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README.md
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pipeline_tag: text-generation
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---
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Fine-tuned version of [abacaj/phi-2-super](https://huggingface.co/abacaj/phi-2-super) for ASR on [librispeech_asr](https://huggingface.co/datasets/librispeech_asr).
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pipeline_tag: text-generation
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---
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# Phi-2-audio-super
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Base Model: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
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Fine-tuned version of [abacaj/phi-2-super](https://huggingface.co/abacaj/phi-2-super) for ASR on [librispeech_asr](https://huggingface.co/datasets/librispeech_asr).
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## How to run inference for text only:
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```python
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import transformers
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import torch
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if __name__ == "__main__":
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model_name = "abacaj/phi-2-audio-super"
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
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model = (
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transformers.AutoModelForCausalLM.from_pretrained(
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model_name,
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)
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.to("cuda:0")
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.eval()
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)
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# Exactly like for phi-2-super :D
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messages = [
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{"role": "user", "content": "Hello, who are you?"}
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]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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input_ids_cutoff = inputs.size(dim=1)
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with torch.no_grad():
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generated_ids = model.generate(
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input_ids=inputs,
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use_cache=True,
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max_new_tokens=512,
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temperature=0.2,
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top_p=0.95,
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do_sample=True,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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)
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completion = tokenizer.decode(
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generated_ids[0][input_ids_cutoff:],
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skip_special_tokens=True,
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)
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print(completion)
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```
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## How to run inference for ASR:
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TODO
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