File size: 3,618 Bytes
b32a6a0 3143b45 b32a6a0 3143b45 |
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 |
---
library_name: transformers
license: apache-2.0
base_model: Samuael/geez-asr
tags:
- generated_from_trainer
datasets:
- alffa_amharic
metrics:
- wer
model-index:
- name: ethiopic-asr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: alffa_amharic
type: alffa_amharic
config: clean
split: None
args: clean
metrics:
- name: Wer
type: wer
value: 0.14692601597777005
---
<!-- 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. -->
# ethiopic-asr
This model is a fine-tuned version of [Samuael/geez-asr](https://huggingface.co/Samuael/geez-asr) on the alffa_amharic dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1301
- Wer: 0.1469
- Phoneme Cer: 0.0296
- Cer: 0.0416
## 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: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Phoneme Cer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:-----------:|:------:|
| No log | 0.0442 | 200 | 3.2216 | 1.0 | 1.0 | 1.0 |
| No log | 0.0883 | 400 | 3.1164 | 1.0 | 1.0 | 1.0 |
| 4.1769 | 0.1325 | 600 | 0.9628 | 0.5476 | 0.1141 | 0.1609 |
| 4.1769 | 0.1767 | 800 | 0.3181 | 0.2150 | 0.0430 | 0.0607 |
| 0.8455 | 0.2208 | 1000 | 0.2195 | 0.1759 | 0.0353 | 0.0503 |
| 0.8455 | 0.2650 | 1200 | 0.1913 | 0.1846 | 0.0365 | 0.0520 |
| 0.8455 | 0.3092 | 1400 | 0.1699 | 0.1591 | 0.0322 | 0.0454 |
| 0.2929 | 0.3534 | 1600 | 0.1603 | 0.1572 | 0.0316 | 0.0442 |
| 0.2929 | 0.3975 | 1800 | 0.1503 | 0.1567 | 0.0315 | 0.0442 |
| 0.2392 | 0.4417 | 2000 | 0.1476 | 0.1587 | 0.0318 | 0.0446 |
| 0.2392 | 0.4859 | 2200 | 0.1449 | 0.1565 | 0.0312 | 0.0438 |
| 0.2392 | 0.5300 | 2400 | 0.1409 | 0.1537 | 0.0308 | 0.0427 |
| 0.2166 | 0.5742 | 2600 | 0.1395 | 0.1551 | 0.0308 | 0.0428 |
| 0.2166 | 0.6184 | 2800 | 0.1345 | 0.1469 | 0.0290 | 0.0410 |
| 0.2068 | 0.6625 | 3000 | 0.1331 | 0.1509 | 0.0297 | 0.0419 |
| 0.2068 | 0.7067 | 3200 | 0.1346 | 0.1518 | 0.0301 | 0.0421 |
| 0.2068 | 0.7509 | 3400 | 0.1335 | 0.1507 | 0.0303 | 0.0426 |
| 0.2037 | 0.7951 | 3600 | 0.1312 | 0.1471 | 0.0297 | 0.0415 |
| 0.2037 | 0.8392 | 3800 | 0.1303 | 0.1438 | 0.0289 | 0.0406 |
| 0.1985 | 0.8834 | 4000 | 0.1300 | 0.1457 | 0.0292 | 0.0410 |
| 0.1985 | 0.9276 | 4200 | 0.1303 | 0.1471 | 0.0295 | 0.0414 |
| 0.1985 | 0.9717 | 4400 | 0.1301 | 0.1469 | 0.0296 | 0.0416 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|