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# Import necessary libraries
import gradio as gr
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from transformers import WhisperModel, WhisperFeatureExtractor
import datasets
from datasets import load_dataset, DatasetDict, Audio
from huggingface_hub import PyTorchModelHubMixin
# Define data class
class SpeechInferenceDataset(Dataset):
def __init__(self, audio_data, text_processor):
self.audio_data = audio_data
self.text_processor = text_processor
def __len__(self):
return len(self.audio_data)
def __getitem__(self, index):
inputs = self.text_processor(self.audio_data[index]["audio"]["array"],
return_tensors="pt",
sampling_rate=self.audio_data[index]["audio"]["sampling_rate"])
input_features = inputs.input_features
# Assuming 'encoder' is defined or available in the scope
decoder_input_ids = torch.tensor([[1, 1]]) * encoder.config.decoder_start_token_id
return input_features, decoder_input_ids
# Define model class
class SpeechClassifier(nn.Module, PyTorchModelHubMixin):
def __init__(self, config):
super(SpeechClassifier, self).__init__()
self.encoder = WhisperModel.from_pretrained(config["encoder"])
self.classifier = nn.Sequential(
nn.Linear(self.encoder.config.hidden_size, 4096),
nn.ReLU(),
nn.Linear(4096, 2048),
nn.ReLU(),
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Linear(1024, 512),
nn.ReLU(),
nn.Linear(512, config["num_labels"])
)
def forward(self, input_features, decoder_input_ids):
outputs = self.encoder(input_features, decoder_input_ids=decoder_input_ids)
pooled_output = outputs['last_hidden_state'][:, 0, :]
logits = self.classifier(pooled_output)
return logits
# Prepare data function
def prepare_data(audio_file_path, model_checkpoint="openai/whisper-base"):
feature_extractor = WhisperFeatureExtractor.from_pretrained(model_checkpoint)
inference_data = datasets.Dataset.from_dict({"path": [audio_file_path], "audio": [audio_file_path]}).cast_column("audio", Audio(sampling_rate=16_000))
inference_dataset = SpeechInferenceDataset(inference_data, feature_extractor)
inference_loader = DataLoader(inference_dataset, batch_size=1, shuffle=False)
input_features, decoder_input_ids = next(iter(inference_loader))
# Ensure 'device' is defined or replace with 'torch.device("cpu")' if GPU is not available
input_features = input_features.squeeze(1).to(device)
decoder_input_ids = decoder_input_ids.squeeze(1).to(device)
return input_features, decoder_input_ids
# Prediction function
def predict(audio_file_path, config={"encoder": "openai/whisper-base", "num_labels": 2}):
input_features, decoder_input_ids = prepare_data(audio_file_path)
model = SpeechClassifier(config)
model.eval()
with torch.no_grad():
logits = model(input_features, decoder_input_ids)
predicted_ids = int(torch.argmax(logits, dim=-1))
return predicted_ids
# Gradio Interface function
def gradio_interface(uploaded_file):
with open(uploaded_file.name, "wb") as f:
f.write(uploaded_file.read())
prediction = predict(uploaded_file.name)
label = "Hypernasality Detected" if prediction == 1 else "No Hypernasality Detected"
return label
# Create and launch Gradio Interface with File upload input
iface = gr.Interface(fn=gradio_interface,
inputs=gr.inputs.File(label="Upload Audio File"),
outputs="text")
iface.launch()
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