readme

```python

from musiclm_pytorch import MuLaNEmbedQuantizer

wavs = torch.randn(2, 1024)

embeds = mulan.get_audio_latents(wavs)

# setup the quantizer with the namespaced conditioning embeddings, unique per quantizer as well as namespace (per transformer)

quantizer = MuLaNEmbedQuantizer(

    mulan = mulan,

    mulan = mulan,                          # pass in trained mulan from above

    conditioning_dims = (1024, 1024, 1024), # say all three transformers have model dimensions of 1024

    namespaces = ('semantic', 'coarse', 'fine')

)

# now say you want the conditioning embeddings for semantic transformer

wavs = torch.randn(2, 1024)

conds = quantizer(wavs = wavs, namespace = 'semantic') # (2, 8, 1024) - 8 is number of quantizers

```