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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62e42763af9d0fdba339eb6f13c62c1af1682312538d652b618083602a0de666
- Size of remote file:
- 582 kB
- SHA256:
- cc593a747487ce8471654495f96ea9f3845ad5616a67b1b11af945cf4b187a9d
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