Instructions to use Mitradn/whisper-cap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mitradn/whisper-cap with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Mitradn/whisper-cap")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Mitradn/whisper-cap") model = AutoModelForSpeechSeq2Seq.from_pretrained("Mitradn/whisper-cap") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 3.4235229155162896, | |
| "best_model_checkpoint": "./Mitradn/whisper-cap/checkpoint-140", | |
| "epoch": 1.8666666666666667, | |
| "eval_steps": 20, | |
| "global_step": 140, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.2, | |
| "learning_rate": 2.2e-06, | |
| "loss": 5.5548, | |
| "step": 15 | |
| }, | |
| { | |
| "epoch": 0.27, | |
| "eval_loss": 3.370603084564209, | |
| "eval_runtime": 142.999, | |
| "eval_samples_per_second": 2.112, | |
| "eval_steps_per_second": 0.266, | |
| "eval_wer": 91.77250138045278, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.4, | |
| "learning_rate": 5.2e-06, | |
| "loss": 3.1877, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 0.53, | |
| "eval_loss": 1.1552890539169312, | |
| "eval_runtime": 148.487, | |
| "eval_samples_per_second": 2.034, | |
| "eval_steps_per_second": 0.256, | |
| "eval_wer": 38.92876863611264, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 0.6, | |
| "learning_rate": 8.2e-06, | |
| "loss": 1.4298, | |
| "step": 45 | |
| }, | |
| { | |
| "epoch": 0.8, | |
| "learning_rate": 9.600000000000001e-06, | |
| "loss": 0.8885, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 0.8, | |
| "eval_loss": 0.7651895880699158, | |
| "eval_runtime": 149.4033, | |
| "eval_samples_per_second": 2.021, | |
| "eval_steps_per_second": 0.254, | |
| "eval_wer": 31.971286581998896, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "learning_rate": 8.6e-06, | |
| "loss": 0.7021, | |
| "step": 75 | |
| }, | |
| { | |
| "epoch": 1.07, | |
| "eval_loss": 0.545852780342102, | |
| "eval_runtime": 139.5729, | |
| "eval_samples_per_second": 2.164, | |
| "eval_steps_per_second": 0.272, | |
| "eval_wer": 12.755383765875209, | |
| "step": 80 | |
| }, | |
| { | |
| "epoch": 1.2, | |
| "learning_rate": 7.600000000000001e-06, | |
| "loss": 0.5242, | |
| "step": 90 | |
| }, | |
| { | |
| "epoch": 1.33, | |
| "eval_loss": 0.3470919132232666, | |
| "eval_runtime": 136.2966, | |
| "eval_samples_per_second": 2.216, | |
| "eval_steps_per_second": 0.279, | |
| "eval_wer": 5.687465488680287, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 1.4, | |
| "learning_rate": 6.600000000000001e-06, | |
| "loss": 0.385, | |
| "step": 105 | |
| }, | |
| { | |
| "epoch": 1.6, | |
| "learning_rate": 5.600000000000001e-06, | |
| "loss": 0.2616, | |
| "step": 120 | |
| }, | |
| { | |
| "epoch": 1.6, | |
| "eval_loss": 0.20668885111808777, | |
| "eval_runtime": 138.619, | |
| "eval_samples_per_second": 2.179, | |
| "eval_steps_per_second": 0.274, | |
| "eval_wer": 5.356156819436776, | |
| "step": 120 | |
| }, | |
| { | |
| "epoch": 1.8, | |
| "learning_rate": 4.600000000000001e-06, | |
| "loss": 0.176, | |
| "step": 135 | |
| }, | |
| { | |
| "epoch": 1.87, | |
| "eval_loss": 0.12983117997646332, | |
| "eval_runtime": 145.2867, | |
| "eval_samples_per_second": 2.079, | |
| "eval_steps_per_second": 0.262, | |
| "eval_wer": 3.4235229155162896, | |
| "step": 140 | |
| } | |
| ], | |
| "logging_steps": 15, | |
| "max_steps": 200, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 20, | |
| "total_flos": 6.4585412591616e+17, | |
| "train_batch_size": 16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |