Create main.py

main.py

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+import time
+from fastapi import FastAPI, File
+from faster_whisper import WhisperModel
+from utils import ffmpeg_read, stt
+from sentence_transformers import SentenceTransformer, util
+import torch
+
+app = FastAPI()
+
+whisper_models = ["tiny", "base", "small", "medium", "large-v1", "large-v2"]
+audio_model = WhisperModel("base", compute_type="int8", device="cpu")
+text_model = SentenceTransformer('all-MiniLM-L6-v2')
+corpus_embeddings = torch.load('corpus_embeddings.pt')
+
+
+def speech_to_text(upload_audio, model_type="whisper"):
+    """
+    Transcribe audio using whisper model.
+    """
+    audio_path = ffmpeg_read(upload_audio, sampling_rate=16000)
+    # Transcribe audio
+    if model_type == "whisper":
+        transcribe_options = dict(task="transcribe", language="ja", beam_size=5, best_of=5, vad_filter=True)
+        segments_raw, info = audio_model.transcribe(audio_path, **transcribe_options)
+        segments = [segment.text for segment in segments_raw]
+        return ' '.join(segments)
+    else:
+        text = stt(audio_path)
+        return text
+
+
+@app.get("/")
+def read_root():
+    return {"Message": "Application startup complete"}
+
+
+@app.post("/voice_detect/")
+async def voice_detect_api(
+        voice_input: bytes = File(None),
+        threshold: float = 0.8,
+        model_type: str = "whisper"
+):
+    """
+    API to detect voice from audio file.
+    """
+    start = time.time()
+    text = speech_to_text(voice_input, model_type)
+    query_embedding = text_model.encode(text, convert_to_tensor=True)
+    hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=1)[0]
+    if hits[0]['score'] > threshold:
+        similar = 1
+    else:
+        similar = 0
+    end = time.time()
+    return {"text": text,
+            "similar": similar,
+            "time_taken": end - start}