Instructions to use hf-internal-testing/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-internal-testing/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-internal-testing/tiny-random-SpeechEncoderDecoderModel-wav2vec2-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SpeechEncoderDecoderModel-wav2vec2-bert") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-SpeechEncoderDecoderModel-wav2vec2-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 269cb0b5b1cef8bcdbb85207363a13b2156e859a784e75a88dfd6cb1be58f010
- Size of remote file:
- 629 kB
- SHA256:
- e720acdad650ca059c3111daf02ec4877d85bcdcd9c8cfbaf0c128c4276b048d
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