Instructions to use fkHug/modelFromWav2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fkHug/modelFromWav2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fkHug/modelFromWav2vec")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("fkHug/modelFromWav2vec") model = AutoModelForCTC.from_pretrained("fkHug/modelFromWav2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from fkHug/modelFromWav2vec: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
-
https://huggingface.co/fkHug/modelFromWav2vec/resolve/main/trainer_state.json
- Command line
-
hf download hf://fkHug/modelFromWav2vec/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/fkHug/modelFromWav2vec/resolve/main/trainer_state.json
1.3 kB
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 48.0, | |
| "global_step": 1200, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 16.0, | |
| "learning_rate": 8e-05, | |
| "loss": 8.9546, | |
| "step": 400 | |
| }, | |
| { | |
| "epoch": 16.0, | |
| "eval_loss": 3.0705316066741943, | |
| "eval_runtime": 27.2617, | |
| "eval_samples_per_second": 7.336, | |
| "eval_wer": 0.99836867862969, | |
| "step": 400 | |
| }, | |
| { | |
| "epoch": 32.0, | |
| "learning_rate": 6.129032258064517e-05, | |
| "loss": 1.7778, | |
| "step": 800 | |
| }, | |
| { | |
| "epoch": 32.0, | |
| "eval_loss": 0.604729413986206, | |
| "eval_runtime": 27.2625, | |
| "eval_samples_per_second": 7.336, | |
| "eval_wer": 0.7781402936378466, | |
| "step": 800 | |
| }, | |
| { | |
| "epoch": 48.0, | |
| "learning_rate": 9.67741935483871e-06, | |
| "loss": 0.2996, | |
| "step": 1200 | |
| }, | |
| { | |
| "epoch": 48.0, | |
| "eval_loss": 0.49922919273376465, | |
| "eval_runtime": 28.4037, | |
| "eval_samples_per_second": 7.041, | |
| "eval_wer": 0.6753670473083198, | |
| "step": 1200 | |
| } | |
| ], | |
| "max_steps": 1275, | |
| "num_train_epochs": 51, | |
| "total_flos": 3.8339975189263565e+18, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |