Audio preprocessing

In this exercise, you will learn how to adjust the sampling rate of audio data, as well as how to use an automatic preprocessor. You will be working with the VCTK Corpus, which includes around 44-hours of speech data uttered by 110 English speakers with various accents.

The dataset has already been loaded.

Latihan ini merupakan bagian dari kursus

Multi-Modal Models with Hugging Face

Instruksi latihan

Resample the audio to a frequency of 16,000 Hz in the dataset using the .cast_column() method.
Load the audio processor using the pretrained openai/whisper-small model.
Preprocess the audio data of the first datapoint, specifying the same sampling rate and padding=True.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Resample the audio to a frequency of 16,000 Hz
dataset = dataset.____("____", ____(sampling_rate=____))

# Load the audio processor
processor = ____

# Preprocess the audio data of the 0th dataset element
audio_pp = ____(dataset[0]["audio"]["array"], sampling_rate=____, padding=True, return_tensors="pt")
make_spectrogram(audio_pp["input_features"][0])

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Multi-Modal Models with Hugging Face

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

Navigate the Hugging Face model hub, transform raw text, audio, and visual data into AI-friendly formats. Learn how to find the latest most popular models for tasks such as text generation and harness the power of pre-built pipelines.

Exercise 1: Hugging Face model navigation Exercise 2: How many models!?Exercise 3: Finding the most popular text-to-image model Exercise 4: Preprocessing different modalities Exercise 5: Text tokenizing Exercise 6: Image preprocessing Exercise 7: Audio preprocessing

Latihan Saat Ini

Exercise 8: Pipeline tasks and evaluations Exercise 9: Pipeline caption generation Exercise 10: Passing keyword arguments Exercise 11: Model evaluation on a custom dataset

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Exercise 1: Zero-shot image classification Exercise 2: Zero-shot learning with CLIP Exercise 3: Automated caption quality assessment Exercise 4: Multi-modal sentiment analysis Exercise 5: Prompting Vision Language Models (VLMs)Exercise 6: Multi-modal sentiment classification with Qwen Exercise 7: Zero-shot video classification Exercise 8: Video audio splitting Exercise 9: Video sentiment analysis with CLIP CLAP

Transform ideas into reality! Master cutting-edge AI techniques to generate and manipulate visual content using text prompts. Create stunning images, edit photos intelligently, and build powerful question-answering systems for images and documents. Turn your creative vision into digital reality with multi-modal AI.

Exercise 1: Visual question-answering (VQA)Exercise 2: VQA with Vision Language Transformers (ViLTs)Exercise 3: Document VQA with LayoutLM Exercise 4: Image editing with diffusion models Exercise 5: Custom image editing Exercise 6: Image inpainting Exercise 7: Video generation Exercise 8: Build a video!Exercise 9: Assessing video generation performance Exercise 10: Congratulations!