Create main.py

main.py

@@ -0,0 +1,54 @@
+from fastapi import FastAPI, UploadFile, File
+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,
+)
+
+sample = dataset[0]["audio"]
+
+# result = pipe(sample)
+# print(result["text"])
+
+
+@app.post("/speech_to_text")
+async def speech_to_text(file : UploadFile = File(...))
+    if file:
+        contents = await file.read()
+        with open(file.filename, "wb") as f:
+            f.write(contents)
+
+        converted_result = pipe(file.filename)
+        return {
+            "status": 200,
+            "text": converted_result["text"]
+        }
+    else:
+        return {
+            "status": -1
+        }