Instructions to use alakxender/whisper-small-dv-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alakxender/whisper-small-dv-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alakxender/whisper-small-dv-full")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alakxender/whisper-small-dv-full") model = AutoModelForSpeechSeq2Seq.from_pretrained("alakxender/whisper-small-dv-full") - Notebooks
- Google Colab
- Kaggle
File size: 1,434 Bytes
dbe4498 eccb10b dbe4498 eccb10b 472affb dbe4498 1ff5087 7f88851 1ff5087 dbe4498 eccb10b | 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 | ---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- Dhivehi
model-index:
- name: whisper-small-dv-full
results: []
language:
- dv
datasets:
- alakxender/dv-audio-syn-lg
---
<!-- 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-small-dv-full
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the arrow dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0273
- eval_wer_ortho: 15.8343
- eval_wer: 2.3726
- eval_runtime: 11624.8972
- eval_samples_per_second: 3.774
- eval_steps_per_second: 0.079
- epoch: 0.6569
- step: 4000
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 48
- total_eval_batch_size: 48
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |