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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 ced_model.feature_extraction_ced import CedFeatureExtractor |
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from ced_model.modeling_ced import CedForAudioClassification |
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model_path = "mispeech/ced-base" |
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feature_extractor = CedFeatureExtractor.from_pretrained(model_path) |
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model = CedForAudioClassification.from_pretrained(model_path) |
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def process(audio_path: str) -> str: |
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if audio_path is None: |
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return "No audio file uploaded." |
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global model |
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global label_maps |
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audio, sr = torchaudio.load(audio_path) |
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if sr != 16000: |
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return "Models are trained on 16khz, please sample your input to 16khz mono." |
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inputs = feature_extractor(audio, sampling_rate=sr, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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predicted_class_ids = torch.argmax(logits, dim=-1).item() |
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predicted_label = model.config.id2label[predicted_class_ids] |
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return predicted_label |
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iface_audio_file = gr.Interface( |
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fn=process, |
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inputs=gr.Audio(sources="upload", type="filepath", streaming=False), |
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outputs="text", |
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) |
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gr.close_all() |
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iface_audio_file.launch() |
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