tensorboard safetensors wav2vec2 generated_from_trainer dataset:common_voice_17_0 base_model:facebook/mms-1b-all base_model:finetune:facebook/mms-1b-all license:cc-by-nc-4.0 model-index region:us
---
base_model: facebook/mms-1b-all
datasets:
- common_voice_17_0
license: cc-by-nc-4.0
metrics:
- wer
- bleu
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-mms-1b-malayalam-colab-CV17.0
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: ml
      split: test
      args: ml
    metrics:
    - type: wer
      value: 1.0315925209542232
      name: Wer
    - type: bleu
      value:
        bleu: 0.0
        precisions:
        - 0.0008639308855291577
        - 0.0
        - 0.0
        - 0.0
        brevity_penalty: 0.7118010694449419
        length_ratio: 0.7462927143778207
        translation_length: 2315
        reference_length: 3102
      name: Bleu
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-mms-1b-malayalam-colab-CV17.0

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7578
- Wer: 1.0316
- Cer: 0.7469
- Bleu: {'bleu': 0.0, 'precisions': [0.0008639308855291577, 0.0, 0.0, 0.0], 'brevity_penalty': 0.7118010694449419, 'length_ratio': 0.7462927143778207, 'translation_length': 2315, 'reference_length': 3102}

## 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: 3e-05
- 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_ratio: 0.15
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    | Bleu                                                                                                                                                                                                  |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 21.8941       | 3.1496  | 200  | 14.1370         | 1.0    | 1.0292 | {'bleu': 0.0, 'precisions': [0.0, 0.0, 0.0, 0.0], 'brevity_penalty': 0.08102287291060646, 'length_ratio': 0.2846550612508059, 'translation_length': 883, 'reference_length': 3102}                    |
| 8.5677        | 6.2992  | 400  | 6.3226          | 1.0071 | 0.8568 | {'bleu': 0.0, 'precisions': [0.0, 0.0, 0.0, 0.0], 'brevity_penalty': 0.2500351312096836, 'length_ratio': 0.4190844616376531, 'translation_length': 1300, 'reference_length': 3102}                    |
| 6.6037        | 9.4488  | 600  | 5.6520          | 1.0351 | 0.7789 | {'bleu': 0.0, 'precisions': [0.0, 0.0, 0.0, 0.0], 'brevity_penalty': 0.6082895680797644, 'length_ratio': 0.6679561573178594, 'translation_length': 2072, 'reference_length': 3102}                    |
| 6.0799        | 12.5984 | 800  | 5.2294          | 1.0574 | 0.7594 | {'bleu': 0.0, 'precisions': [0.00041631973355537054, 0.0, 0.0, 0.0], 'brevity_penalty': 0.7471989379147929, 'length_ratio': 0.7743391360412637, 'translation_length': 2402, 'reference_length': 3102} |
| 5.7804        | 15.7480 | 1000 | 5.1733          | 1.0329 | 0.7629 | {'bleu': 0.0, 'precisions': [0.0, 0.0, 0.0, 0.0], 'brevity_penalty': 0.6734028056038747, 'length_ratio': 0.7166344294003868, 'translation_length': 2223, 'reference_length': 3102}                    |
| 5.4453        | 18.8976 | 1200 | 4.9821          | 1.0525 | 0.7467 | {'bleu': 0.0, 'precisions': [0.0004042037186742118, 0.0, 0.0, 0.0], 'brevity_penalty': 0.7758159728382187, 'length_ratio': 0.7975499677627337, 'translation_length': 2474, 'reference_length': 3102}  |
| 5.2983        | 22.0472 | 1400 | 4.8784          | 1.0297 | 0.7521 | {'bleu': 0.0, 'precisions': [0.0008806693086745927, 0.0, 0.0, 0.0], 'brevity_penalty': 0.693559602972643, 'length_ratio': 0.7321083172147002, 'translation_length': 2271, 'reference_length': 3102}   |
| 5.0862        | 25.1969 | 1600 | 4.7948          | 1.0358 | 0.7446 | {'bleu': 0.0, 'precisions': [0.0008399832003359933, 0.0, 0.0, 0.0], 'brevity_penalty': 0.7387365301547462, 'length_ratio': 0.7675693101225016, 'translation_length': 2381, 'reference_length': 3102}  |
| 4.9884        | 28.3465 | 1800 | 4.7578          | 1.0316 | 0.7479 | {'bleu': 0.0, 'precisions': [0.000864304235090752, 0.0, 0.0, 0.0], 'brevity_penalty': 0.711389009553914, 'length_ratio': 0.7459703417150225, 'translation_length': 2314, 'reference_length': 3102}    |
| 5.0447        | 31.4961 | 2000 | 4.7578          | 1.0316 | 0.7469 | {'bleu': 0.0, 'precisions': [0.0008639308855291577, 0.0, 0.0, 0.0], 'brevity_penalty': 0.7118010694449419, 'length_ratio': 0.7462927143778207, 'translation_length': 2315, 'reference_length': 3102}  |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
