| ## Usage | |
| The model can be used directly (without a language model) as follows: | |
| ```python | |
| import soundfile as sf | |
| import torch | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| import argparse | |
| def parse_transcription(wav_file): | |
| # load pretrained model | |
| processor = Wav2Vec2Processor.from_pretrained("addy88/hindi-wav2vec2-stt") | |
| model = Wav2Vec2ForCTC.from_pretrained("addy88/hindi-wav2vec2-stt") | |
| # load audio | |
| audio_input, sample_rate = sf.read(wav_file) | |
| # pad input values and return pt tensor | |
| input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values | |
| # INFERENCE | |
| # retrieve logits & take argmax | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| # transcribe | |
| transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) | |
| print(transcription) | |
| ``` |