fal_app.py

@@ -1,14 +1,21 @@
-import io
+import datetime
 from pathlib import Path
+import threading
 from audiocraft.data.audio import audio_write
 import fal
-from fastapi import WebSocket
+from fastapi import Response, status
 import torch
 
-from prompts import PROMPTS
-
 DATA_DIR = Path("/data/audio")
 
+PROMPTS = [
+    "Create a futuristic lo-fi beat that blends modern electronic elements with synthwave influences. Incorporate smooth, atmospheric synths and gentle, relaxing rhythms to evoke a sense of a serene, neon-lit future. Ensure  the track is continuous with no background noise or interruptions, maintaining a calm and tranquil atmosphere throughout while adding a touch of retro-futuristic vibes.",
+    "gentle lo-fi beat with a smooth, mellow piano melody in the background. Ensure there are no background noises or interruptions, maintaining a continuous and seamless flow throughout the track. The beat should be relaxing and tranquil, perfect for a calm and reflective atmosphere.",
+    "Create an earthy lo-fi beat that evokes a natural, grounded atmosphere. Incorporate organic sounds like soft percussion, rustling leaves, and gentle acoustic instruments. The track should have a warm, soothing rhythm with a continuous flow and no background noise or interruptions, maintaining a calm and reflective ambiance throughout.",
+    "Create a soothing lo-fi beat featuring gentle, melodic guitar riffs. The guitar should be the focal point, supported by subtle, ambient electronic elements and a smooth, relaxed rhythm. Ensure the track is continuous with no background noise or interruptions, maintaining a warm and mellow atmosphere throughout.",
+    "Create an ambient lo-fi beat with a tranquil and ethereal atmosphere. Use soft, atmospheric pads, gentle melodies, and minimalistic percussion to evoke a sense of calm and serenity. Ensure the track is continuous with no background noise or interruptions, maintaining a soothing and immersive ambiance throughout.",
+]
+
 
 class InfinifiFalApp(fal.App, keep_alive=300):
     machine_type = "GPU-A6000"
@@ -17,8 +24,11 @@ class InfinifiFalApp(fal.App, keep_alive=300):
         "audiocraft==1.3.0",
         "torchaudio==2.1.0",
         "websockets==11.0.3",
+        "numpy==1.26.4",
     ]
 
+    __is_generating = False
+
     def setup(self):
         import torchaudio
         from audiocraft.models.musicgen import MusicGen
@@ -28,22 +38,26 @@ class InfinifiFalApp(fal.App, keep_alive=300):
 
     @fal.endpoint("/generate")
     def run(self):
-        wav = self.model.generate(PROMPTS)
+        if self.__is_generating:
+            return Response(status_code=status.HTTP_409_CONFLICT)
+        threading.Thread(target=self.__generate_audio).start()
 
-        serialized = []
-        for one_wav in wav:
-            buf = io.BytesIO()
-            torch.save(one_wav.cpu(), buf)
-            serialized.append(buf.getvalue())
+    @fal.endpoint("/clips/{index}")
+    def get_clips(self, index):
+        if self.__is_generating:
+            return Response(status_code=status.HTTP_404_NOT_FOUND)
 
-        return serialized
+        path = DATA_DIR.joinpath(f"{index}")
+        with open(path.with_suffix(".mp3"), "rb") as f:
+            data = f.read()
+            return Response(content=data)
 
-    @fal.endpoint("/ws")
-    async def run_ws(self, ws: WebSocket):
-        await ws.accept()
+    def __generate_audio(self):
+        self.__is_generating = True
+
+        print(f"[INFO] {datetime.datetime.now()}: generating audio...")
 
         wav = self.model.generate(PROMPTS)
-
         for i, one_wav in enumerate(wav):
             path = DATA_DIR.joinpath(f"{i}")
             audio_write(
@@ -53,9 +67,7 @@ class InfinifiFalApp(fal.App, keep_alive=300):
                 format="mp3",
                 strategy="loudness",
                 loudness_compressor=True,
+                make_parent_dir=True,
             )
-            with open(path, "rb") as f:
-                data = f.read()
-                await ws.send_bytes(data)
 
-        await ws.close()
+        self.__is_generating = False

server.py

@@ -1,9 +1,11 @@
 import threading
 import os
+from time import sleep
+import requests
 
 import websocket
 from contextlib import asynccontextmanager
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect, status
 from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
 from logger import log_info, log_warn
@@ -15,18 +17,18 @@ current_index = -1
 t = None
 # websocket connection to the inference server
 ws = None
-ws_url = ""
+inference_url = ""
 ws_connection_manager = WebSocketConnectionManager()
 active_listeners = set()
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    global ws, ws_url
+    global ws, inference_url
 
-    ws_url = os.environ.get("INFERENCE_SERVER_WS_URL")
-    if not ws_url:
-        ws_url = "ws://localhost:8001"
+    inference_url = os.environ.get("INFERENCE_SERVER_URL")
+    if not inference_url:
+        inference_url = "ws://localhost:8001"
 
     advance()
 
@@ -39,7 +41,7 @@ async def lifespan(app: FastAPI):
 
 
 def generate_new_audio():
-    if not ws_url:
+    if not inference_url:
         return
 
     global current_index
@@ -52,31 +54,50 @@ def generate_new_audio():
     else:
         return
 
-    log_info("generating new audio...")
+    log_info("requesting new audio...")
 
     try:
-        ws = websocket.create_connection(ws_url)
-
-        ws.send("generate")
-
-        wavs = []
-        for i in range(5):
-            raw = ws.recv()
-            if isinstance(raw, str):
-                continue
-            wavs.append(raw)
-
-        for i, wav in enumerate(wavs):
-            with open(f"{i + offset}.mp3", "wb") as f:
-                f.write(wav)
-
-        log_info("audio generated.")
-
-        ws.close()
+        print(f"{inference_url}/generate")
+        requests.post(f"{inference_url}/generate")
     except:
         log_warn(
             "inference server potentially unreachable. recycling cached audio for now."
         )
+        return
+
+    is_available = False
+    while not is_available:
+        try:
+            res = requests.post(f"{inference_url}/clips/0", stream=True)
+        except:
+            log_warn(
+                "inference server potentially unreachable. recycling cached audio for now."
+            )
+            return
+
+        if res.status_code != status.HTTP_200_OK:
+            print("still generating...")
+            sleep(5)
+            continue
+
+        print("inference complete! downloading new clips")
+
+        is_available = True
+        with open(f"{offset}.mp3", "wb") as f:
+            for chunk in res.iter_content(chunk_size=128):
+                f.write(chunk)
+
+    for i in range(4):
+        res = requests.post(f"{inference_url}/clips/{i + 1}", stream=True)
+
+        if res.status_code != status.HTTP_200_OK:
+            continue
+
+        with open(f"{i + 1 + offset}.mp3", "wb") as f:
+            for chunk in res.iter_content(chunk_size=128):
+                f.write(chunk)
+
+    log_info("audio generated.")
 
 
 def advance():