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https://github.com/ml-explore/mlx-examples.git
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107 lines
2.4 KiB
Python
107 lines
2.4 KiB
Python
# Copyright © 2023 Apple Inc.
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import sys
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import time
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import mlx.core as mx
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from whisper import load_models
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from whisper import audio
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from whisper import decoding
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from whisper import transcribe
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audio_file = "whisper/assets/ls_test.flac"
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def timer(fn, *args):
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for _ in range(5):
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fn(*args)
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num_its = 10
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tic = time.perf_counter()
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for _ in range(num_its):
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fn(*args)
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toc = time.perf_counter()
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return (toc - tic) / num_its
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def feats():
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data = audio.load_audio(audio_file)
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data = audio.pad_or_trim(data)
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mels = audio.log_mel_spectrogram(data)
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mx.eval(mels)
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return mels
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def model_forward(model, mels, tokens):
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logits = model(mels, tokens)
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mx.eval(logits)
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return logits
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def decode(model, mels):
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return decoding.decode(model, mels)
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def everything():
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return transcribe(audio_file)
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if __name__ == "__main__":
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# get command line arguments without 3rd party libraries
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# the command line argument to benchmark all models is "all"
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models = ["tiny"]
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if len(sys.argv) > 1:
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if sys.argv[1] == "--all":
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models = ["tiny", "small", "medium", "large"]
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for model_name in models:
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feat_time = timer(feats)
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print(f"\nModel: {model_name.upper()}")
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print(f"\nFeature time {feat_time:.3f}")
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mels = feats()[None]
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tokens = mx.array(
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[
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50364,
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1396,
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264,
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665,
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5133,
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23109,
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25462,
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264,
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6582,
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293,
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750,
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632,
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42841,
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292,
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370,
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938,
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294,
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4054,
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293,
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12653,
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356,
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50620,
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50620,
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23563,
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322,
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3312,
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13,
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50680,
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],
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mx.int32,
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)[None]
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model = load_models.load_model(f"{model_name}")
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model_forward_time = timer(model_forward, model, mels, tokens)
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print(f"Model forward time {model_forward_time:.3f}")
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decode_time = timer(decode, model, mels)
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print(f"Decode time {decode_time:.3f}")
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everything_time = timer(everything)
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print(f"Everything time {everything_time:.3f}")
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print(f"\n{'-----' * 10}\n")
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