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