Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use razhan/whisper-base-glk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razhan/whisper-base-glk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="razhan/whisper-base-glk")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("razhan/whisper-base-glk") model = AutoModelForSpeechSeq2Seq.from_pretrained("razhan/whisper-base-glk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from razhan/whisper-base-glk: direct link, hf CLI and curl.
- Browser
- Download file 258 Bytes
-
https://huggingface.co/razhan/whisper-base-glk/resolve/main/eval_results.json
- Command line
-
hf download hf://razhan/whisper-base-glk/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/razhan/whisper-base-glk/resolve/main/eval_results.json
258 Bytes
| { | |
| "epoch": 5.0, | |
| "eval_cer": 0.5467684084675434, | |
| "eval_loss": 2.6806018352508545, | |
| "eval_runtime": 53.2976, | |
| "eval_samples": 625, | |
| "eval_samples_per_second": 11.727, | |
| "eval_steps_per_second": 0.056, | |
| "eval_wer": 1.0472082810539523 | |
| } |