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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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- techiaith/banc-trawsgrifiadau-bangor |
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- techiaith/commonvoice_16_1_en_cy |
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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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pipeline_tag: automatic-speech-recognition |
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--- |
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# wav2vec2-xlsr-ft-cy-en |
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An acoustic encoder model for Welsh and English speech recognition, fine-tuned from |
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[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) using transcribed |
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spontaneous speech from |
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[techiaith/banc-trawsgrifiadau-bangor (v24.01)](https://huggingface.co/datasets/techiaith/banc-trawsgrifiadau-bangor/tree/24.01) |
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as well as Welsh and English speech data derived from version 16.1 the Common Voice datasets [techiaith/commonvoice_16_1_en_cy](https://huggingface.co/datasets/techiaith/commonvoice_16_1_en_cy) |
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## Usage |
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The wav2vec2-xlsr-ft-cy-en 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-cy-en") |
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model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-ft-cy-en") |
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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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