Instructions to use esc-bench/wav2vec2-aed-ami with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-bench/wav2vec2-aed-ami with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-bench/wav2vec2-aed-ami")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-bench/wav2vec2-aed-ami") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-bench/wav2vec2-aed-ami") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a1fbbe2bddb10a19c1f1ea01f8ab0cbd4854f4ed0b33651d7d88f70d2f1f8b0
- Size of remote file:
- 2.35 GB
- SHA256:
- 601eb9d548ec5f86813779b9e917e8641b68cd5bd796b316fce7d2c1db7d0785
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