Automatic Speech Recognition
Transformers
PyTorch
English
data2vec-audio
speech
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use facebook/data2vec-audio-base-960h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-audio-base-960h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/data2vec-audio-base-960h")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-audio-base-960h") model = AutoModelForCTC.from_pretrained("facebook/data2vec-audio-base-960h") - Notebooks
- Google Colab
- Kaggle
GGUF + pure-C++ runtime in CrispASR (Data2Vec on the wav2vec2 backend)
#6 opened 18 days ago
by
cstr
Add Open ASR Leaderboard evaluation results
#5 opened about 1 month ago
by
SaylorTwift
Add Open ASR Leaderboard evaluation results
#4 opened about 1 month ago
by
SaylorTwift
Adding `safetensors` variant of this model
#3 opened about 3 years ago
by
SFconvertbot
Update README.md
#2 opened almost 4 years ago
by
mazharsaif
Transcribe Streaming audio and very long audio files(Out of Memory:how to read in chunks)
#1 opened almost 4 years ago
by
mazharsaif