Instructions to use espnet/fastspeech2_conformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use espnet/fastspeech2_conformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="espnet/fastspeech2_conformer")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToSpectrogram tokenizer = AutoTokenizer.from_pretrained("espnet/fastspeech2_conformer") model = AutoModelForTextToSpectrogram.from_pretrained("espnet/fastspeech2_conformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79ff3fd30e89ebe02ec0063723372b3c1e906faa2748e4b0090306f1d8739323
- Size of remote file:
- 281 MB
- SHA256:
- fa4096f1c4ce019198a7ec8054a7828916bc94b6c6cb968d7723d5a4b808b7a8
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