File size: 4,574 Bytes
27d0c57 a495fd7 27d0c57 bc91ec9 27d0c57 1830e0f 974dfd3 1830e0f 488c40e 974dfd3 488c40e 974dfd3 488c40e 974dfd3 434e938 488c40e 79f3110 6ab3aac 79f3110 1c82004 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
---
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- sv
- robust-speech-event
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- 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. -->
# XLS-R-300m-SV
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3171
- Wer: 0.2730
## 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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.3349 | 1.45 | 500 | 3.2858 | 1.0 |
| 2.9298 | 2.91 | 1000 | 2.9225 | 1.0000 |
| 2.0839 | 4.36 | 1500 | 1.1546 | 0.8295 |
| 1.7093 | 5.81 | 2000 | 0.6827 | 0.5701 |
| 1.5855 | 7.27 | 2500 | 0.5597 | 0.4947 |
| 1.4831 | 8.72 | 3000 | 0.4923 | 0.4527 |
| 1.4416 | 10.17 | 3500 | 0.4670 | 0.4270 |
| 1.3848 | 11.63 | 4000 | 0.4341 | 0.3980 |
| 1.3749 | 13.08 | 4500 | 0.4203 | 0.4011 |
| 1.3311 | 14.53 | 5000 | 0.4310 | 0.3961 |
| 1.317 | 15.99 | 5500 | 0.3898 | 0.4322 |
| 1.2799 | 17.44 | 6000 | 0.3806 | 0.3572 |
| 1.2771 | 18.89 | 6500 | 0.3828 | 0.3427 |
| 1.2451 | 20.35 | 7000 | 0.3702 | 0.3359 |
| 1.2182 | 21.8 | 7500 | 0.3685 | 0.3270 |
| 1.2152 | 23.26 | 8000 | 0.3650 | 0.3308 |
| 1.1837 | 24.71 | 8500 | 0.3568 | 0.3187 |
| 1.1721 | 26.16 | 9000 | 0.3659 | 0.3249 |
| 1.1764 | 27.61 | 9500 | 0.3547 | 0.3145 |
| 1.1606 | 29.07 | 10000 | 0.3514 | 0.3104 |
| 1.1431 | 30.52 | 10500 | 0.3469 | 0.3062 |
| 1.1047 | 31.97 | 11000 | 0.3313 | 0.2979 |
| 1.1315 | 33.43 | 11500 | 0.3298 | 0.2992 |
| 1.1022 | 34.88 | 12000 | 0.3296 | 0.2973 |
| 1.0935 | 36.34 | 12500 | 0.3278 | 0.2926 |
| 1.0676 | 37.79 | 13000 | 0.3208 | 0.2868 |
| 1.0571 | 39.24 | 13500 | 0.3322 | 0.2885 |
| 1.0536 | 40.7 | 14000 | 0.3245 | 0.2831 |
| 1.0525 | 42.15 | 14500 | 0.3285 | 0.2826 |
| 1.0464 | 43.6 | 15000 | 0.3223 | 0.2796 |
| 1.0415 | 45.06 | 15500 | 0.3166 | 0.2774 |
| 1.0356 | 46.51 | 16000 | 0.3177 | 0.2746 |
| 1.04 | 47.96 | 16500 | 0.3150 | 0.2735 |
| 1.0209 | 49.42 | 17000 | 0.3175 | 0.2731 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "hf-test/xls-r-300m-sv"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "sv-SE", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "jag lämnade grovjobbet åt honom"
```
### Eval results on Common Voice 7 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 27.30 | 18.85 |
|