Instructions to use facebook/mms-tts-acr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-acr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-acr")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-acr") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-acr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/mms-tts-acr: direct link, hf CLI and curl.
- Browser
- Download file 145 MB
-
https://huggingface.co/facebook/mms-tts-acr/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/mms-tts-acr/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/mms-tts-acr/resolve/main/pytorch_model.bin
145 MB
- Xet hash:
- b17fe00dd67ebc55db90a115abc0fa0edf2c38a579a541221bca06c6b4e7b2e2
- Size of remote file:
- 145 MB
- SHA256:
- 6e699e482f05e6f9fa64db4091a89523373ca4d697a1d31dca82255b1e3d0ebc
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