---
license: apache-2.0
base_model: facebook/wav2vec2-large-lv60
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60_phoneme-timit_english_timit-4k_002
  results: []
datasets:
- timit-asr/timit_asr
language:
- en
metrics:
- wer
library_name: transformers
pipeline_tag: automatic-speech-recognition
---

<!-- 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. -->

# wav2vec2-large-lv60_phoneme-timit_english_timit-4k_002

This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the [TIMIT dataset](https://huggingface.co/datasets/timit-asr/timit_asr).
It achieves the following results on the evaluation set:
- Loss: 0.3354
- PER: 0.1053
- So far the highest peforming model among my models

## Intended uses & limitations

- Phoneme recognition based on the TIMIT phoneme set

## Phoneme-wise errors

### Vowel Phonemes

![Vowel confusion matrix](img/vowel_confusion_matrix.png)

### Stop Phonemes

![Stop_consonant confusion matrix](img/stop_confusion_matrix.png)

### Affricate Phonemes

![Affricate_consonant confusion matrix](img/affricate_confusion_matrix.png)

### Fricative Phonemes

![Fricative_consonant confusion matrix](img/fricative_confusion_matrix.png)

### Nasal Phonemes

![Nasal_consonant confusion matrix](img/nasal_confusion_matrix.png)

### Semivowels/Glide Phonemes

![Vowel confusion matrix](img/semivowel_glide_confusion_matrix.png)

## Training and evaluation data

- Train: TIMIT train dataset (4620 samples)
- Test: TIMIT test dataset (1680 samples)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | PER    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 7.9352        | 1.04  | 300  | 3.7710          | 0.9617 |
| 2.7874        | 2.08  | 600  | 0.9080          | 0.1929 |
| 0.8205        | 3.11  | 900  | 0.4670          | 0.1492 |
| 0.5504        | 4.15  | 1200 | 0.4025          | 0.1408 |
| 0.4632        | 5.19  | 1500 | 0.3696          | 0.1374 |
| 0.4148        | 6.23  | 1800 | 0.3519          | 0.1343 |
| 0.3873        | 7.27  | 2100 | 0.3419          | 0.1329 |
| 0.3695        | 8.3   | 2400 | 0.3368          | 0.1317 |
| 0.3531        | 9.34  | 2700 | 0.3406          | 0.1320 |
| 0.3507        | 10.38 | 3000 | 0.3354          | 0.1315 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.2