947b1e27b7
* Fix local data path * Remove non-essential files * Add readme * Minor fixes to script * Bugfix, refactor * Add references to original source. Bugfixes. * Reviewer comments * Properly print and explain output * Move files to dev/benchmark * Fixes
72 lines
No EOL
3 KiB
Python
72 lines
No EOL
3 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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from datasets import load_dataset
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from bigdl.llm.transformers import AutoModelForSpeechSeq2Seq
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from transformers import WhisperProcessor
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import torch
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from evaluate import load
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import time
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import argparse
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def get_args():
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parser = argparse.ArgumentParser(description="Evaluate Whisper performance and accuracy")
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parser.add_argument('--model_path', required=True, help='pretrained model path')
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parser.add_argument('--data_type', required=True, help='clean, other')
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parser.add_argument('--device', required=False, help='cpu, xpu')
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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args = get_args()
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if args.device == "":
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args.device = "cpu"
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speech_dataset = load_dataset('./librispeech_asr.py', name=args.data_type, split='test').select(range(500))
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processor = WhisperProcessor.from_pretrained(args.model_path)
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forced_decoder_ids = processor.get_decoder_prompt_ids(language='en', task='transcribe')
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model = AutoModelForSpeechSeq2Seq.from_pretrained(args.model_path, load_in_low_bit="sym_int4", optimize_model=True).eval().to(args.device)
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model.config.forced_decoder_ids = None
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def map_to_pred(batch):
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audio = batch["audio"]
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start_time = time.time()
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input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
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batch["reference"] = processor.tokenizer._normalize(batch['text'])
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with torch.no_grad():
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predicted_ids = model.generate(input_features.to(args.device), forced_decoder_ids=forced_decoder_ids, use_cache=True)[0]
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if args.device == "xpu":
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torch.xpu.synchronize()
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infer_time = time.time() - start_time
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transcription = processor.decode(predicted_ids)
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batch["prediction"] = processor.tokenizer._normalize(transcription)
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batch["length"] = len(audio["array"])/audio["sampling_rate"]
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batch["time"] = infer_time
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print(batch["reference"])
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print(batch["prediction"])
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return batch
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result = speech_dataset.map(map_to_pred, keep_in_memory=True)
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wer = load("./wer")
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speech_length = sum(result["length"][1:])
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prc_time = sum(result["time"][1:])
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print("Realtime Factor(RTF) is : %.4f" % (prc_time/speech_length))
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print("Realtime X(RTX) is : %.2f" % (speech_length/prc_time))
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print(f'WER is {100 * wer.compute(references=result["reference"], predictions=result["prediction"])}') |