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--- |
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base_model: |
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- hubertsiuzdak/snac_24khz |
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--- |
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SNAC in safetensors format. |
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```py |
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import json |
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from pathlib import Path |
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import safetensors.torch |
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import torch |
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import torchaudio |
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from snac import SNAC |
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config = json.loads(Path("config.json").read_text(encoding="utf-8")) |
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model = SNAC(**config) |
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state_dict = safetensors.torch.load_file("model.safetensors") |
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model.load_state_dict(state_dict) |
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model.cuda().eval() |
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input, sr = torchaudio.load("input.wav") |
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input = torchaudio.functional.resample(input, sr, model.sampling_rate) |
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input = input.cuda().unsqueeze(0) |
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with torch.inference_mode(): |
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codes = model.encode(input) |
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output = model.decode(codes) |
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output = output.cpu().squeeze(0) |
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torchaudio.save("output.wav", output, model.sampling_rate) |
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``` |
