Next possible is real-time sorry if this is too much tech stuff. I don’t have the expertise I am a programmer but simple programming compared to this
“### Real-time stem separation over Wi‑Fi (streaming architecture)
Below is a practical blueprint for a near–real-time solution where your device streams audio chunks to a GPU-backed server, which returns separated audio in a continuous stream. This minimizes end-to-end latency vs. file upload.
At a high level:
- Client captures/reads audio in small frames (e.g., 20–100 ms), encodes them (Opus/WAV PCM), and sends them over WebSocket.
- Server buffers just enough audio to feed a streaming-capable separation model, runs inference in overlapping windows, and streams back per-stem audio frames.
- Client plays back returned stems with a jitter buffer to smooth network variance.
Key components
- Transport: WebSockets for bi-directional low-latency streaming.
- Codec: PCM 16-bit WAV frames for simplicity, or Opus for bandwidth efficiency.
- Model: Demucs v4, MDX/UVR variants, or Spleeter-like models. For streaming, you’ll use a sliding-window pipeline with overlap-add to avoid boundary artifacts.
- Latency budget: capture (frame size) + client→server network + server buffering window + model inference + server→client network + playback jitter buffer.
Typical starting parameters:
- Sample rate: 44.1 kHz or 48 kHz
- Frame size: 1024–4096 samples (≈21–85 ms at 48 kHz)
- Overlap: 50% for STFT-like pipelines
- Initial server buffer (warmup): 0.5–1.5 s
- Jitter buffer: 100–300 ms
Below is a working reference design you can adapt.
Server: FastAPI + WebSocket stream (Python)
This simplified server:
- Accepts PCM chunks over a WebSocket.
- Buffers chunks into a ring buffer.
- Runs a separation worker on a rolling window with overlap-add.
- Sends back per-stem PCM chunks tagged with sequence numbers.
Notes:
- Replace the placeholder “fake” separator with a real streaming pipeline (Demucs windowed inference or MDX with overlap).
- For true low latency, keep the model loaded on GPU and pre-warm it.
```python
server.py
import asyncio
import json
import struct
from typing import Dict, Any, List
import numpy as np
import uvicorn
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.websockets import WebSocketState
from fastapi.middleware.cors import CORSMiddleware
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=[“*”], allow_credentials=True, allow_methods=[“*”], allow_headers=[“*”]
)
SAMPLE_RATE = 48000
CHANNELS = 1 # mono for input; you can extend to stereo
FRAME_SAMPLES = 2048 # ~42.7 ms at 48 kHz
BYTES_PER_SAMPLE = 2 # int16
FRAME_BYTES = FRAME_SAMPLES * BYTES_PER_SAMPLE * CHANNELS
Separation window
WINDOW_SAMPLES = SAMPLE_RATE # 1.0s window
HOP_SAMPLES = WINDOW_SAMPLES // 2 # 50% overlap
class StreamingSeparator:
def _init_(self, sample_rate: int):
self.sample_rate = sample_rate
# TODO: load your model here (e.g., Demucs/MDX). Keep on GPU and warmed up.
def process_window(self, audio_np: np.ndarray) -> Dict\[str, np.ndarray\]:
"""
audio_np: shape \[samples\], mono float32 in \[-1,1\]
Returns dict of stems of same length (or slightly shorter if model trims).
Replace with actual model inference using a torch.no_grad() section.
"""
# Placeholder: pass-through to 'mixture' and a fake 'vocals' low-passed
mixture = audio_np
# "Fake" vocals: a simple high-pass emphasis (not real separation)
from scipy.signal import butter, filtfilt
b, a = butter(4, 200.0 / (self.sample_rate / 2.0), btype='high')
vocals = filtfilt(b, a, mixture)
other = mixture - 0.5 \* vocals
return {
"mixture": mixture,
"vocals": vocals,
"other": other
}
separator = StreamingSeparator(SAMPLE_RATE)
def int16_to_float32(pcm_bytes: bytes) → np.ndarray:
arr = np.frombuffer(pcm_bytes, dtype=np.int16).astype(np.float32) / 32768.0
return arr
def float32_to_int16(arr: np.ndarray) → bytes:
arr = np.clip(arr, -1.0, 1.0)
return (arr * 32767.0).astype(np.int16).tobytes()
@app.websocket(“/ws/separate”)
async def ws_separate(ws: WebSocket):
await ws.accept()
# ring buffer for input audio
input_buffer = np.zeros(0, dtype=np.float32)
# output overlap-add state per stem
overlap_cache: Dict[str, np.ndarray] = {}
seq_in = 0
seq_out = 0
# control: expect a JSON config message first
try:
init_msg = await ws.receive_text()
cfg = json.loads(init_msg)
