Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Romanian
whisper
Generated from Trainer
Instructions to use iRaduS/whisper-memory-efficient with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iRaduS/whisper-memory-efficient with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="iRaduS/whisper-memory-efficient")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("iRaduS/whisper-memory-efficient") model = AutoModelForSpeechSeq2Seq.from_pretrained("iRaduS/whisper-memory-efficient", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from iRaduS/whisper-memory-efficient: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/iRaduS/whisper-memory-efficient/resolve/main/training_args.bin
- Command line
-
hf download hf://iRaduS/whisper-memory-efficient/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/iRaduS/whisper-memory-efficient/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- 7e7584188e4eb66b2d4b368f042ea45305ca61fefaf1ad6cde81089e4ad4aabb
- Size of remote file:
- 5.91 kB
- SHA256:
- a0a7483b416e2dbf361d667c4f40b7ab6168995c4a38a8bb71daa9eea53940db
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