Instructions to use Atotti/Google-USM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Atotti/Google-USM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Atotti/Google-USM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Atotti/Google-USM") model = AutoModel.from_pretrained("Atotti/Google-USM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -45,7 +45,7 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
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audio_encoder.to(device)
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audio_encoder.eval()
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waveform, sampling_rate = sf.read("/
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inputs = feature_extractor(
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audio_encoder.to(device)
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audio_encoder.eval()
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waveform, sampling_rate = sf.read("/path/to/your_audio_file.wav")
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inputs = feature_extractor(
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