# cfg could specify sample_rate, stems, etc. Here we assume defaults.
except Exception:
await ws.close(code=1002)
return
async def send_chunk(stem: str, seq: int, chunk: np.ndarray):
header = json.dumps({"stem": stem, "seq": seq, "samples": int(chunk.shape\[0\])})
await ws.send_text(header)
await ws.send_bytes(float32_to_int16(chunk))
try:
while True:
msg = await ws.receive()
if msg\["type"\] == "websocket.disconnect":
break
if msg\["type"\] == "websocket.receive_bytes":
# Append incoming PCM16 frame
pcm_bytes = msg\["bytes"\]
if len(pcm_bytes) == 0:
continue
audio_f32 = int16_to_float32(pcm_bytes)
input_buffer = np.concatenate(\[input_buffer, audio_f32\])
seq_in += 1
# Process as long as we have enough for a window
while input_buffer.shape\[0\] >= WINDOW_SAMPLES:
window = input_buffer\[:WINDOW_SAMPLES\]
input_buffer = input_buffer\[HOP_SAMPLES:\] # hop forward
stems = separator.process_window(window)
# Simple overlap-add using Hann window
hann = np.hanning(WINDOW_SAMPLES).astype(np.float32)
for stem_name, stem_audio in stems.items():
stem_audio = stem_audio.astype(np.float32)
stem_audio \*= hann
# Get previous overlap cache
prev = overlap_cache.get(stem_name)
if prev is None or prev.shape\[0\] != WINDOW_SAMPLES:
prev = np.zeros(WINDOW_SAMPLES, dtype=np.float32)
# Overlap-add: first half add to prev tail and emit HOP chunk
first_half = stem_audio\[:HOP_SAMPLES\] + prev\[HOP_SAMPLES:\]
# send chunk
await send_chunk(stem_name, seq_out, first_half)
# Prepare cache: store current window for next overlap
overlap_cache\[stem_name\] = stem_audio
seq_out += 1
elif msg\["type"\] == "websocket.receive_text":
# Could handle control messages, e.g., stop, change stems, etc.
cmd = json.loads(msg\["text"\])
if cmd.get("cmd") == "stop":
break
except WebSocketDisconnect:
pass
finally:
if ws.application_state != WebSocketState.DISCONNECTED:
await ws.close()
```
Run the server:
```bash
pip install fastapi uvicorn numpy scipy
uvicorn server:app --host 0.0.0.0 --port 8000
```
To integrate a real separator:
- Load your torch model in `StreamingSeparator._init_`.
- In `process_window`, run model inference on the window (consider stereo and batching).
- Maintain consistent latency by fixing `WINDOW_SAMPLES` and `HOP_SAMPLES`.
For Demucs-like models, use windowed inference with 50% overlap, and soft masks to reduce seams. Keep the model on GPU and avoid reallocation per call.
Client: Python example (streams microphone or WAV to server and plays stems)
This client:
- Captures audio frames (or reads from a WAV file).
- Sends frames as PCM16 over WebSocket.
- Receives back per-stem frames and plays them using a jitter buffer.
You can adapt this to a browser (WebAudio + WebSocket) or mobile. For Python, we’ll use `sounddevice` for capture/playback.
```python
client.py
import asyncio
import json
import queue
import numpy as np
import sounddevice as sd
import websockets
SAMPLE_RATE = 48000
CHANNELS = 1
FRAME_SAMPLES = 2048
BYTES_PER_SAMPLE = 2
SERVER_URL = “ws://YOUR_SERVER_IP:8000/ws/separate”
def float32_to_int16(arr: np.ndarray) → bytes:
arr = np.clip(arr, -1.0, 1.0)
return (arr * 32767.0).astype(np.int16).tobytes()
def int16_to_float32(pcm_bytes: bytes) → np.ndarray:
return np.frombuffer(pcm_bytes, dtype=np.int16).astype(np.float32) / 32768.0
async def run():
# Queues per stem for playback
stem_queues = {“vocals”: queue.Queue(maxsize=100), “other”: queue.Queue(maxsize=100), “mixture”: queue.Queue(maxsize=100)}
# Playback streams per stem (mix or solo as desired)
def playback_callback(outdata, frames, time, status, stem="vocals"):
try:
chunk = stem_queues\[stem\].get_nowait()
except queue.Empty:
chunk = np.zeros(frames, dtype=np.float32)
outdata\[:\] = chunk.reshape(-1, 1)
stream_vocals = sd.OutputStream(samplerate=SAMPLE_RATE, channels=1, blocksize=FRAME_SAMPLES,
dtype="float32", callback=lambda \*args: playback_callback(\*args, stem="vocals"))
stream_other = sd.OutputStream(samplerate=SAMPLE_RATE, channels=1, blocksize=FRAME_SAMPLES,
dtype="float32", callback=lambda \*args: playback_callback(\*args, stem="other"))
