Instructions to use labhamlet/AudioSphere with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use labhamlet/AudioSphere with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="labhamlet/AudioSphere", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("labhamlet/AudioSphere", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload feature extractor
Browse files
feature_extraction_gramt_ambisonics.py
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@@ -177,6 +177,7 @@ class AmbisonicsFeatureExtractor(SequenceFeatureExtractor):
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if waveform.shape[0] == 1:
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waveform = torch.cat([waveform, waveform, waveform, waveform], dim = 0).unsqueeze(0)
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log_mel = melspec(waveform).transpose(3, 2)[0]
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elif waveform.shape[0] == 2:
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waveform = waveform[0]
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waveform = torch.cat([waveform, waveform, waveform, waveform], dim = 0).unsqueeze(0)
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if waveform.shape[0] == 1:
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waveform = torch.cat([waveform, waveform, waveform, waveform], dim = 0).unsqueeze(0)
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log_mel = melspec(waveform).transpose(3, 2)[0]
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return log_mel
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elif waveform.shape[0] == 2:
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waveform = waveform[0]
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waveform = torch.cat([waveform, waveform, waveform, waveform], dim = 0).unsqueeze(0)
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