Merge branch 'main' of https://huggingface.co/spaces/EntrepreneurFirst/team3

Dockerfile

@@ -4,7 +4,7 @@ FROM nvidia/cuda:12.4.1-runtime-ubuntu22.04
 USER root
 
 # Install python and pip
-RUN apt-get update && apt-get install -y python3 python3-pip \\
+RUN apt-get update && apt-get install -y python3 python3-pip \
     && apt-get install -y git
 
 # The commands as another prerequisite

app/main.py

@@ -1,7 +1,55 @@
 from fastapi import FastAPI
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 
 app = FastAPI()
 
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "openai/whisper-large-v3"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    max_new_tokens=128,
+    chunk_length_s=30,
+    batch_size=16,
+    return_timestamps=True,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+def speech_to_text(path_to_file: str) -> str:
+    """
+    Given the path to the .wav file, it returns the transcription.
+    """
+    result = pipe(path_to_file)
+    
+    return result["text"]
+
+
 @app.get("/")
 def root():
-    return {"Hello": "World"}
+    return {"Hello": "World"}
+
+@app.post("/upload/")
+async def create_upload_file(file: UploadFile = File(...)):
+    
+    # Process the file here
+    content = await file.read()
+    
+    with open("tempfile.wav", "wb") as f:
+        f.write(contents)
+    response_text = speech_to_text("tempfile.wav")
+    
+    return PlainTextResponse(content=response_text, status_code=200)