---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- f1
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-assamese_speech_to_IPA_with_wer_cer_f1
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: as
      split: test
      args: as
    metrics:
    - name: F1
      type: f1
      value: 0.032467532467532464
    - name: Wer
      type: wer
      value: 0.9675324675324676
---

<!-- 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-assamese_speech_to_IPA_with_wer_cer_f1

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_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8170
- Exact Match: 0.0325
- F1: 0.0325
- Wer: 0.9675
- Cer: 0.1450

## 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: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Exact Match | F1     | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:-----------:|:------:|:------:|:------:|
| 4.6801        | 9.8765  | 400  | 0.8184          | 0.0032      | 0.0032 | 0.9968 | 0.2199 |
| 0.2678        | 19.7531 | 800  | 0.7753          | 0.0227      | 0.0227 | 0.9773 | 0.1628 |
| 0.1009        | 29.6296 | 1200 | 0.8270          | 0.0292      | 0.0292 | 0.9708 | 0.1504 |
| 0.0619        | 39.5062 | 1600 | 0.8170          | 0.0325      | 0.0325 | 0.9675 | 0.1450 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1