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---
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](https://github.com/allenai/OLMoASR.git)
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](https://github.com/allenai/OLMoASR.git) 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](link).
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](https://github.com/allenai/OLMoASR.git) for more details.
# License
This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with [Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use).
# BibTeX entry and citation info |