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
File size: 214 Bytes
1e01745 | 1 2 3 4 5 6 7 8 9 10 | {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": true,
"sampling_rate": 16000
}
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