---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-large
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- alexandrainst/ftspeech
metrics:
- wer
model-index:
- name: Whisper Large FTSpeech - Your Name
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ftspeech
      type: alexandrainst/ftspeech
      args: 'split: test'
    metrics:
    - name: Wer
      type: wer
      value: 55.483870967741936
---

<!-- 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 Large FTSpeech - Your Name

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the ftspeech dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8143
- Wer: 55.4839

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0           | 1000.0 | 1000 | 1.2419          | 47.0968 |
| 0.0           | 2000.0 | 2000 | 1.5725          | 50.9677 |
| 0.0           | 3000.0 | 3000 | 1.7241          | 54.8387 |
| 0.0           | 4000.0 | 4000 | 1.8143          | 55.4839 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.21.0