9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
77 lines
3.4 KiB
Python
77 lines
3.4 KiB
Python
#
|
|
# Copyright 2016 The BigDL Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
import torch
|
|
import time
|
|
import argparse
|
|
|
|
from ipex_llm.transformers import AutoModelForSpeechSeq2Seq
|
|
from transformers import WhisperProcessor
|
|
from datasets import load_dataset
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Recognize Tokens using `generate()` API for Whisper model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="openai/whisper-tiny",
|
|
help='The huggingface repo id for the Whisper model to be downloaded'
|
|
', or the path to the huggingface checkpoint folder')
|
|
parser.add_argument('--repo-id-or-data-path', type=str,
|
|
default="hf-internal-testing/librispeech_asr_dummy",
|
|
help='The huggingface repo id for the audio dataset to be downloaded'
|
|
', or the path to the huggingface dataset folder')
|
|
parser.add_argument('--language', type=str, default="english",
|
|
help='language to be transcribed')
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
dataset_path = args.repo_id_or_data_path
|
|
language = args.language
|
|
|
|
# Load model in 4 bit,
|
|
# which convert the relevant layers in the model into INT4 format
|
|
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path,
|
|
load_in_4bit=True,
|
|
optimize_model=False,
|
|
use_cache=True)
|
|
model.to('xpu')
|
|
model.config.forced_decoder_ids = None
|
|
|
|
# Load processor
|
|
processor = WhisperProcessor.from_pretrained(model_path)
|
|
forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task="transcribe")
|
|
|
|
# Load dummy dataset and read audio files
|
|
ds = load_dataset(dataset_path, "clean", split="validation")
|
|
|
|
# Generate predicted tokens
|
|
with torch.inference_mode():
|
|
sample = ds[0]["audio"]
|
|
|
|
input_features = processor(sample["array"],
|
|
sampling_rate=sample["sampling_rate"],
|
|
return_tensors="pt").input_features.to('xpu')
|
|
st = time.time()
|
|
# if your selected model is capable of utilizing previous key/value attentions
|
|
# to enhance decoding speed, but has `"use_cache": false` in its model config,
|
|
# it is important to set `use_cache=True` explicitly in the `generate` function
|
|
# to obtain optimal performance with BigDL-LLM INT4 optimizations
|
|
predicted_ids = model.generate(input_features,
|
|
forced_decoder_ids=forced_decoder_ids)
|
|
end = time.time()
|
|
output_str = processor.batch_decode(predicted_ids, skip_special_tokens=True)
|
|
print(f'Inference time: {end-st} s')
|
|
print('-'*20, 'Output', '-'*20)
|
|
print(output_str)
|