Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use kavyamanohar/XLSR-WithLM-Malayalam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kavyamanohar/XLSR-WithLM-Malayalam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kavyamanohar/XLSR-WithLM-Malayalam")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("kavyamanohar/XLSR-WithLM-Malayalam") model = AutoModelForCTC.from_pretrained("kavyamanohar/XLSR-WithLM-Malayalam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download language_model/trigram.bin from kavyamanohar/XLSR-WithLM-Malayalam: direct link, hf CLI and curl.
- Browser
- Download file 83.2 MB
-
https://huggingface.co/kavyamanohar/XLSR-WithLM-Malayalam/resolve/main/language_model/trigram.bin
- Command line
-
hf download hf://kavyamanohar/XLSR-WithLM-Malayalam/language_model/trigram.bin
-
curl -L -o trigram.bin https://huggingface.co/kavyamanohar/XLSR-WithLM-Malayalam/resolve/main/language_model/trigram.bin
83.2 MB
- Xet hash:
- 3106f2a5a392d6bd468e42b75141cda86bc83546dd8ce703f8f1cd69caaee77b
- Size of remote file:
- 83.2 MB
- SHA256:
- c7424cb716afc8937b8a981fa08edadce4f7846006e5d484852140c85ebdecbd
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