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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9ff487eb64b6ed80e5e7f58592399a23767d4c5e422f5eb8bfcd5879da6ed8a
- Size of remote file:
- 967 MB
- SHA256:
- f6ef8213d74ea7df32b3cf8b1ffacba8db2ae1288436fedae9c19f0f29b2055e
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