Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
English
wav2vec2
speech
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Vikasbhandari/wav2vec2-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vikasbhandari/wav2vec2-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Vikasbhandari/wav2vec2-train")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Vikasbhandari/wav2vec2-train") model = AutoModelForCTC.from_pretrained("Vikasbhandari/wav2vec2-train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Vikasbhandari/wav2vec2-train: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Vikasbhandari/wav2vec2-train/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Vikasbhandari/wav2vec2-train/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Vikasbhandari/wav2vec2-train/resolve/main/flax_model.msgpack
1.26 GB
- Xet hash:
- eb99f6dd647c66977ab265a001ceff352e845f26cfe3c33cce80c0c0057a8977
- Size of remote file:
- 1.26 GB
- SHA256:
- 90568e6185400541adead27c34d550df8fde3d35515c314fae28eaabbfe166a1
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