README.md

---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

## Code to create model
```py
import torch
from transformers import MimiConfig, MimiModel, AutoProcessor

model_id = 'kyutai/mimi'
config = MimiConfig.from_pretrained(
    model_id,
    intermediate_size=64,
    hidden_size=16,
    num_hidden_layers=2,
    num_key_value_heads=2,
    upsample_groups=16,
    num_filters=8,
    codebook_dim=8,
    vector_quantization_hidden_dimension=8,
    codebook_size=32,
)

# Create model and randomize all weights
model = MimiModel(config)

torch.manual_seed(0) # Set for reproducibility
for name, param in model.named_parameters():
    param.data = torch.randn_like(param)

processor = AutoProcessor.from_pretrained(model_id)
```

## ONNX conversion code
```py
import torch
import torch.nn as nn
from transformers import MimiModel

class MimiEncoder(nn.Module):
    def __init__(self, model):
        super(MimiEncoder, self).__init__()
        self.model = model

    def forward(self, input_values, padding_mask=None):
        return self.model.encode(input_values, padding_mask=padding_mask).audio_codes

class MimiDecoder(nn.Module):
    def __init__(self, model):
        super(MimiDecoder, self).__init__()
        self.model = model

    def forward(self, audio_codes, padding_mask=None):
        return self.model.decode(audio_codes, padding_mask=padding_mask).audio_values

model = MimiModel.from_pretrained("hf-internal-testing/tiny-random-MimiModel")
encoder = MimiEncoder(model)
decoder = MimiDecoder(model)

dummy_encoder_inputs = torch.randn((5, 1, 82500))
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: 'codes_length'},
    },
)

dummy_decoder_inputs = torch.randint(8, (4, model.config.num_quantizers, 91))
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: 'codes_length'},
        'audio_values': {0: 'batch_size', 1: 'num_channels', 2: 'sequence_length'},
    },
)
```

## 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]

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- **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

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## Bias, Risks, and Limitations

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### Recommendations

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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

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### Training Procedure

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#### Preprocessing [optional]

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#### 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]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## 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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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