TroglodyteDerivations
commited on
Updated with: [removal] of display_segment
Browse files
app.py
CHANGED
@@ -336,33 +336,7 @@ def plot_alignments(trellis, segments, word_segments, waveform=np.random.randn(1
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#plot_alignments(trellis, segments, word_segments, waveform, sample_rate)
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st.pyplot(plot_alignments(trellis, segments, word_segments, waveform=np.random.randn(1024), sample_rate=16000))
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# Part N:
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sample_rate = 16000
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def display_segment(i):
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word = word_segments[i]
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x0 = int(word.start * sample_rate)
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x1 = int(word.end * sample_rate)
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print(f"{word.label} ({word.score:.2f}): {x0 / sample_rate:.3f} - {x1 / sample_rate:.3f} sec")
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segment = waveform[x0:x1]
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# Ensure the segment is a 1D array
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segment = segment.squeeze()
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return segment.numpy() # Return the audio data as a numpy array
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# Display the full audio file
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st.audio(waveform.numpy(), format="audio/wav", sample_rate=sample_rate)
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# Display the audio for each segment
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for i in range(len(word_segments)):
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segment_audio = display_segment(i)
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# Ensure the segment_audio is a 1D array before passing to st.audio
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if segment_audio.ndim == 2:
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segment_audio = segment_audio.squeeze()
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st.audio(segment_audio.astype('float32'), format="audio/wav", sample_rate=sample_rate)
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st.write(display_segment(3))
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# Part O: Audio generation for each segment
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st.write('Abby Cadabby Transcript:')
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# Display the audio in the Streamlit app
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st.audio(SPEECH_FILE, format="audio/wav")
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#plot_alignments(trellis, segments, word_segments, waveform, sample_rate)
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st.pyplot(plot_alignments(trellis, segments, word_segments, waveform=np.random.randn(1024), sample_rate=16000))
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# Part N: Audio generation
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st.write('Abby Cadabby Transcript:')
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# Display the audio in the Streamlit app
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st.audio(SPEECH_FILE, format="audio/wav")
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