File size: 3,780 Bytes
389e2c1 e88c671 7324e16 389e2c1 99485d2 e88c671 389e2c1 99485d2 8aaedc8 389e2c1 99485d2 e88c671 99485d2 e88c671 8aaedc8 e88c671 99485d2 e88c671 8aaedc8 7cf43f8 e88c671 7cf43f8 8aaedc8 e88c671 8aaedc8 e88c671 8aaedc8 389e2c1 92d0074 72cac42 99485d2 389e2c1 92d0074 d092371 389e2c1 92d0074 d092371 389e2c1 92d0074 d092371 92d0074 389e2c1 |
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 |
---
language:
- sv
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
base_model: facebook/wav2vec2-xls-r-1b
model-index:
- name: wav2vec2-large-xls-r-1b-Swedish
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: Common Voice sv-SE
type: mozilla-foundation/common_voice_8_0
args: sv-SE
metrics:
- type: wer
value: 14.04
name: Test WER With LM
- type: cer
value: 4.86
name: Test CER With LM
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- type: wer
value: 29.69
name: Test WER
- type: cer
value: 12.59
name: Test CER
---
<!-- 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-large-xls-r-1b-Swedish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset.
It achieves the following results on the evaluation set:
**Without LM**
- Loss: 0.3370
- Wer: 18.44
- Cer: 5.75
**With LM**
- Loss: 0.3370
- Wer: 14.04
- Cer: 4.86
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Swedish --dataset mozilla-foundation/common_voice_8_0 --config sv-SE --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Swedish --dataset speech-recognition-community-v2/dev_data --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "kingabzpro/wav2vec2-large-xls-r-1b-Swedish"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_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
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.1562 | 11.11 | 500 | 0.4830 | 0.3729 | 0.1169 |
| 0.5655 | 22.22 | 1000 | 0.3553 | 0.2381 | 0.0743 |
| 0.3376 | 33.33 | 1500 | 0.3359 | 0.2179 | 0.0696 |
| 0.2419 | 44.44 | 2000 | 0.3232 | 0.1844 | 0.0575 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|