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--- |
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license: mit |
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--- |
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The model was fine-tuned on 300h of public and private speech data. More information will be given once the underlying paper gets published. |
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``` |
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import librosa |
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from transformers import Wav2Vec2Processor, AutoModelForCTC |
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import torch |
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audio, _ = librosa.load("[audio_path]", sr=16000) |
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model = AutoModelForCTC.from_pretrained("racai/wav2vec2-base-100k-voxpopuli-romanian") |
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processor = Wav2Vec2Processor.from_pretrained("racai/wav2vec2-base-100k-voxpopuli-romanian") |
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input_dict = processor(audio, sampling_rate=16000, return_tensors="pt") |
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with torch.inference_mode(): |
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logits = model(input_dict.input_values).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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predicted_sentence = processor.batch_decode(predicted_ids)[0] |
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print("Prediction:", predicted_sentence) |
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``` |
