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import gradio as gr |
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import torch |
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import torchaudio |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import librosa |
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import numpy as np |
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import re |
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rocessor = Wav2Vec2Processor.from_pretrained("adilism/wav2vec2-large-xlsr-kyrgyz") |
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model = Wav2Vec2ForCTC.from_pretrained("adilism/wav2vec2-large-xlsr-kyrgyz") |
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def transcribe(file_): |
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arr_audio, _ librosa.load(file, rate=16000) |
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inputs = processor(arr_audio, sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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text = processor.batch_decode(pred_ids) |
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return text |
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iface = gr.Interface( |
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fn=transcribe, |
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inputs=gr.Audio(type="filepath"), |
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outputs="text", |
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title="Kyrgyz-STT-Small", |
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description="Realtime demo for Kyrgyz speech recognition using a fine-tuned Whisper small model.", |
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) |
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iface.launch() |
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