Instructions to use hf-tiny-model-private/tiny-random-SpeechEncoderDecoderModel-wav2vec2-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-SpeechEncoderDecoderModel-wav2vec2-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-tiny-model-private/tiny-random-SpeechEncoderDecoderModel-wav2vec2-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SpeechEncoderDecoderModel-wav2vec2-bert") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-tiny-model-private/tiny-random-SpeechEncoderDecoderModel-wav2vec2-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 35a6080c2855b052e659c2e22d20cf4554437287b6af114fe9cc91cca1d7cce0
- Size of remote file:
- 629 kB
- SHA256:
- cb125985ec7a2cce6874224513ad3151893370ec85c2ebfd8b293af8becd2667
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