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