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1from datasets import load_dataset
2from transformers import AutoProcessor
3
4from optimum.neuron import NeuronModelForAudioClassification, pipeline
5
6
7dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
8dataset = dataset.sort("id")
9sampling_rate = dataset.features["audio"].sampling_rate
10
11model_id = "anton-l/wav2vec2-base-superb-sd"
12feature_extractor = AutoFeatureExtractor.from_pretrained("anton-l/wav2vec2-base-superb-sd")
13input_shapes = {"batch_size": 1, "audio_sequence_length": 100000}
14compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
15model = NeuronModelForAudioFrameClassification.from_pretrained(
16 model_id,
17 export=True,
18 disable_neuron_cache=True,
19 **input_shapes,
20 **compiler_args,
21)
22model.save_pretrained("wav2vec2_neuron")