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--- |
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language: nl |
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datasets: |
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- common_voice |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- phoneme-recognition |
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model-index: |
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- name: wav2vec2-base-960h-phoneme-reco-dutch |
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results: |
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- task: |
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name: Automatic Phoneme Recognition |
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type: automatic-phoneme-recognition |
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dataset: |
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name: CommonVoice (clean) |
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type: librispeech_asr |
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config: clean |
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split: test |
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args: |
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language: nl |
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metrics: |
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- name: Test PER |
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type: per |
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value: 20.83 |
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- name: Val PER |
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type: per |
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value: 16.18 |
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--- |
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# Model Description |
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The Wav2vec2 base model [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) fine tuned on phoneme recognition task for the dutch language. |
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# Usage |
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To transcribe in phonemes audio files the model can be used as a standalone acoustic model as follows: |
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```python |
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
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from datasets import load_dataset |
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import torch |
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# load model and tokenizer |
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processor = Wav2Vec2Processor.from_pretrained("Clementapa/wav2vec2-base-960h-phoneme-reco-dutch") |
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model = Wav2Vec2ForCTC.from_pretrained("Clementapa/wav2vec2-base-960h-phoneme-reco-dutch") |
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# load dummy dataset and read soundfiles |
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ds = load_dataset("common_voice", "nl", split="validation") |
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# tokenize |
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input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1 |
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# retrieve logits |
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logits = model(input_values).logits |
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# take argmax and decode |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(predicted_ids) |
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``` |
