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from datasets import load_dataset, Audio
from transformers import pipeline
import torchaudio
import numpy as np
# Initialize the zero-shot audio classification pipeline
zero_shot_classifier = pipeline(
task="zero-shot-audio-classification",
model="laion/clap-htsat-unfused"
)
# Define the candidate labels for classification
candidate_labels = [
"Sound of a dog barking",
"Sound of car driving",
"Sound of a person talking",
"Sound of a bird singing",
"Sound of a plane flying",
]
# Function to perform inference on a dataset
def audio_dataset_inference():
# Load a dataset containing different 5-second sound clips
dataset = load_dataset("ashraq/esc50", split="train[0:10]")
# Ensure all audio samples in the dataset have the same sampling rate (48kHz)
dataset = dataset.cast_column("audio", Audio(sampling_rate=48_000))
# Select the first audio sample from the dataset
audio_sample = dataset[0]
# Perform zero-shot classification on the selected audio sample
result = zero_shot_classifier(
audio_sample["audio"]["array"], # Extract the audio array from the dataset sample
candidate_labels=candidate_labels # Pass the candidate labels for classification
)
print(result)
def classify_audio(audio_file):
"""
Perform zero-shot classification on a single audio file.
Args:
audio_file (str): Path to the audio file to classify.
Returns:
dict: Classification labels and their corresponding scores.
"""
try:
# Load audio file using torchaudio
waveform, sample_rate = torchaudio.load(audio_file)
# Resample audio to 48kHz (if necessary)
if sample_rate != 48000:
resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=48000)
waveform = resampler(waveform)
# Convert waveform to NumPy array
audio_array = waveform.squeeze().numpy()
# Perform zero-shot classification
result = zero_shot_classifier(
audio_array, # Pass the audio array
candidate_labels=candidate_labels
)
return {label['label']: label['score'] for label in result}
except Exception as e:
print(f"Error in classify_audio: {e}")
return {"Error": str(e)}
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