Instructions to use sanchit-gandhi/flax-wav2vec2-2-bart-large-cnn-gradient-accumulation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sanchit-gandhi/flax-wav2vec2-2-bart-large-cnn-gradient-accumulation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sanchit-gandhi/flax-wav2vec2-2-bart-large-cnn-gradient-accumulation")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("sanchit-gandhi/flax-wav2vec2-2-bart-large-cnn-gradient-accumulation") model = AutoModelForSpeechSeq2Seq.from_pretrained("sanchit-gandhi/flax-wav2vec2-2-bart-large-cnn-gradient-accumulation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
flax-wav2vec2-2-bart-large-cnn-gradient-accumulation / events.out.tfevents.1648134387.t1v-n-0e948115-w-0.175280.0.v2
- Xet hash:
- 53496bf52dd2978b82790216167e5d1bc54f3a53bf161a551afabdac172728a8
- Size of remote file:
- 3.15 MB
- SHA256:
- d311ab3bf05e2ffb53c81c62963f8b8e666c805b2d7f7fa3220fec4bc7d23000
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