license: apache-2.0
pipeline_tag: audio-text-to-text
OLMoASR
OLMoASR is a series of English automatic speech recognition (ASR) models proposed in the OLMoASR: Open Models and Data for Training Robust Speech Recognition Models paper by Huong Ngo et al. from Ai2. Trained on 440K hours of weakly-supervised audio-text pairs collected from the public internet, OLMoASR demonstrates strong robustness and zero-shot capabilities. Visit the OLMoASR repository for access to data processing, training and evaluation code.
Model Details
OLMoASR uses a Transformer-based encoder-decoder architecture and is an audio language model (LM), where there is an audio encoder and language decoder. OLMoASR has 5 different model sizes and all checkpoints are trained with English-only data. Below is a table enumerating the different model sizes and associated parameter count.
| Size | Parameters |
|---|---|
| tiny | 39 M |
| base | 74 M |
| small | 244 M |
| medium | 769 M |
| large | 1.5 B |
| large-v2 | 1.5 B |
Training Data
OLMoASR is trained on 440K hours of weakly-supervised data subsampled from OLMoASR-Mix, a filtered version of OLMoASR-Pool. OLMoASR-Mix is a collection 1M hours of audio-text pairs, curated from the 3M hours of OLMoASR-Pool.
Usage
To perform transcription, you can run
import olmoasr
model = olmoasr.load_model("medium", inference=True)
result = model.transcribe("audio.mp3")
print(result)
Evaluation
To perform evaluation, you can visit the OLMoASR repository for more details.