Instructions to use timBoML/2cent-tts-25m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use timBoML/2cent-tts-25m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="timBoML/2cent-tts-25m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("timBoML/2cent-tts-25m") model = AutoModelForCausalLM.from_pretrained("timBoML/2cent-tts-25m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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---
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library_name: transformers
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---
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Converted the models from https://github.com/taylorchu/2cent-tts to .safetensors. Below is inference code:
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from IPython.display import Audio, display
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display(Audio(sample.detach().squeeze().to("cpu").numpy(), rate=24000))
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```
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---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-to-speech
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---
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Converted the models from https://github.com/taylorchu/2cent-tts to .safetensors. Below is inference code:
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from IPython.display import Audio, display
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display(Audio(sample.detach().squeeze().to("cpu").numpy(), rate=24000))
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```
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