Instructions to use harshit345/wav2vec2-large-lv60-timit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshit345/wav2vec2-large-lv60-timit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="harshit345/wav2vec2-large-lv60-timit")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("harshit345/wav2vec2-large-lv60-timit") model = AutoModelForCTC.from_pretrained("harshit345/wav2vec2-large-lv60-timit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from harshit345/wav2vec2-large-lv60-timit: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/harshit345/wav2vec2-large-lv60-timit/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://harshit345/wav2vec2-large-lv60-timit/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/harshit345/wav2vec2-large-lv60-timit/resolve/main/flax_model.msgpack
1.26 GB
- Xet hash:
- e6ff657e15ecd8579f480ebee04a7855f37b87974a358efb4f8dc54c7b0a7ff8
- Size of remote file:
- 1.26 GB
- SHA256:
- b8a00fb80893512f553cc97489bdf627920cb734a04984fd41d7b1c5590880c4
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