transformers tensorboard safetensors wav2vec2-bert automatic-speech-recognition mozilla-foundation/common_voice_16_0 generated_from_trainer tr dataset:common_voice_16_0 base_model:ylacombe/w2v-bert-2.0 model-index endpoints_compatible region:us
---
language:
- tr
base_model: ylacombe/w2v-bert-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_16_0
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: wav2vec2-common_voice-tr-demo
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - TR
      type: common_voice_16_0
      config: tr
      split: test
      args: 'Config: tr, Training split: train+validation, Eval split: test'
    metrics:
    - name: Wer
      type: wer
      value: 1.0
---

<!-- 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-common_voice-tr-demo

This model is a fine-tuned version of [ylacombe/w2v-bert-2.0](https://huggingface.co/ylacombe/w2v-bert-2.0) on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0

## 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: 0.007448827845832091
- train_batch_size: 20
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log        | 0.27  | 300   | 3.2930          | 1.0    |
| 5.6462        | 0.55  | 600   | 3.4159          | 1.0    |
| 5.6462        | 0.82  | 900   | 3.4422          | 1.0    |
| 3.3522        | 1.1   | 1200  | 3.3719          | 1.0    |
| 3.2605        | 1.37  | 1500  | 3.4026          | 1.0    |
| 3.2605        | 1.64  | 1800  | 3.4448          | 1.0    |
| 3.2766        | 1.92  | 2100  | 3.4736          | 0.9999 |
| 3.2766        | 2.19  | 2400  | 3.9828          | 1.0    |
| 3.2853        | 2.47  | 2700  | 3.5532          | 1.0    |
| 3.3389        | 2.74  | 3000  | 3.7819          | 1.0    |
| 3.3389        | 3.01  | 3300  | 3.2250          | 1.0    |
| 3.2186        | 3.29  | 3600  | 3.2373          | 1.0    |
| 3.2186        | 3.56  | 3900  | 3.2162          | 1.0    |
| 3.1916        | 3.84  | 4200  | 3.2368          | 1.0    |
| 3.2188        | 4.11  | 4500  | 3.2377          | 1.0    |
| 3.2188        | 4.38  | 4800  | 3.4207          | 1.0    |
| 5.3067        | 4.66  | 5100  | nan             | 1.0    |
| 5.3067        | 4.93  | 5400  | nan             | 1.0    |
| 0.0           | 5.21  | 5700  | nan             | 1.0    |
| 0.0           | 5.48  | 6000  | nan             | 1.0    |
| 0.0           | 5.75  | 6300  | nan             | 1.0    |
| 0.0           | 6.03  | 6600  | nan             | 1.0    |
| 0.0           | 6.3   | 6900  | nan             | 1.0    |
| 0.0           | 6.58  | 7200  | nan             | 1.0    |
| 0.0           | 6.85  | 7500  | nan             | 1.0    |
| 0.0           | 7.12  | 7800  | nan             | 1.0    |
| 0.0           | 7.4   | 8100  | nan             | 1.0    |
| 0.0           | 7.67  | 8400  | nan             | 1.0    |
| 0.0           | 7.95  | 8700  | nan             | 1.0    |
| 0.0           | 8.22  | 9000  | nan             | 1.0    |
| 0.0           | 8.49  | 9300  | nan             | 1.0    |
| 0.0           | 8.77  | 9600  | nan             | 1.0    |
| 0.0           | 9.04  | 9900  | nan             | 1.0    |
| 0.0           | 9.32  | 10200 | nan             | 1.0    |
| 0.0           | 9.59  | 10500 | nan             | 1.0    |
| 0.0           | 9.86  | 10800 | nan             | 1.0    |
| 0.0           | 10.14 | 11100 | nan             | 1.0    |
| 0.0           | 10.41 | 11400 | nan             | 1.0    |
| 0.0           | 10.68 | 11700 | nan             | 1.0    |
| 0.0           | 10.96 | 12000 | nan             | 1.0    |
| 0.0           | 11.23 | 12300 | nan             | 1.0    |
| 0.0           | 11.51 | 12600 | nan             | 1.0    |
| 0.0           | 11.78 | 12900 | nan             | 1.0    |
| 0.0           | 12.05 | 13200 | nan             | 1.0    |
| 0.0           | 12.33 | 13500 | nan             | 1.0    |
| 0.0           | 12.6  | 13800 | nan             | 1.0    |
| 0.0           | 12.88 | 14100 | nan             | 1.0    |
| 0.0           | 13.15 | 14400 | nan             | 1.0    |
| 0.0           | 13.42 | 14700 | nan             | 1.0    |
| 0.0           | 13.7  | 15000 | nan             | 1.0    |
| 0.0           | 13.97 | 15300 | nan             | 1.0    |
| 0.0           | 14.25 | 15600 | nan             | 1.0    |
| 0.0           | 14.52 | 15900 | nan             | 1.0    |
| 0.0           | 14.79 | 16200 | nan             | 1.0    |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.15.0
