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import gradio as gr |
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from huggingface_hub import snapshot_download |
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
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model_id = "wbbbbb/wav2vec2-large-chinese-zh-cn" |
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model = snapshot_download(repo_id=model_id, cache_dir='cache') |
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processor = Wav2Vec2Processor.from_pretrained(model_id) |
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def transcribe(audio): |
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inputs = processor(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).logits |
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prediction = processor.batch_decode(torch.argmax(logits, dim=-1)) |
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return prediction[0] |
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iface = gr.Interface(fn=transcribe, inputs="audio", outputs="text") |
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iface.launch() |
