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import gradio as gr
import torch
from transformers import pipeline, AutoTokenizer
from nemo.collections.asr.models import EncDecMultiTaskModel
# load model
canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b')
# update dcode params
decode_cfg = canary_model.cfg.decoding
decode_cfg.beam.beam_size = 1
canary_model.change_decoding_strategy(decode_cfg)
pipe = pipeline(
"automatic-speech-recognition",
model="nvidia/canary-1b"
)
# pipe = pipeline(
# "text-generation",
# model="QuantFactory/Meta-Llama-3-8B-Instruct-GGUF",
# model_kwargs={"torch_dtype": torch.bfloat16},
# device_map="auto"
# )
gr.Interface.from_pipeline(pipe,
title="ASR",
description="Using pipeline with Canary-1B",
).launch(inbrowser=True) |