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---
language:
- en
base_model: distil-small.en
tags:
- generated_from_trainer
datasets:
- librispeech_asr
metrics:
- wer
model-index:
- name: DistilFT-English-10m
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: librispeech
type: librispeech_asr
config: default
split: None
args: 'config: en, split: test-clean'
metrics:
- name: Wer
type: wer
value: 3.5814019853645607
---
<!-- 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. -->
# DistilFT-English-10m
This model is a fine-tuned version of [distil-small.en](https://huggingface.co/distil-small.en) on the librispeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5012
- Wer: 3.5814
## 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: 5e-07
- 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: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:--------:|:----:|:---------------:|:------:|
| 0.5641 | 33.3333 | 100 | 0.9641 | 3.4754 |
| 0.3271 | 66.6667 | 200 | 0.7822 | 3.4652 |
| 0.0871 | 100.0 | 300 | 0.5731 | 3.4530 |
| 0.0149 | 133.3333 | 400 | 0.5142 | 3.4774 |
| 0.0043 | 166.6667 | 500 | 0.5051 | 3.5345 |
| 0.0026 | 200.0 | 600 | 0.5030 | 3.5569 |
| 0.002 | 233.3333 | 700 | 0.5020 | 3.5671 |
| 0.0016 | 266.6667 | 800 | 0.5015 | 3.5773 |
| 0.0014 | 300.0 | 900 | 0.5013 | 3.5936 |
| 0.0014 | 333.3333 | 1000 | 0.5012 | 3.5814 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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