2d81521019
* add llama2 and chatglm optimize_model examples * update default usage * update command and some descriptions * move folder and remove general_int4 descriptions * change folder name
58 lines
2.2 KiB
Python
58 lines
2.2 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 whisper
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import time
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import librosa
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import argparse
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from bigdl.llm import optimize_model
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Recognize Tokens using `transcribe()` API for Openai Whisper model')
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parser.add_argument('--model-name', type=str, default="tiny",
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help="The model name(tiny, medium, base, etc.) for the Whisper model to be downloaded."
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"It is one of the official model names listed by `whisper.available_models()`, or"
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"path to a model checkpoint containing the model dimensions and the model state_dict.")
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parser.add_argument('--audio-file', type=str, required=True,
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help='The path of the audio file to be recognized.')
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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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# Load the input audio
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y, sr = librosa.load(args.audio_file)
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# Downsample the audio to 16kHz
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target_sr = 16000
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audio = librosa.resample(y,
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orig_sr=sr,
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target_sr=target_sr)
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# Load whisper model under pytorch framework
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model = whisper.load_model(args.model_name)
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# With only one line to enable bigdl optimize on a pytorch model
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model = optimize_model(model)
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st = time.time()
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result = model.transcribe(audio, verbose=True, language=args.language)
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end = time.time()
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Output', '-'*20)
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print(result["text"])
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