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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-xlsr-53-ft-ccv-en-cy |
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results: [] |
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datasets: |
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- techiaith/commonvoice_16_1_en_cy |
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language: |
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- cy |
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- en |
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pipeline_tag: automatic-speech-recognition |
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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-xlsr-53-ft-ccv-en-cy |
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A speech recognition acoustic model for Welsh and English, fine-tuned from [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) using English/Welsh balanced data derived from version 11 of their respective Common Voice datasets (https://commonvoice.mozilla.org/cy/datasets). Custom bilingual Common Voice train/dev and test splits were built using the scripts at https://github.com/techiaith/docker-commonvoice-custom-splits-builder#introduction |
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Source code and scripts for training wav2vec2-xlsr-ft-en-cy can be found at [https://github.com/techiaith/docker-wav2vec2-cy](https://github.com/techiaith/docker-wav2vec2-cy/blob/main/train/fine-tune/python/run_en_cy.sh). |
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## Usage |
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The wav2vec2-xlsr-53-ft-ccv-en-cy model can be used directly as follows: |
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```python |
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import torch |
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import torchaudio |
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import librosa |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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processor = Wav2Vec2Processor.from_pretrained("techiaith/wav2vec2-xlsr-53-ft-ccv-en-cy") |
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model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-53-ft-ccv-en-cy") |
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audio, rate = librosa.load(audio_file, sr=16000) |
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inputs = processor(audio, sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
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# greedy decoding |
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predicted_ids = torch.argmax(logits, dim=-1) |
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print("Prediction:", processor.batch_decode(predicted_ids)) |
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``` |
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## Evaluation |
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According to a balanced English+Welsh test set derived from Common Voice version 16.1, the WER of techiaith/wav2vec2-xlsr-53-ft-ccv-en-cy is **23.79%** |
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However, when evaluated with language specific test sets, the model exhibits a bias to perform better with Welsh. |
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| Common Voice Test Set Language | WER | CER | |
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| -------- | --- | --- | |
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| EN+CY | 23.79| 9.68 | |
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| EN | 34.47 | 14.83 | |
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| CY | 12.34 | 3.55 | |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 800 |
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- training_steps: 9000 |
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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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| 6.0574 | 0.25 | 500 | 2.0297 | 0.9991 | |
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| 1.224 | 0.5 | 1000 | 0.5368 | 0.4342 | |
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| 0.434 | 0.75 | 1500 | 0.4861 | 0.3891 | |
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| 0.3295 | 1.01 | 2000 | 0.4301 | 0.3411 | |
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| 0.2739 | 1.26 | 2500 | 0.3818 | 0.3053 | |
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| 0.2619 | 1.51 | 3000 | 0.3894 | 0.3060 | |
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| 0.2517 | 1.76 | 3500 | 0.3497 | 0.2802 | |
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| 0.2244 | 2.01 | 4000 | 0.3519 | 0.2792 | |
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| 0.1854 | 2.26 | 4500 | 0.3376 | 0.2718 | |
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| 0.1779 | 2.51 | 5000 | 0.3206 | 0.2520 | |
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| 0.1749 | 2.77 | 5500 | 0.3169 | 0.2535 | |
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| 0.1636 | 3.02 | 6000 | 0.3122 | 0.2465 | |
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| 0.137 | 3.27 | 6500 | 0.3054 | 0.2382 | |
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| 0.1311 | 3.52 | 7000 | 0.2956 | 0.2280 | |
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| 0.1261 | 3.77 | 7500 | 0.2898 | 0.2236 | |
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| 0.1187 | 4.02 | 8000 | 0.2847 | 0.2176 | |
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| 0.1011 | 4.27 | 8500 | 0.2763 | 0.2124 | |
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| 0.0981 | 4.52 | 9000 | 0.2754 | 0.2115 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
