---
language:
- en
tags:
- generated_from_trainer
base_model: openai/whisper-small
datasets:
- QEC
metrics:
- wer
model-index:
- name: whisper-small-quartr
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Quartr Earnings Calls
      type: QEC
      args: 'config: en, split: test'
    metrics:
    - type: wer
      value: 32.834424695977546
      name: Wer
---

<!-- 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-quartr

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Quartr Earnings Calls dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6429
- Wer: 32.8344

## 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: 8.120528078446462e-06
- train_batch_size: 8
- eval_batch_size: 8
- 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: cosine_with_restarts
- lr_scheduler_warmup_steps: 84
- training_steps: 1500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6771        | 0.32  | 100  | 0.6577          | 25.7437 |
| 0.6533        | 0.64  | 200  | 0.6025          | 34.6804 |
| 0.5793        | 0.96  | 300  | 0.5784          | 24.3530 |
| 0.3872        | 1.28  | 400  | 0.5856          | 32.9592 |
| 0.4447        | 1.61  | 500  | 0.5646          | 23.2429 |
| 0.414         | 1.93  | 600  | 0.5616          | 70.7016 |
| 0.2489        | 2.25  | 700  | 0.5816          | 35.2666 |
| 0.2863        | 2.57  | 800  | 0.5853          | 24.7583 |
| 0.2698        | 2.89  | 900  | 0.5844          | 32.7409 |
| 0.1646        | 3.21  | 1000 | 0.6182          | 27.7892 |
| 0.174         | 3.53  | 1100 | 0.6228          | 36.2021 |
| 0.2021        | 3.85  | 1200 | 0.6269          | 35.9775 |
| 0.2239        | 4.17  | 1300 | 0.6367          | 35.2666 |
| 0.1551        | 4.49  | 1400 | 0.6420          | 32.4478 |
| 0.1351        | 4.82  | 1500 | 0.6429          | 32.8344 |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.1.dev0
- Tokenizers 0.15.2