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README.md
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# Model Usage
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```python
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from transformers import Wav2Vec2Processor
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import torch
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from torch import nn
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from datasets import load_dataset
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dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
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dataset = dataset.sort("id")
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sampling_rate = dataset.features["audio"].sampling_rate
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processor = Wav2Vec2Processor.from_pretrained("
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# loading our model weights
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model =
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# audio file is decoded on the fly
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inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
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# Model Usage
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```python
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from transformers import Wav2Vec2Processor
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from transformers import AutoModel
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import torch
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from torch import nn
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from datasets import load_dataset
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dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
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dataset = dataset.sort("id")
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sampling_rate = dataset.features["audio"].sampling_rate
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processor = Wav2Vec2Processor.from_pretrained("m-a-p/MERT-v0")
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# loading our model weights
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model = AutoModel.from_pretrained("m-a-p/MERT-v0")
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# audio file is decoded on the fly
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inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
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