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--- |
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language: |
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- cy |
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- en |
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datasets: |
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- common_voice |
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metrics: |
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- wer |
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tags: |
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- automatic-speech-recognition |
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- speech |
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license: apache-2.0 |
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model-index: |
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- name: wav2vec2-xlsr-ft-en-cy |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice cy |
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type: common_voice |
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args: cy |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 17.70% |
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--- |
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# wav2vec2-xlsr-ft-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-ft-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-ft-en-cy") |
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model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-ft-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 11, the WER of techiaith/wav2vec2-xlsr-ft-en-cy is **17.7%** |
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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 | 17.07| 7.32 | |
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| EN | 27.54 | 11.6 | |
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| CY | 7.13 | 2.2 | |
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