Model name to use for transcription"),
     output_format: OutputFormat = Query(OutputFormat.json, description="Output format: plaintext, simple, or json"),
-    speedup: float = Query(1.0, ge=0.25, le=4.0, description="Speed up factor for audio (0.25-4.0)")
+    speedup: float = Query(1.0, ge=0.25, le=4.0, description="Speed up factor for audio (0.25-4.0)"),
+    # Whisper model parameters
+    verbose: Optional[bool] = Query(None, description="Whether to print out the progress and debug messages"),
+    temperature: Union[float, str] = Query("0.0,0.2,0.4,0.6,0.8,1.0", description="Temperature for sampling (single float or comma-separated values)"),
+    compression_ratio_threshold: Optional[float] = Query(2.4, description="If the gzip compression ratio is above this value, treat as failed"),
+    logprob_threshold: Optional[float] = Query(-1.0, description="If the average log probability over sampled tokens is below this value, treat as failed"),
+    no_speech_threshold: Optional[float] = Query(0.6, description="If the no_speech probability is higher than this value AND the average log probability over sampled tokens is below logprob_threshold, consider the segment as silent"),
+    condition_on_previous_text: bool = Query(True, description="If True, the previous output of the model is provided as a prompt for the next window"),
+    initial_prompt: Optional[str] = Query(None, description="Optional text to provide as a prompt for the first window"),
+    carry_initial_prompt: bool = Query(False, description="If True, the initial prompt is carried over to the next window"),
+    word_timestamps: bool = Query(False, description="Extract word-level timestamps using the cross-attention pattern and dynamic time warping"),
+    prepend_punctuations: str = Query("\"'([{-", description="If word_timestamps is True, merge these punctuation marks with the next word"),
+    append_punctuations: str = Query("\"'.,:;!?)]}", description="If word_timestamps is True, merge these punctuation marks with the previous word"),
+    clip_timestamps: Union[str, List[float]] = Query("0", description="Comma-separated list of clip timestamps to use for transcription"),
+    hallucination_silence_threshold: Optional[float] = Query(None, description="When word_timestamps is True, skip silent periods longer than this threshold (in seconds)"),
 ):
-    """Transcribe audio file with configurable output format"""
+    """Transcribe audio file with configurable output format and comprehensive Whisper parameters"""
 
     # Token validation
     if token not in get_keys():
@@ -142,9 +156,59 @@ async def transcribe_audio(
         else:
             audio_file_path = temp_input_path
 
+        # Prepare transcription parameters
+        transcribe_params = {}
+
+        # Handle temperature parameter (can be single value or tuple)
+        if isinstance(temperature, str) and "," in temperature:
+            try:
+                temp_values = [float(x.strip()) for x in temperature.split(",")]
+                transcribe_params["temperature"] = tuple(temp_values)
+            except ValueError:
+                transcribe_params["temperature"] = 0.0
+        else:
+            try:
+                transcribe_params["temperature"] = float(temperature)
+            except (ValueError, TypeError):
+                transcribe_params["temperature"] = 0.0
+
+        # Handle clip_timestamps parameter
+        if isinstance(clip_timestamps, str) and clip_timestamps != "0":
+            try:
+                if "," in clip_timestamps:
+                    transcribe_params["clip_timestamps"] = [float(x.strip()) for x in clip_timestamps.split(",")]
+                else:
+                    transcribe_params["clip_timestamps"] = clip_timestamps
+            except ValueError:
+                transcribe_params["clip_timestamps"] = "0"
+        else:
+            transcribe_params["clip_timestamps"] = clip_timestamps
+
+        # Add other parameters if they are not None
+        if verbose is not None:
+            transcribe_params["verbose"] = verbose
+        if compression_ratio_threshold is not None:
+            transcribe_params["compression_ratio_threshold"] = compression_ratio_threshold
+        if logprob_threshold is not None:
+            transcribe_params["logprob_threshold"] = logprob_threshold
+        if no_speech_threshold is not None:
+            transcribe_params["no_speech_threshold"] = no_speech_threshold
+
+        transcribe_params["condition_on_previous_text"] = condition_on_previous_text
+        transcribe_params["carry_initial_prompt"] = carry_initial_prompt
+        transcribe_params["word_timestamps"] = word_timestamps
+        transcribe_params["prepend_punctuations"] = prepend_punctuations
+        transcribe_params["append_punctuations"] = append_punctuations
+
+        if initial_prompt is not None:
+            transcribe_params["initial_prompt"] = initial_prompt
+        if hallucination_silence_threshold is not None:
+            transcribe_params["hallucination_silence_threshold"] = hallucination_silence_threshold
+
         # Transcribe
         logger.info("Starting transcription")
-        result = model.transcribe(audio_file_path)
+        logger.debug(f"Transcription parameters: {transcribe_params}")
+        result = model.transcribe(audio_file_path, **transcribe_params)
 
         # Format output based on requested format
         if output_format == OutputFormat.plaintext:
@@ -170,7 +234,7 @@ async def transcribe_audio(
 @app.get("/health")
 async def health_check():
     """Health check endpoint"""
-    return {"status": "healthy", "model_loaded": default_model is not None}
+    return {"status": "healthy", "model_loaded": default_model is not None, "model_name": default_model.__str__()}
 
 def main():
     import uvicorn