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import gradio as gr |
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from transformers import Wav2Vec2Processor |
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import torch |
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import librosa |
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import numpy as np |
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from huggingface_hub import hf_hub_download |
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class Wav2Vec2Classifier(torch.nn.Module): |
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def __init__(self, num_classes): |
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super().__init__() |
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from transformers import Wav2Vec2Model |
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self.wav2vec2 = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base") |
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self.dropout = torch.nn.Dropout(0.3) |
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self.classifier = torch.nn.Linear(self.wav2vec2.config.hidden_size, num_classes) |
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def forward(self, input_values, attention_mask=None): |
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outputs = self.wav2vec2(input_values, attention_mask=attention_mask) |
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pooled_output = outputs.last_hidden_state.mean(dim=1) |
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pooled_output = self.dropout(pooled_output) |
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logits = self.classifier(pooled_output) |
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return logits |
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processor = Wav2Vec2Processor.from_pretrained("hrid0yyy/BornoNet") |
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num_classes = 50 |
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model = Wav2Vec2Classifier(num_classes=num_classes) |
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model.load_state_dict(torch.load(hf_hub_download("hrid0yyy/BornoNet", "pytorch_model.bin"), map_location="cpu")) |
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model.eval() |
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le_classes = np.load(hf_hub_download("hrid0yyy/BornoNet", "label_encoder_classes.npy"), allow_pickle=True) |
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def predict(audio): |
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try: |
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y, sr = librosa.load(audio, sr=16000) |
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inputs = processor(y, sampling_rate=sr, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values) |
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predicted = le_classes[torch.argmax(logits, dim=1).item()] |
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return f"Predicted character: {predicted}" |
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except Exception as e: |
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return f"Error processing audio: {str(e)}" |
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iface = gr.Interface( |
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fn=predict, |
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inputs=gr.Audio(type="filepath", label="Upload an MP3 file (16kHz)"), |
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outputs=gr.Textbox(label="Prediction"), |
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title="BornoNet: Bengali Speech Recognition", |
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description="Upload a 16kHz MP3 file to classify Bengali speech into characters (e.g., ত, অ, ক)." |
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) |
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iface.launch() |
