Instructions to use hf-internal-testing/tiny-random-MimiModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MimiModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-MimiModel")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-MimiModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MimiModel") - Notebooks
- Google Colab
- Kaggle
| 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. | |
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| ## Uses | |
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| 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). | |
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