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") - Notebooks
- Google Colab
- Kaggle
Commit ·
07f9c4a
1
Parent(s): 36c8941
Upload tokenizer
Browse files- tokenizer_config.json +1 -0
tokenizer_config.json
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"eos_token": "<sos/eos>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<blank>",
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"tokenizer_class": "FastSpeech2ConformerTokenizer",
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"unk_token": "<unk>"
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}
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"eos_token": "<sos/eos>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<blank>",
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"should_strip_spaces": true,
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"tokenizer_class": "FastSpeech2ConformerTokenizer",
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"unk_token": "<unk>"
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}
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