---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-tr-cv16.1
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: tr
      split: test
      args: tr
    metrics:
    - name: Wer
      type: wer
      value: 0.41599252148275984
---

<!-- 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-xls-r-300m-tr-cv16.1

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3356
- Wer: 0.4160

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP

## Model Inference
```python
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

model = Wav2Vec2ForCTC.from_pretrained("rumeyskeskn/wav2vec2-large-xls-r-300m-tr-cv16.1").to("cpu")
processor = Wav2Vec2Processor.from_pretrained("rumeyskeskn/wav2vec2-large-xls-r-300m-tr-cv16.1")
audio_path = "audio.wav"

audio_array, sampling_rate = librosa.load(audio_path, sr=16000)

input_values = processor(audio_array, sampling_rate=sampling_rate).input_values[0]

input_dict = processor(input_values, return_tensors="pt", padding=True)


logits = model(input_dict.input_values).logits

pred_ids = torch.argmax(logits, dim=-1)
prediction = processor.decode(pred_ids[0])

print("Prediction:")
print(prediction)
```

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.669         | 0.39  | 400  | 1.2228          | 0.8840 |
| 0.6809        | 0.78  | 800  | 0.6371          | 0.6557 |
| 0.4224        | 1.17  | 1200 | 0.4607          | 0.5226 |
| 0.3151        | 1.56  | 1600 | 0.3671          | 0.4457 |
| 0.2633        | 1.95  | 2000 | 0.3356          | 0.4160 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2