Automatic Speech Recognition
Transformers
ONNX
Safetensors
Armenian
whisper
asr
audio
speech
low-resource
morpheme-tokenization
armenian
compact-model
Generated from Trainer
Eval Results (legacy)
Instructions to use Chillarmo/ATOM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chillarmo/ATOM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Chillarmo/ATOM")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Chillarmo/ATOM") model = AutoModelForSpeechSeq2Seq.from_pretrained("Chillarmo/ATOM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding ONNX file of this model
Verified signatureBeep boop I am the [ONNX export bot 🤖🏎️](https://huggingface.co/spaces/onnx/export). On behalf of [Chillarmo](https://huggingface.co/Chillarmo), I would like to add to this repository the model converted to ONNX. What is ONNX? It stands for "Open Neural Network Exchange", and is the most commonly used open standard for machine learning interoperability. You can find out more at [onnx.ai](https://onnx.ai/)! The exported ONNX model can be then be consumed by various backends as TensorRT or TVM, or simply be used in a few lines with 🤗 Optimum through ONNX Runtime, check out how [here](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models)!4916958
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Training complete - Strict Armenian WER: 42%
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initial commit
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