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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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datasets: |
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- techiaith/commonvoice_16_1_en_cy |
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- techiaith/banc-trawsgrifiadau-bangor |
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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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# wav2vec2-xlsr-53-ft-cy-en-withlm |
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An acoustic encoder model for Welsh and English speech recognition accompanied with a n-gram language model. |
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The acoustic model is 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) and |
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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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The accompanying language model is a single KenLM n-gram model trained with a balanced |
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collection of Welsh and English texts from [OSCAR](https://huggingface.co/datasets/oscar), thus avoiding language specific models |
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and language detection during CTC decoding. |
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## Usage |
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The `wav2vec2-xlsr-53-ft-cy-en-withlm` 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, Wav2Vec2ProcessorWithLM |
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processor = Wav2Vec2ProcessorWithLM.from_pretrained("techiaith/wav2vec2-xlsr-53-ft-cy-en-withlm") |
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model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-53-ft-cy-en-withlm") |
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audio, rate = librosa.load(<path/to/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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print("Prediction: ", processor.batch_decode(tlogits.numpy(), beam_width=10).text[0].strip()) |
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``` |
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Usage with a pipeline is even simpler... |
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``` |
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from transformers import pipeline |
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transcriber = pipeline("automatic-speech-recognition", model="techiaith/wav2vec2-xlsr-53-ft-cy-en-withlm") |
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def transcribe(audio): |
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return transcriber(audio)["text"] |
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transcribe(<path/or/url/to/any/audiofile>) |
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``` |
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