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import gradio as gr
from transformers import Wav2Vec2Processor
import torch
import librosa
import numpy as np
from huggingface_hub import hf_hub_download
class Wav2Vec2Classifier(torch.nn.Module):
def __init__(self, num_classes):
super().__init__()
from transformers import Wav2Vec2Model
self.wav2vec2 = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base")
self.dropout = torch.nn.Dropout(0.3)
self.classifier = torch.nn.Linear(self.wav2vec2.config.hidden_size, num_classes)
def forward(self, input_values, attention_mask=None):
outputs = self.wav2vec2(input_values, attention_mask=attention_mask)
pooled_output = outputs.last_hidden_state.mean(dim=1)
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
return logits
processor = Wav2Vec2Processor.from_pretrained("hrid0yyy/BornoNet")
num_classes = 50
model = Wav2Vec2Classifier(num_classes=num_classes)
model.load_state… -
initial commit
