---
language:
- en
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- QEC
metrics:
- wer
model-index:
- name: whisper-medium-quartr
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Quartr Earnings Calls
      type: QEC
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 22.31368880573745
---

<!-- 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-medium-quartr

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Quartr Earnings Calls dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6825
- Wer: 22.3137

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5817        | 0.32  | 100  | 0.5708          | 21.9832 |
| 0.5817        | 0.64  | 200  | 0.5332          | 20.1559 |
| 0.5253        | 0.96  | 300  | 0.5127          | 25.4256 |
| 0.3177        | 1.28  | 400  | 0.5276          | 28.5688 |
| 0.3603        | 1.61  | 500  | 0.5195          | 22.2950 |
| 0.3374        | 1.93  | 600  | 0.5101          | 24.3343 |
| 0.1734        | 2.25  | 700  | 0.5530          | 23.1743 |
| 0.2002        | 2.57  | 800  | 0.5525          | 21.1537 |
| 0.1894        | 2.89  | 900  | 0.5589          | 21.7774 |
| 0.0868        | 3.21  | 1000 | 0.6291          | 23.4487 |
| 0.0931        | 3.53  | 1100 | 0.6410          | 21.9208 |
| 0.1094        | 3.85  | 1200 | 0.6339          | 22.5008 |
| 0.1007        | 4.17  | 1300 | 0.6698          | 21.7524 |
| 0.0652        | 4.49  | 1400 | 0.6820          | 22.3262 |
| 0.0614        | 4.82  | 1500 | 0.6825          | 22.3137 |


### Framework versions

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