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library_name: transformers
license: other
tags:
- audio
- audio-codec
- pytorch
- bfloat16
---
# Liquid Codec BF16
## Remote-code loading
The repository includes the codec configuration and model implementation, so it can be loaded with `AutoModel`:
```python
import torch
from transformers import AutoModel
codec = AutoModel.from_pretrained(
"LiquidAI/Liquid-Codec",
trust_remote_code=True,
dtype=torch.bfloat16,
device_map="cuda",
)
waveform = torch.randn(1, 24_000 * 3, device="cuda") # 24 kHz
codes = codec.encode(waveform).audio_codes
reconstructed = codec.decode(codes).audio_values
```
## Native loading and streaming
```python
import torch
from transformers import LiquidCodecModel, LiquidCodecStreamingCache
codec = LiquidCodecModel.from_pretrained("LiquidAI/Liquid-Codec", dtype=torch.bfloat16, device_map="cuda")
waveform = torch.randn(1, 24_000 * 3, device="cuda") # 24 kHz
codes = codec.encode(waveform).audio_codes # (1, 12, 38)
reconstructed = codec.decode(codes).audio_values # (1, 38 * 1920), float32
# streaming synthesis: every chunk returns the samples that no later frame changes any more
cache = LiquidCodecStreamingCache(codec.config)
chunks = [codec.decode(codes[..., i : i + 1], cache, finalize=False).audio_values for i in range(codes.shape[-1])]
chunks.append(codec.decode(codes[..., :0], cache, finalize=True).audio_values)
streamed = torch.cat(chunks, dim=-1)
```
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