Instructions to use hf-internal-testing/tiny-random-SpeechT5ForSpeechToText with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SpeechT5ForSpeechToText with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-SpeechT5ForSpeechToText")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SpeechT5ForSpeechToText") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-SpeechT5ForSpeechToText", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5a9a28d9107b7f765e9e295b32fbb183e2bf9bf93c113b0744af48052e46e02
- Size of remote file:
- 342 kB
- SHA256:
- e8266a098825e7fc700624553243bdc617bfcfa8b270b3790f7af9d314c7fa79
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.