File size: 1,806 Bytes
e134a68 56a843c e134a68 56a843c e5da973 56a843c e5da973 e134a68 6ee1e34 b599b90 2e2e3c6 6ee1e34 2e2e3c6 627d41f 2e2e3c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
---
language:
- en
license: mit
tags:
- convAI
- conversational
- ASR
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
widget:
- text: Hello who are you?
example_title: Identity
- text: What can you do?
example_title: Capabilities
- text: Create a fastapi endpoint to retrieve the weather given a zip code.
example_title: Coding
pipeline_tag: text-generation
---
# Disclaimer
THIS PROJECT IS STILL IN WIP
# Phi-2-audio-super
Base Model: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
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).
## How to run inference for text only:
```python
import transformers
import torch
if __name__ == "__main__":
model_name = "Thytu/phi-2-audio-super"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = (
transformers.AutoModelForCausalLM.from_pretrained(
model_name,
)
.to("cuda:0")
.eval()
)
# Exactly like for phi-2-super :D
messages = [
{"role": "user", "content": "Hello, who are you?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
input_ids_cutoff = inputs.size(dim=1)
with torch.no_grad():
generated_ids = model.generate(
input_ids=inputs,
use_cache=True,
max_new_tokens=512,
temperature=0.2,
top_p=0.95,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
completion = tokenizer.decode(
generated_ids[0][input_ids_cutoff:],
skip_special_tokens=True,
)
print(completion)
```
## How to run inference for ASR:
TODO |