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README.md

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+---
+license: apache-2.0
+---
+# 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. 
+
+# BibTeX entry and citation info

models/OLMoASR-base.en.pt

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models/OLMoASR-large.en-v2.pt

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models/OLMoASR-large.en.pt

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models/OLMoASR-medium.en.pt

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models/OLMoASR-small.en.pt

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models/OLMoASR-tiny.en.pt

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