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add link to fine-tuning example notebook
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app.py
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@@ -68,6 +68,8 @@ SpeechT5 can be fine-tuned for different speech tasks. This space demonstrates t
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See also the <a href="https://huggingface.co/spaces/Matthijs/speecht5-asr-demo">speech recognition (ASR) demo</a>
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and the <a href="https://huggingface.co/spaces/Matthijs/speecht5-vc-demo">voice conversion demo</a>.
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<b>How to use:</b> Enter some English text and choose a speaker. The output is a mel spectrogram, which is converted to a mono 16 kHz waveform by the
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HiFi-GAN vocoder. Because the model always applies random dropout, each attempt will give slightly different results.
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The <em>Surprise Me!</em> option creates a completely randomized speaker.
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See also the <a href="https://huggingface.co/spaces/Matthijs/speecht5-asr-demo">speech recognition (ASR) demo</a>
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and the <a href="https://huggingface.co/spaces/Matthijs/speecht5-vc-demo">voice conversion demo</a>.
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Refer to <a href="https://colab.research.google.com/drive/1i7I5pzBcU3WDFarDnzweIj4-sVVoIUFJ">this Colab notebook</a> to learn how to fine-tune the SpeechT5 TTS model on your own dataset or language.
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<b>How to use:</b> Enter some English text and choose a speaker. The output is a mel spectrogram, which is converted to a mono 16 kHz waveform by the
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HiFi-GAN vocoder. Because the model always applies random dropout, each attempt will give slightly different results.
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The <em>Surprise Me!</em> option creates a completely randomized speaker.
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