is real-time voice conversion possible?

[eschmidbauer]

Hi- very impressed by the VC framework. It's very fast and accurate.
I'm wondering is real-time possible? I have a simple WS server that receives audio, but when i push the data through soft-vc, the end result is just noise. In the code below, I save the input stream just to confirm the audio is being received correctly (which it is).
Here is a snippet of my code:

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
hubert = torch.hub.load("bshall/hubert:main", "hubert_soft").cuda()

acoustic_load_path = "./pretrained_models/acoustic.pt"
checkpoint = torch.load(acoustic_load_path, map_location=device)["acoustic-model"]
acoustic = AcousticModel().to(device)
acoustic.load_state_dict(checkpoint)
acoustic.eval()

# load custom vocoder
hifigan_load_path = "./pretrained_models/hifigan.pt"
checkpoint = torch.load(hifigan_load_path, map_location=device)[
    "generator"]["model"]
hifigan = HifiganGenerator().to(device)
consume_prefix_in_state_dict_if_present(checkpoint, "module.")
hifigan.load_state_dict(checkpoint)
hifigan.eval()
hifigan.remove_weight_norm()



inputs = []
outputs = []
while True:
    data = None
    try:
        data = await websocket.recv()
    except:
        break
    if isinstance(data, str):
        print(f"string -> {data}")
        continue

    source = torch.from_numpy(numpy.frombuffer(
        data, dtype=numpy.int16).astype('float32') / 32767)
    source = source.reshape((1, -1))
    source = source.unsqueeze(0).cuda()
    # # Convert to the target speaker
    with torch.inference_mode():
        # Extract speech units
        units = hubert.units(source)
        # Generate target spectrogram
        mel = acoustic.generate(units).transpose(1, 2)
        # Generate audio waveform
        target = hifigan(mel)
        inputs.append(source.squeeze(0).cpu())
        outputs.append(target.squeeze(0).cpu())
        await ws.send(data)

print(f"saving files...")
input_result = torch.cat(inputs, dim=1)
torchaudio.save("inputs.wav", input_result, sample_rate=16_000)
output_result = torch.cat(outputs, dim=1)
torchaudio.save("outputs.wav", output_result, sample_rate=16_000)