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@ -18,48 +18,22 @@ As suggested, it is suitable to extract features at high level or warm up a larg |
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Generate embeddings for the audio "test.wav". |
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*Write the pipeline in simplified style*: |
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*Write a pipeline with explicit inputs/outputs name specifications:* |
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```python |
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import towhee |
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( |
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towhee.glob('test.wav') |
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.audio_decode.ffmpeg() |
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.runas_op(func=lambda x:[y[0] for y in x]) |
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.audio_embedding.vggish() |
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.show() |
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) |
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``` |
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| [-0.4931737, -0.40068552, -0.032327592, ...] shape=(10, 128) | |
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*Write a same pipeline with explicit inputs/outputs name specifications:* |
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from towhee.dc2 import pipe, ops, DataCollection |
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```python |
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import towhee |
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( |
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towhee.glob['path']('test.wav') |
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.audio_decode.ffmpeg['path', 'frames']() |
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.runas_op['frames', 'frames'](func=lambda x:[y[0] for y in x]) |
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.audio_embedding.vggish['frames', 'vecs']() |
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.select['path', 'vecs']() |
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.show() |
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p = ( |
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pipe.input('path') |
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.map('path', 'frame', ops.audio_decode.ffmpeg()) |
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.map('frame', 'vecs', ops.audio_embedding.vggish()) |
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.output('path', 'vecs') |
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) |
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DataCollection(p('./test.wav')).show() |
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``` |
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[array([[-0.4931737 , -0.40068552, -0.03232759, ..., -0.33428153, |
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0.1333081 , -0.25221825], |
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[-0.49023268, -0.40161428, -0.03255743, ..., -0.33395663, |
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0.13261834, -0.25324696], |
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[-0.4992406 , -0.39848825, -0.03186834, ..., -0.33684137, |
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0.13326398, -0.25385314], |
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..., |
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[-0.49047503, -0.40119144, -0.03144619, ..., -0.33282205, |
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0.13334712, -0.2520305 ], |
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[-0.48861542, -0.40097567, -0.03173053, ..., -0.33255234, |
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0.13278192, -0.25157905], |
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[-0.4886143 , -0.40098593, -0.03175077, ..., -0.3325425 , |
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0.13271847, -0.25159872]], dtype=float32)] |
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<img src="./result.png" width="800px"/> |
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<br /> |
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