import os
os.system("apt-get update")
os.system("apt-get install -y python3-pip") # Make sure pip is available
os.system("pip install transformers")
# Restart the kernel here if you have the option (in a notebook setting)
import transformers
from torch.utils.data import DataLoader
import streamlit as st
from datasets import load_dataset, Audio
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
import torch
import os
# Install using apt
# Load the MInDS-14 dataset
dataset = load_dataset("PolyAI/minds14", "en-US", split="train", trust_remote_code=True)
# Load pretrained model and feature extractor
model = AutoModelForAudioClassification.from_pretrained("facebook/wav2vec2-base")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
# Resample audio to 16kHz
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
# Preprocessing function
def preprocess_function(examples):
audio_arrays = [x["array"] for x in examples["audio"]]
inputs = feature_extractor(
audio_arrays,
sampling_rate=16000,
padding=True,
max_length=100000,
truncation=True,
)
return inputs
dataset = dataset.map(preprocess_function, batched=True)
dataset = dataset.rename_column("intent_class", "labels")
dataset = dataset.set_format(type="torch", columns=["input_values", "labels"])
# Create DataLoader
batch_size = 4 # Adjust as needed
dataloader = DataLoader(dataset, batch_size=batch_size)
# Set device and move model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Training loop (example)
num_epochs = 2 # Keep small for testing on Spaces!
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
for epoch in range(num_epochs):
for batch in dataloader:
input_values = batch["input_values"].to(device)
labels = batch["labels"].to(device)
optimizer.zero_grad()
outputs = model(input_values, labels=labels)
loss = outputs.loss
loss.backward()
optimizer.step()
print(f"Epoch: {epoch+1}, Loss: {loss.item()}")
# Streamlit UI
st.title("Audio Classification with Minds14")
st.write("Training complete!") # You'll want to add more insightful outputs here eventually
st.markdown("""
Check out Chapter 5 of the Hugging Face course to learn more about other important topics such as loading remote or local datasets, tools for cleaning up a dataset, and creating your own dataset.
""", unsafe_allow_html=True)