Instructions to use manuth/w6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manuth/w6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="manuth/w6")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("manuth/w6") model = AutoModelForSpeechSeq2Seq.from_pretrained("manuth/w6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 716e73d469aa46a9d65fbffe52d45b4b2c631faee97751dc5e31de430aa9e818
- Size of remote file:
- 6.46 GB
- SHA256:
- bce92229e36cdaef7e38cd7a671a6ada3eea939e14e66de37517f8e600d77006
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