72 lines
2.9 KiB
Python
72 lines
2.9 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 time
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import librosa
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import argparse
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from transformers import pipeline
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from bigdl.llm.transformers import AutoModelForSpeechSeq2Seq
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from transformers.models.whisper import WhisperFeatureExtractor, WhisperTokenizer
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Recognize Long Segment using `generate()` API for Whisper model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="openai/whisper-medium",
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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('--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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parser.add_argument('--batch-size', type=int, default=2,
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help='The batch_size of pipeline inference, '
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'it usually equals of length of the audio divided by chunk-length.')
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parser.add_argument('--chunk-length', type=int, default=30,
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help="The maximum time lengths of chuncks of sampling_rate samples used to trim"
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"and pad longer or shorter audio sequences. Default to be 30s.")
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args = parser.parse_args()
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# Path to the .wav audio file
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audio_file_path = args.audio_file
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model_path = args.repo_id_or_model_path
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# Load the input audio
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y, sr = librosa.load(audio_file_path, sr=None)
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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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model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path, load_in_4bit=True)
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model.config.forced_decoder_ids = None
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pipe = pipeline(
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"automatic-speech-recognition",
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model=model,
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feature_extractor= WhisperFeatureExtractor.from_pretrained(model_path),
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tokenizer= WhisperTokenizer.from_pretrained(model_path, language=args.language),
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chunk_length_s=args.chunk_length,
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)
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start = time.time()
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prediction = pipe(audio, batch_size=args.batch_size)["text"]
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print(f"inference time is {time.time()-start}")
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print(prediction)
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