---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- dataset_whisper
metrics:
- wer
model-index:
- name: Transcriber-Medium
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: dataset_whisper
      type: dataset_whisper
      config: default
      split: test
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 108.52032520325203
---

<!-- 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. -->

# Transcriber-Medium

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the dataset_whisper dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9360
- Wer: 108.5203

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7536        | 4.02  | 100  | 2.9360          | 108.5203 |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.14.1
- Tokenizers 0.13.3