Instructions to use rashmi035/whisper-tiny-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rashmi035/whisper-tiny-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rashmi035/whisper-tiny-en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rashmi035/whisper-tiny-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("rashmi035/whisper-tiny-en") - Notebooks
- Google Colab
- Kaggle
File size: 2,007 Bytes
024bba7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-tiny-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-en
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5089
- Wer: 31.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4475 | 10.53 | 100 | 0.6788 | 20.5584 |
| 0.0166 | 21.05 | 200 | 0.4262 | 19.2893 |
| 0.0005 | 31.58 | 300 | 0.4534 | 22.8426 |
| 0.0003 | 42.11 | 400 | 0.4673 | 68.7817 |
| 0.0002 | 52.63 | 500 | 0.4806 | 72.5888 |
| 0.0002 | 63.16 | 600 | 0.4908 | 72.3350 |
| 0.0001 | 73.68 | 700 | 0.4987 | 31.4721 |
| 0.0001 | 84.21 | 800 | 0.5045 | 31.4721 |
| 0.0001 | 94.74 | 900 | 0.5078 | 31.4721 |
| 0.0001 | 105.26 | 1000 | 0.5089 | 31.4721 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|