Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Latvian
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use M2LabOrg/whisper-small-lv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M2LabOrg/whisper-small-lv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="M2LabOrg/whisper-small-lv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("M2LabOrg/whisper-small-lv") model = AutoModelForSpeechSeq2Seq.from_pretrained("M2LabOrg/whisper-small-lv") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67eb27ec6457d1f6f99d050242067dc97b0aa478d61dda320c2b205f9a69dc84
- Size of remote file:
- 5.24 kB
- SHA256:
- 417d2b23c2761e2bba08c0e46bb2f03bd7b06f8389f93161cb3334c78bc3775c
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