Instructions to use mkrausio/EmoWhisper-BnS-Small-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkrausio/EmoWhisper-BnS-Small-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mkrausio/EmoWhisper-BnS-Small-v0.1")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mkrausio/EmoWhisper-BnS-Small-v0.1") model = AutoModelForSpeechSeq2Seq.from_pretrained("mkrausio/EmoWhisper-BnS-Small-v0.1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff7989cfa11ab91c3362d3625207999fe4e37d2a7d8648cf4bdcc6217be49210
- Size of remote file:
- 967 MB
- SHA256:
- 8cf09fd57564823a271391dd6fb864e55a24a054cef746203864596a829d9dc9
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