Instructions to use moonshine-ai/moonshine-streaming-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moonshine-ai/moonshine-streaming-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="moonshine-ai/moonshine-streaming-small")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("moonshine-ai/moonshine-streaming-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from moonshine-ai/moonshine-streaming-small: direct link, hf CLI and curl.
- Browser
- Download file 159 Bytes
-
https://huggingface.co/moonshine-ai/moonshine-streaming-small/resolve/main/processor_config.json
- Command line
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hf download hf://moonshine-ai/moonshine-streaming-small/processor_config.json
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curl -L -o processor_config.json https://huggingface.co/moonshine-ai/moonshine-streaming-small/resolve/main/processor_config.json
159 Bytes
| { | |
| "processor_class": "MoonshineStreamingProcessor", | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "tokenizer_class": "PreTrainedTokenizerFast" | |
| } | |