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---
library_name: transformers
language:
- hu
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_17_0
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-hungarian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: MOZILLA-FOUNDATION/COMMON_VOICE_17_0 - HU
type: common_voice_17_0
config: hu
split: test
args: 'Config: hu, Training split: train+validation, Eval split: test'
metrics:
- name: Wer
type: wer
value: 0.1727824914378453
---
<!-- 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-xlsr-53-hungarian
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_17_0 - HU dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1748
- Wer: 0.2997
The training and measured wer values differ due to ignored characters.
## Model Comparison with the previous best wav2vec model (eval on CV17)
| Model name | WER | CER |
|:-----------------------------------------------:|:------------------:|:----------------:|
| jonatasgrosman/wav2vec2-large-xlsr-53-hungarian | 46.199835320230555 | 9.85170677112479 |
| sarpba/wav2vec2-large-xlsr-53-hungarian | 17.27824914378453 | 3.151354554132789 |
Igonore characters on eval:
```
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
```
## Intended uses & limitations
More information needed
## Train & Evaluation
Trained with transformers example pytorch script
Eval:
```
import torch
import librosa
import re
import warnings
from datasets import load_dataset
import evaluate
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "hu"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-hungarian"
DEVICE = "cuda"
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
test_dataset = load_dataset("mozilla-foundation/common_voice_17_0", LANG_ID, split="test")
wer = evaluate.load("wer") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
cer = evaluate.load("cer") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
model.to(DEVICE)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
predictions = [x.upper() for x in result["pred_strings"]]
references = [x.upper() for x in result["sentence"]]
print(f"WER: {wer.compute(predictions=predictions, references=references) * 100}")
print(f"CER: {cer.compute(predictions=predictions, references=references) * 100}")
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.7968 | 1.0 | 758 | 0.2848 | 0.5295 |
| 0.2547 | 2.0 | 1516 | 0.1908 | 0.4222 |
| 0.1929 | 3.0 | 2274 | 0.1753 | 0.4000 |
| 0.1532 | 4.0 | 3032 | 0.1558 | 0.3710 |
| 0.1297 | 5.0 | 3790 | 0.1512 | 0.3536 |
| 0.1167 | 6.0 | 4548 | 0.1574 | 0.3514 |
| 0.101 | 7.0 | 5306 | 0.1483 | 0.3374 |
| 0.0859 | 8.0 | 6064 | 0.1490 | 0.3299 |
| 0.0791 | 9.0 | 6822 | 0.1523 | 0.3250 |
| 0.0702 | 10.0 | 7580 | 0.1608 | 0.3192 |
| 0.0629 | 11.0 | 8338 | 0.1664 | 0.3146 |
| 0.0559 | 12.0 | 9096 | 0.1641 | 0.3103 |
| 0.0527 | 13.0 | 9854 | 0.1665 | 0.3063 |
| 0.0468 | 14.0 | 10612 | 0.1691 | 0.3011 |
| 0.0443 | 15.0 | 11370 | 0.1748 | 0.2998 |
### Framework versions
- Transformers 4.50.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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