Instructions to use mutemoon/whisper-tiny-zh-food with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mutemoon/whisper-tiny-zh-food with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mutemoon/whisper-tiny-zh-food")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mutemoon/whisper-tiny-zh-food") model = AutoModelForSpeechSeq2Seq.from_pretrained("mutemoon/whisper-tiny-zh-food", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload WhisperForConditionalGeneration
Browse files- config.json +2 -2
- model.safetensors +2 -2
config.json
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"activation_function": "gelu",
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"apply_spec_augment": false,
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"architectures": [
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"
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"attention_dropout": 0.0,
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"begin_suppress_tokens": [
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"init_std": 0.02,
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"is_encoder_decoder":
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"mask_feature_length": 10,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.0,
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"activation_function": "gelu",
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"apply_spec_augment": false,
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"architectures": [
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"WhisperForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"begin_suppress_tokens": [
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],
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"mask_feature_length": 10,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.0,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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size 151061672
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