Update app.py

app.py

@@ -1,22 +1,30 @@
 from fastapi import FastAPI, HTTPException, UploadFile, File
 from pydantic import BaseModel
 from multiprocessing import Process, Queue
-import whisper
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import torch
 import io
 import uvicorn
+import soundfile as sf
 
 app = FastAPI()
 
-model = whisper.load_model("large")
+# Cargar el modelo y el procesador
+model_name = "facebook/wav2vec2-large-960h-lv60"
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
 
 class TranscriptionRequest(BaseModel):
     file: UploadFile
 
 def transcribe_audio(file, queue):
     try:
-        audio = io.BytesIO(file.file.read())
-        result = model.transcribe(audio)
-        queue.put(result["text"])
+        audio, _ = sf.read(io.BytesIO(file.file.read()))
+        input_values = processor(audio, return_tensors="pt", padding="longest").input_values
+        logits = model(input_values).logits
+        predicted_ids = torch.argmax(logits, dim=-1)
+        transcription = processor.batch_decode(predicted_ids)[0]
+        queue.put(transcription)
     except Exception as e:
         queue.put(f"Error: {str(e)}")