# You can start only one stem or mix them locally as you prefer.
stream_vocals.start()
# stream_other.start()
async with websockets.connect(SERVER_URL, max_size=None) as ws:
# Send init config
await ws.send(json.dumps({"sample_rate": SAMPLE_RATE, "channels": CHANNELS}))
# Microphone input stream
in_stream = sd.InputStream(samplerate=SAMPLE_RATE, channels=1, blocksize=FRAME_SAMPLES, dtype="float32")
in_stream.start()
try:
while True:
# Read a frame from mic
indata, \_ = in_stream.read(FRAME_SAMPLES)
pcm = float32_to_int16(indata.flatten())
await ws.send(pcm)
# Receive any available frames (drain loop)
recv_more = True
while recv_more:
try:
msg = await asyncio.wait_for(ws.recv(), timeout=0.0)
except asyncio.TimeoutError:
recv_more = False
break
if isinstance(msg, str):
header = json.loads(msg)
stem = header\["stem"\]
samples = header\["samples"\]
# next message is the corresponding bytes payload
payload = await ws.recv()
audio = int16_to_float32(payload)
# Optional: basic jitter buffer; if too much data, drop old
q = stem_queues.get(stem)
if q:
if q.full():
try: q.get_nowait()
except queue.Empty: pass
q.put_nowait(audio)
finally:
in_stream.stop()
in_stream.close()
stream_vocals.stop()
stream_vocals.close()
# stream_other.stop(); stream_other.close()
if _name_ == “_main_”:
asyncio.run(run())
```
Replace `YOUR_SERVER_IP` with your server’s IP/hostname.
Integrating a real model (Demucs/MDX) with windowing
Checklist:
- Load model once:
- torch.set_grad_enabled(False)
- model.eval().to(device)
- Pre-allocate tensors on GPU.
- For each `process_window`:
- Convert `float32` NumPy → torch tensor on GPU: `torch.from_numpy(window).to(device)`
- If stereo needed, `window` shape [2, T].
- Compute STFT or feed raw waveform depending on model.
- Get stems; ensure time alignment.
- Overlap-add:
- Use Hann or Tukey window.
- Maintain per-stem overlap cache exactly as shown.
- Latency tuning:
- Reduce `WINDOW_SAMPLES` to 0.5 s (with 50% hop) for lower latency, at slight quality cost.
- Keep a 100–200 ms client jitter buffer to hide network jitter.
Security, scaling, and ops
- Protect the WebSocket endpoint with a token (JWT in the first control message).
- Use TLS (wss://) via a reverse proxy (Caddy/Nginx) and systemd to keep the server running.
- If you expect multiple concurrent clients, use a lightweight queue per connection and consider batching on GPU if windows line up.
When to choose Opus
- If Wi‑Fi bandwidth is constrained, encode frames with Opus at 48 kHz mono 64–96 kbps. Use `pyogg`/`opuslib` on Python client or native Opus in browser. This adds a small codec delay but saves bandwidth substantially.
Next steps
- Do you want me to provide:
- A Demucs-based `StreamingSeparator` implementation with PyTorch?
- A browser client (WebAudio + Opus + WebSocket) so phones can use it without Python?
- A Dockerfile and systemd unit to deploy the server on a GPU VM?
. Cool that it works for you! 
Concept Overview
How It Works (Architecturally)
What You Need
Seamlessness Factor
Limitations
TL;DR
Step 4 – Connect Your Device
Step 5 – Optimization & Seamlessness
Optional — Cloud / Zero Setup Options
Team Denon is extremely experienced in so many things within the intersection of music and tech. I’m sure they have their own licenses to AI dev stuff.