0284801fbd
* Set BIGDL_IMPORT_IPEX default to true, i.e., auto import IPEX for XPU. * Remove import intel_extension_for_pytorch as ipex from GPU example. * Add support for bigdl-core-xe-21.
70 lines
3 KiB
Python
70 lines
3 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 time
|
|
import argparse
|
|
|
|
from transformers import pipeline
|
|
from bigdl.llm.transformers import AutoModelForSpeechSeq2Seq
|
|
from transformers.models.whisper import WhisperFeatureExtractor, WhisperTokenizer
|
|
from datasets import load_dataset
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Recognize Long Segment using `generate()` API for Distil-Whisper model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="distil-whisper/distil-large-v2",
|
|
help='The huggingface repo id for the Distil-Whisper model to be downloaded'
|
|
', or the path to the huggingface checkpoint folder')
|
|
parser.add_argument('--repo-id-or-data-path', type=str,
|
|
default="distil-whisper/librispeech_long",
|
|
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')
|
|
parser.add_argument('--batch-size', type=int, default=16,
|
|
help='The batch_size of pipeline inference, '
|
|
'it usually equals of length of the audio divided by chunk-length.')
|
|
parser.add_argument('--chunk-length', type=int, default=15,
|
|
help="The maximum time lengths of chuncks of sampling_rate samples used to trim"
|
|
"and pad longer or shorter audio sequences. Default to be 30s.")
|
|
|
|
args = parser.parse_args()
|
|
|
|
model_path = args.repo_id_or_model_path
|
|
dataset_path = args.repo_id_or_data_path
|
|
|
|
# Load dummy dataset and read audio files
|
|
dataset = load_dataset(dataset_path, "clean", split="validation")
|
|
audio = dataset[0]["audio"]
|
|
|
|
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path, load_in_4bit=True)
|
|
model.to('xpu')
|
|
model.config.forced_decoder_ids = None
|
|
|
|
pipe = pipeline(
|
|
"automatic-speech-recognition",
|
|
model=model,
|
|
feature_extractor=WhisperFeatureExtractor.from_pretrained(model_path),
|
|
tokenizer= WhisperTokenizer.from_pretrained(model_path, language=args.language),
|
|
chunk_length_s=args.chunk_length,
|
|
device='xpu'
|
|
)
|
|
|
|
start = time.time()
|
|
prediction = pipe(audio, batch_size=args.batch_size)["text"]
|
|
print(f"inference time is {time.time()-start}")
|
|
|
|
print(prediction)
|