onnx-community
/

dac_24khz-ONNX

@@ -1,199 +1,87 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+base_model:
+- descript/dac_24khz
+library_name: transformers.js
 ---
+ONNX weights for https://huggingface.co/descript/dac_24khz.
+## 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_24khz")
+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'},
+    },
+)
+```