---
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: 0.29972911371257416
---

<!-- 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

## 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 | | |

## Intended uses & limitations

More information needed

## Train & Evaluation

Trained with transformers examply 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