File size: 4,030 Bytes
4f1efb4 7bc4bab 4f1efb4 7bc4bab 4f1efb4 7bc4bab 4f1efb4 7bc4bab 4f1efb4 7bc4bab 4f1efb4 7bc4bab 4f1efb4 7bc4bab 4f1efb4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
---
language:
- hi
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_16_0
- mms
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: wav2vec2-common_voice-hi-mms-demo
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - HI
type: common_voice_16_0
config: hi
split: test
args: 'Config: hi, Training split: train+validation, Eval split: test'
metrics:
- name: Wer
type: wer
value: 0.2516432655283731
---
<!-- 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-hi-mms-demo
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2672
- Wer: 0.2516
## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.11 | 100 | 0.4487 | 0.3565 |
| No log | 0.23 | 200 | 0.3544 | 0.3317 |
| No log | 0.34 | 300 | 0.3693 | 0.3088 |
| No log | 0.45 | 400 | 0.3404 | 0.3040 |
| 1.5084 | 0.56 | 500 | 0.3346 | 0.2995 |
| 1.5084 | 0.68 | 600 | 0.3411 | 0.2936 |
| 1.5084 | 0.79 | 700 | 0.3175 | 0.2887 |
| 1.5084 | 0.9 | 800 | 0.3159 | 0.2898 |
| 1.5084 | 1.02 | 900 | 0.3139 | 0.3045 |
| 0.3485 | 1.13 | 1000 | 0.3067 | 0.2958 |
| 0.3485 | 1.24 | 1100 | 0.2969 | 0.2767 |
| 0.3485 | 1.35 | 1200 | 0.2916 | 0.2714 |
| 0.3485 | 1.47 | 1300 | 0.2893 | 0.2663 |
| 0.3485 | 1.58 | 1400 | 0.3183 | 0.2985 |
| 0.3152 | 1.69 | 1500 | 0.2961 | 0.2688 |
| 0.3152 | 1.81 | 1600 | 0.2848 | 0.2665 |
| 0.3152 | 1.92 | 1700 | 0.2844 | 0.2656 |
| 0.3152 | 2.03 | 1800 | 0.2855 | 0.2707 |
| 0.3152 | 2.14 | 1900 | 0.2887 | 0.2686 |
| 0.3058 | 2.26 | 2000 | 0.2858 | 0.2657 |
| 0.3058 | 2.37 | 2100 | 0.2814 | 0.2629 |
| 0.3058 | 2.48 | 2200 | 0.2809 | 0.2633 |
| 0.3058 | 2.6 | 2300 | 0.2779 | 0.2613 |
| 0.3058 | 2.71 | 2400 | 0.2745 | 0.2581 |
| 0.2861 | 2.82 | 2500 | 0.2769 | 0.2618 |
| 0.2861 | 2.93 | 2600 | 0.2742 | 0.2576 |
| 0.2861 | 3.05 | 2700 | 0.2730 | 0.2575 |
| 0.2861 | 3.16 | 2800 | 0.2727 | 0.2564 |
| 0.2861 | 3.27 | 2900 | 0.2726 | 0.2563 |
| 0.2839 | 3.39 | 3000 | 0.2713 | 0.2576 |
| 0.2839 | 3.5 | 3100 | 0.2690 | 0.2537 |
| 0.2839 | 3.61 | 3200 | 0.2706 | 0.2540 |
| 0.2839 | 3.72 | 3300 | 0.2687 | 0.2542 |
| 0.2839 | 3.84 | 3400 | 0.2671 | 0.2521 |
| 0.2706 | 3.95 | 3500 | 0.2673 | 0.2522 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|