{"library":"torchaudio","code":"import torch\nimport torchaudio\nfrom torchaudio import transforms\n\n# Create a dummy waveform (1 channel, 16000 samples at 16kHz)\n# In a real scenario, you would load an audio file: waveform, sample_rate = torchaudio.load(\"path/to/audio.wav\")\nwaveform = torch.randn(1, 16000)\nsample_rate = 16000\n\n# Define a MelSpectrogram transform\nmelspectrogram_transform = transforms.MelSpectrogram(sample_rate=sample_rate, n_mels=128)\n\n# Apply the transform\nmelspectrogram = melspectrogram_transform(waveform)\n\nprint(f\"Waveform shape: {waveform.shape}\")\nprint(f\"MelSpectrogram shape: {melspectrogram.shape}\")\n# Expected output: \n# Waveform shape: torch.Size([1, 16000])\n# MelSpectrogram shape: torch.Size([1, 128, X]) where X depends on n_fft and hop_length\n","lang":"python","description":"This quickstart demonstrates how to import TorchAudio, create a dummy audio waveform, and apply a common audio transformation like MelSpectrogram. In practice, `torchaudio.load` is used to load actual audio files.","tag":null,"tag_description":null,"last_tested":"2026-04-24","results":[{"runtime":"python:3.10-alpine","exit_code":1},{"runtime":"python:3.10-slim","exit_code":1},{"runtime":"python:3.11-alpine","exit_code":1},{"runtime":"python:3.11-slim","exit_code":1},{"runtime":"python:3.12-alpine","exit_code":1},{"runtime":"python:3.12-slim","exit_code":1},{"runtime":"python:3.13-alpine","exit_code":1},{"runtime":"python:3.13-slim","exit_code":1},{"runtime":"python:3.9-alpine","exit_code":1},{"runtime":"python:3.9-slim","exit_code":-1}]}