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---
base_model:
- descript/dac_16khz
library_name: transformers.js
---
ONNX weights for https://huggingface.co/descript/dac_16khz.
## Inference sample code
```py
import onnxruntime as ort
encoder_session = ort.InferenceSession("encoder_model.onnx")
decoder_session = ort.InferenceSession("decoder_model.onnx")
encoder_inputs = {encoder_session.get_inputs()[0].name: dummy_encoder_inputs.numpy()}
encoder_outputs = encoder_session.run(None, encoder_inputs)[0]
decoder_inputs = {decoder_session.get_inputs()[0].name: encoder_outputs}
decoder_outputs = decoder_session.run(None, decoder_inputs)[0]
# Print the results
print("Encoder Output Shape:", encoder_outputs.shape)
print("Decoder Output Shape:", decoder_outputs.shape)
```
## Conversion code
```py
import torch
import torch.nn as nn
from transformers import DacModel
class DacEncoder(nn.Module):
def __init__(self, model):
super(DacEncoder, self).__init__()
self.model = model
def forward(self, input_values):
return self.model.encode(input_values).audio_codes
class DacDecoder(nn.Module):
def __init__(self, model):
super(DacDecoder, self).__init__()
self.model = model
def forward(self, audio_codes):
quantized_representation = self.model.quantizer.from_codes(audio_codes)[0]
return self.model.decoder(quantized_representation)
model = DacModel.from_pretrained("descript/dac_16khz")
encoder = DacEncoder(model)
decoder = DacDecoder(model)
# Export encoder
dummy_encoder_inputs = torch.randn((4, 1, 12340))
torch.onnx.export(
encoder,
dummy_encoder_inputs,
"encoder_model.onnx",
export_params=True,
opset_version=14,
do_constant_folding=True,
input_names=['input_values'],
output_names=['audio_codes'],
dynamic_axes={
'input_values': {0: 'batch_size', 1: 'num_channels', 2: 'sequence_length'},
'audio_codes': {0: 'batch_size', 2: 'time_steps'},
},
)
# Export decoder
dummy_decoder_inputs = torch.randint(model.config.codebook_size, (4, model.config.n_codebooks, 100))
torch.onnx.export(
decoder,
dummy_decoder_inputs,
"decoder_model.onnx",
export_params=True,
opset_version=14,
do_constant_folding=True,
input_names=['audio_codes'],
output_names=['audio_values'],
dynamic_axes={
'audio_codes': {0: 'batch_size', 2: 'time_steps'},
'audio_values': {0: 'batch_size', 1: 'num_channels', 2: 'sequence_length'},
},
)
``` |