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## Usage |
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The model can be used directly (without a language model) as follows: |
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--- |
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language: |
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- ne |
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tags: |
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- speech-to-text |
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--- |
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```python |
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import soundfile as sf |
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import torch |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import argparse |
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def parse_transcription(wav_file): |
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# load pretrained model |
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processor = Wav2Vec2Processor.from_pretrained("shniranjan/wav2vec2-large-xlsr-300m-nepali") |
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model = Wav2Vec2ForCTC.from_pretrained("shniranjan/wav2vec2-large-xlsr-300m-nepali") |
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# load audio |
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audio_input, sample_rate = sf.read(wav_file) |
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# pad input values and return pt tensor |
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input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values |
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# INFERENCE |
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# retrieve logits & take argmax |
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logits = model(input_values).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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# transcribe |
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transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) |
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print(transcription) |
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``` |