|
--- |
|
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 |
|
|
