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
File size: 125 Bytes
f7f0f85 | 1 2 3 4 5 6 7 8 | {
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"unk_token": "[UNK]"
}
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