Instructions to use hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech") model = AutoModelForTextToWaveform.from_pretrained("hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65d13ea44b6414e92e6c5514b27e0041621ab1c051c61789b6e6c85b957c4c6a
- Size of remote file:
- 309 kB
- SHA256:
- 4f46246e891ccbb448c23d7f07f8cdba74c45fa3ce8bb4018b02580ac8e6d38e
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