Datasets:
Update generate_robust_hp.py
Browse files- generate_robust_hp.py +2 -2
generate_robust_hp.py
CHANGED
@@ -94,7 +94,7 @@ for line in f_noisy_wav.readlines():
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assert clean_utt_id == utt_id, (line, clean_line)
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gt = ' '.join(f_text.readline().strip().split()[1:])
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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(model.device)
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options = whisper.DecodingOptions(language='en', beam_size=50)
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texts, confidences = whisper.decode_score(model, mel, options)
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@@ -104,7 +104,7 @@ for line in f_noisy_wav.readlines():
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## clean audio feats
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clean_audio = whisper.load_audio(clean_audio_path)
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clean_audio = whisper.pad_or_trim(clean_audio)
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clean_mel = whisper.log_mel_spectrogram(clean_audio).to(model.device)
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clean_audio_features = model.encoder(clean_mel.unsqueeze(0))[0]
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assert clean_utt_id == utt_id, (line, clean_line)
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gt = ' '.join(f_text.readline().strip().split()[1:])
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audio = whisper.load_audio(audio_path)
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+
# audio = whisper.pad_or_trim(audio) # padding to 30s
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mel = whisper.log_mel_spectrogram(audio).to(model.device)
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options = whisper.DecodingOptions(language='en', beam_size=50)
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texts, confidences = whisper.decode_score(model, mel, options)
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## clean audio feats
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clean_audio = whisper.load_audio(clean_audio_path)
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+
# clean_audio = whisper.pad_or_trim(clean_audio) # padding to 30s
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clean_mel = whisper.log_mel_spectrogram(clean_audio).to(model.device)
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clean_audio_features = model.encoder(clean_mel.unsqueeze(0))[0]
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