created speech_to_text

Using openai whisper model for converting speech to text

speech_to_text.py

@@ -0,0 +1,36 @@
+import os
+from flask import Flask, request, jsonify
+from openai import OpenAI
+
+app = Flask(__name__)
+client = OpenAI()  # reads OPENAI_API_KEY from env
+
+def transcribe_with_timestamps(local_path: str):
+    # Use whisper-1 for verbose_json + timestamp_granularities (segment/word)
+    # timestamp_granularities requires response_format="verbose_json" :contentReference[oaicite:1]{index=1}
+    with open(local_path, "rb") as f:
+        tr = client.audio.transcriptions.create(
+            model="whisper-1",
+            file=f,
+            response_format="verbose_json",
+            timestamp_granularities=["segment"],
+        )
+    segments = []
+    for seg in tr.segments:
+        segments.append({
+            "start_ms": int(seg["start"] * 1000),
+            "end_ms": int(seg["end"] * 1000),
+            "text": seg["text"].strip(),
+        })
+    return segments
+
+@app.post("/upload-audio")
+def upload_audio():
+    file = request.files["file"]
+    local_path = f"/tmp/{file.filename}"
+    file.save(local_path)
+
+    segments = transcribe_with_timestamps(local_path)
+
+    # TODO: insert `segments` into transcript_segments table
+    return jsonify({"segments": segments})