# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained("SinghManish/TransformersModel")
model = AutoModel.from_pretrained("SinghManish/TransformersModel", device_map="auto")Quick Links
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SinghManish / audio-classification-model
like 0 Feature Extraction TensorFlow Transformers wav2vec2 generated_from_keras_callback License: apache-2.0 Model card Files and versions Community Settings audio-classification-model / README.md SinghManish's picture SinghManish Upload model f1e8dbd about 1 month ago Preview Code | raw history blame edit delete 997 Bytes
license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: audio-classification-model results: []
audio-classification-model
This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.31.0.dev0
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3
- Downloads last month
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SinghManish/TransformersModel")