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--- |
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license: apache-2.0 |
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datasets: |
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- ASCEND |
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language: |
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- zh |
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metrics: |
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- cer |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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- xlsr-fine-tuning-week |
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--- |
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## inference |
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The model can be used directly (without a language model) as follows... |
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Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: |
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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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import torchaudio |
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# load model and processor |
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processor = Wav2Vec2Processor.from_pretrained("gymeee/demo_code_switching") |
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model = Wav2Vec2ForCTC.from_pretrained("gymeee/demo_code_switching") |
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# load speech |
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speech_array, sampling_rate = torchaudio.load("speech.wav") |
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# tokenize |
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input_values = processor(speech_array[0], 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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transcription |
