server: implement modal backend for llm and transcription

server/reflector/processors/audio_transcript_modal.py

@@ -0,0 +1,62 @@
+"""
+Implementation using the GPU service from modal.com
+
+API will be a POST request to TRANSCRIPT_URL:
+
+```form
+"timestamp": 123.456
+"language": "en"
+"file": <audio file>
+```
+
+"""
+
+from reflector.processors.audio_transcript import AudioTranscriptProcessor
+from reflector.processors.audio_transcript_auto import AudioTranscriptAutoProcessor
+from reflector.processors.types import AudioFile, Transcript, Word
+from reflector.settings import settings
+from reflector.utils.retry import retry
+import httpx
+
+
+class AudioTranscriptModalProcessor(AudioTranscriptProcessor):
+    def __init__(self, modal_api_key: str):
+        super().__init__()
+        self.transcript_url = settings.TRANSCRIPT_URL + "/transcribe"
+        self.timeout = settings.TRANSCRIPT_TIMEOUT
+        self.headers = {
+            "Authorization": f"Bearer {modal_api_key}",
+        }
+
+    async def _transcript(self, data: AudioFile):
+        async with httpx.AsyncClient() as client:
+            print(f"Try to transcribe audio {data.path.name}")
+            files = {
+                "file": (data.path.name, data.path.open("rb")),
+            }
+            form = {
+                "timestamp": float(round(data.timestamp, 2)),
+            }
+            response = await retry(client.post)(
+                self.transcript_url,
+                files=files,
+                data=form,
+                timeout=self.timeout,
+                headers=self.headers,
+            )
+
+            print(f"Transcript response: {response.status_code} {response.content}")
+            response.raise_for_status()
+            result = response.json()
+            transcript = Transcript(
+                text=result["text"],
+                words=[
+                    Word(text=word["text"], start=word["start"], end=word["end"])
+                    for word in result["words"]
+                ],
+            )
+
+        return transcript
+
+
+AudioTranscriptAutoProcessor.register("modal", AudioTranscriptModalProcessor)