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| #### Whisper Tiny (EN) | |
| - ID: openai/whisper-tiny.en | |
| - Hugging Face: [model](https://huggingface.co/openai/whisper-tiny.en) | |
| - Creator: openai | |
| - Finetuned: No | |
| - Model Size: 39 M Parameters | |
| - Model Paper: [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) | |
| - Training Data: The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. | |
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| #### S2T Medium ASR | |
| - ID: facebook/s2t-medium-librispeech-asr | |
| - Hugging Face: [model](https://huggingface.co/facebook/s2t-medium-librispeech-asr) | |
| - Creator: facebook | |
| - Finetuned: No | |
| - Model Size: 71.2 M Parameters | |
| - Model Paper: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) | |
| - Training Data: [LibriSpeech ASR Corpus](https://www.openslr.org/12) | |
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