File size: 3,928 Bytes
506fa99 c4e7470 ae64dce f0e97b6 ae64dce c4e7470 ae64dce 05d379e f0e97b6 a27570e f0e97b6 a27570e f0e97b6 5b11ee7 a27570e 5b11ee7 a27570e 5b11ee7 a27570e 5b11ee7 a27570e 5b11ee7 f0e97b6 506fa99 ae64dce 05d379e ae64dce b3bb914 ae64dce 05d379e ae64dce b3bb914 ae64dce f0e97b6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
---
base_model: facebook/w2v-bert-2.0
license: mit
datasets:
- thennal/IMaSC
- vrclc/festvox-iiith-ml
- vrclc/openslr63
- thennal/msc
- mozilla-foundation/common_voice_16_1
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: w2v2bert-Malayalam
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: OpenSLR Malayalam -Test
type: vrclc/openslr63
config: ml
split: test
args: ml
metrics:
- type: wer
value: 12.66
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Goole Fleurs
type: google/fleurs
config: ml
split: test
args: ml
metrics:
- type: wer
value: 40.58
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 16 Malayalam
type: mozilla-foundation/common_voice_16_1
config: ml
split: test
args: ml
metrics:
- type: wer
value: 52.10
name: WER
---
<!-- 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. -->
# w2v-bert-2.0-nonstudio_and_studioRecords
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an these datasets: [IMASC](https://huggingface.co/datasets/thennal/IMaSC), [MSC](https://huggingface.co/datasets/smcproject/MSC), [OpenSLR Malayalam Train split](https://huggingface.co/datasets/vrclc/openslr63), [Festvox Malayalam](https://huggingface.co/datasets/vrclc/openslr63), [common_voice_16_1](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1)
It achieves the following results on the evaluation set:
- Loss: 0.1722
- Wer: 0.1299
## Training procedure
Trained on NVIDIA A100 GPU
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1416 | 0.46 | 600 | 0.3393 | 0.4616 |
| 0.1734 | 0.92 | 1200 | 0.2414 | 0.3493 |
| 0.1254 | 1.38 | 1800 | 0.2205 | 0.2963 |
| 0.1097 | 1.84 | 2400 | 0.2157 | 0.3133 |
| 0.0923 | 2.3 | 3000 | 0.1854 | 0.2473 |
| 0.0792 | 2.76 | 3600 | 0.1939 | 0.2471 |
| 0.0696 | 3.22 | 4200 | 0.1720 | 0.2282 |
| 0.0589 | 3.68 | 4800 | 0.1768 | 0.2013 |
| 0.0552 | 4.14 | 5400 | 0.1635 | 0.1864 |
| 0.0437 | 4.6 | 6000 | 0.1501 | 0.1826 |
| 0.0408 | 5.06 | 6600 | 0.1500 | 0.1645 |
| 0.0314 | 5.52 | 7200 | 0.1559 | 0.1655 |
| 0.0317 | 5.98 | 7800 | 0.1448 | 0.1553 |
| 0.022 | 6.44 | 8400 | 0.1592 | 0.1590 |
| 0.0218 | 6.9 | 9000 | 0.1431 | 0.1458 |
| 0.0154 | 7.36 | 9600 | 0.1514 | 0.1366 |
| 0.0141 | 7.82 | 10200 | 0.1540 | 0.1383 |
| 0.0113 | 8.28 | 10800 | 0.1558 | 0.1391 |
| 0.0085 | 8.74 | 11400 | 0.1612 | 0.1356 |
| 0.0072 | 9.2 | 12000 | 0.1697 | 0.1289 |
| 0.0046 | 9.66 | 12600 | 0.1722 | 0.1299 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 |