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wav2vec2-base-superb-sd-neuronx
Jingya/wav2vec2-base-superb-sd-neuronx
wav2vec2-base-superb-sd-neuronx is a machine learning model from Jingya. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
This is model is compiled explicitly for AWS Neuronx(inferentia 2 / trainium 1) with the following codes:
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Jul 9, 2024
- Updated
- Jul 9, 2024
- SHA
- c5af7e81
Model card
From the Hugging Face model README
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")
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Jul 9, 2024
- Updated
- Jul 9, 2024