This is model is compiled explicitly for AWS Neuronx(inferentia 2 / trainium 1) with the following codes:
from datasets import load_dataset
from transformers import AutoProcessor
from optimum.neuron import NeuronModelForAudioClassification, pipeline
dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
dataset = dataset.sort("id")
sampling_rate = dataset.features["audio"].sampling_rate
model_id = "anton-l/wav2vec2-base-superb-sd"
feature_extractor = AutoFeatureExtractor.from_pretrained("anton-l/wav2vec2-base-superb-sd")
input_shapes = {"batch_size": 1, "audio_sequence_length": 100000}
compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
model = NeuronModelForAudioFrameClassification.from_pretrained(
model_id,
export=True,
disable_neuron_cache=True,
**input_shapes,
**compiler_args,
)
model.save_pretrained("wav2vec2_neuron")
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