Generating new speech

Time to complete your mastery of using Hugging Face audio models! You'll use a fine-tuned model to generate new speech for a given voice. You will choose a voice from the VCTK Corpus as the basis for the new audio.

The dataset and SpeechT5ForTextToSpeech model (model) have already been loaded, and a make_spectogram() function has been provided to aid with plotting.

Diese Übung ist Teil des Kurses

Multi-Modal Models with Hugging Face

Anleitung zur Übung

Load a sample speaker embedding from index 5 of the test dataset.
Generate the speech from the processed text by specifying the inputs, speaker_embedding, and vocoder.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

text = "Hi, welcome to your new voice."

# Load a speaker embedding from the dataset
speaker_embedding = torch.tensor(dataset[5]["____"]).unsqueeze(0)

inputs = processor(text=text, return_tensors="pt")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

# Generate speech
speech = model.generate_speech(____["input_ids"], ____, ____=____)

make_spectrogram(speech)

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Diese Übung ist Teil des Kurses

Multi-Modal Models with Hugging Face

Mittlere SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

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

Learn to master individual modalities with state-of-the-art models. Dive into computer vision for image classification and segmentation, explore speech recognition and text-to-speech synthesis, and learn effective fine-tuning techniques. Build practical skills with pre-trained models from Hugging Face's transformers library.

Exercise 1: Computer vision Exercise 2: Image classification Exercise 3: Object detection Exercise 4: Image background removal Exercise 5: Fine-tuning computer vision models Exercise 6: CV fine-tuning: dataset prep Exercise 7: CV fine-tuning: model classes Exercise 8: CV fine-tuning: trainer configuration Exercise 9: Speech recognition and audio generation Exercise 10: Automatic speech recognition Exercise 11: Creating speech embeddings Exercise 12: Audio denoising Exercise 13: Fine-tuning text-to-speech models Exercise 14: Fine-tuning a text-to-speech model Exercise 15: Generating new speech

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