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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6848cf841e0b9668c60b8a1bd52ad34d331ed701f1568236d93f0fbd93a6ce45
- Size of remote file:
- 309 kB
- SHA256:
- 656bc98cf084e26df1946f73d57b5dc1ad948baef9f90063e745386d9c51d450
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