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* It ensures that audio features are correctly duplicated across beams for each batch item. * Added a test for `decode()` that includes a regression test for this. * Update *.github/workflows/test.yml* to run the new test for `decode()` in tiny. * This issue was introduced in PR #1483.
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import os
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import pytest
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import torch
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import whisper
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@pytest.mark.parametrize("model_name", whisper.available_models())
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def test_decode(model_name: str):
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# Regression test: batch_size and beam_size should work together
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beam_size = 2
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batch_size = 2
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = whisper.load_model(model_name).to(device)
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audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac")
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language = "en" if model_name.endswith(".en") else None
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options = whisper.DecodingOptions(language=language, beam_size=beam_size)
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audio = whisper.load_audio(audio_path)
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audio = whisper.pad_or_trim(audio)
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mel = whisper.log_mel_spectrogram(audio).to(device)
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# Create a small batch
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batch_mel = mel.unsqueeze(0).repeat(batch_size, 1, 1)
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results = model.decode(batch_mel, options)
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# Since both examples are the same, results should be identical
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assert len(results) == batch_size
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assert results[0].text == results[1].text
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decoded_text = results[0].text.lower()
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assert "my fellow americans" in decoded_text
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assert "your country" in decoded_text
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assert "do for you" in decoded_text
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timing_checked = False
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if hasattr(results[0], "segments"):
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for segment in results[0].segments:
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for timing in segment["words"]:
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assert timing["start"] < timing["end"]
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if timing["word"].strip(" ,") == "Americans":
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assert timing["start"] <= 1.8
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assert timing["end"] >= 1.8
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timing_checked = True
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if hasattr(results[0], "segments"):
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assert timing_checked
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