testing time

app.py

@@ -1,12 +1,36 @@
+
 from fastapi import FastAPI, Request, HTTPException
+import torch
+import torchaudio
+from transformers import AutoProcessor, pipeline
 import io
-import time
-from faster_whisper import WhisperModel
+from pydub import AudioSegment
+from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
+import numpy as np
 import uvicorn
-
+import time
 app = FastAPI()
 
-model = WhisperModel("WajeehAzeemX/faster-whisper-smallar2-int8", device="cpu", compute_type="int8")
+# Device configuration
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(device)
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Load the model and processor
+model_id = "WajeehAzeemX/whisper-small-ar2_onnx"
+model = ORTModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+processor = AutoProcessor.from_pretrained(model_id)
+
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+)
 
 @app.post("/transcribe/")
 async def transcribe_audio(request: Request):
@@ -16,18 +40,43 @@ async def transcribe_audio(request: Request):
 
         # Convert binary data to a file-like object
         audio_file = io.BytesIO(audio_data)
+        
+        # Load the audio file using pydub
+        try:
+            audio_segment = AudioSegment.from_file(audio_file, format="wav")
+        except Exception as e:
+            raise HTTPException(status_code=400, detail=f"Error loading audio file: {str(e)}")
 
-        # Start timing the transcription
-        start_time = time.time()
+        # Convert to mono if the audio is stereo (multi-channel)
+        if audio_segment.channels > 1:
+            audio_segment = audio_segment.set_channels(1)
+
+        # Resample the audio to 16kHz
+        target_sample_rate = 16000
+        if audio_segment.frame_rate != target_sample_rate:
+            audio_segment = audio_segment.set_frame_rate(target_sample_rate)
 
-        # Transcribe the audio
-        segments, info = model.transcribe(audio_file)
-        transcription = " ".join([segment.text for segment in segments])
+        # Convert audio to numpy array
+        audio_array = np.array(audio_segment.get_array_of_samples())
+        if audio_segment.sample_width == 2:
+            audio_array = audio_array.astype(np.float32) / 32768.0
+        else:
+            raise HTTPException(status_code=400, detail="Unsupported sample width")
+        start_time = time.time()
+        # Convert to the format expected by the model
+        inputs = processor(audio_array, sampling_rate=target_sample_rate, return_tensors="pt")
+        inputs = inputs.to(device)
 
+        # Get the transcription result
+        result = pipe(audio_array)
         # Calculate time taken
         time_taken = time.time() - start_time
+        transcription = result["text"]
 
         return {"transcription": transcription, "time_taken": time_taken}
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
+    
+
+
 

app_backup.py

@@ -72,4 +72,53 @@ async def transcribe_audio(request: Request):
 
         return {"transcription": transcription}
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+        raise HTTPException(status_code=500, detail=str(e))
+    
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+#     from fastapi import FastAPI, Request, HTTPException
+# import io
+# import time
+# from faster_whisper import WhisperModel
+# import uvicorn
+
+# app = FastAPI()
+
+# model = WhisperModel("WajeehAzeemX/faster-whisper-smallar2-int8", device="cpu", compute_type="int8")
+
+# @app.post("/transcribe/")
+# async def transcribe_audio(request: Request):
+#     try:
+#         # Read binary data from the request
+#         audio_data = await request.body()
+
+#         # Convert binary data to a file-like object
+#         audio_file = io.BytesIO(audio_data)
+
+#         # Start timing the transcription
+#         start_time = time.time()
+
+#         # Transcribe the audio
+#         segments, info = model.transcribe(audio_file)
+#         transcription = " ".join([segment.text for segment in segments])
+
+#         # Calculate time taken
+#         time_taken = time.time() - start_time
+
+#         return {"transcription": transcription, "time_taken": time_taken}
+#     except Exception as e:
+#         raise HTTPException(status_code=500, detail=str(e))
+