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--- |
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library_name: speechbrain |
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pipeline_tag: text-to-speech |
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language: "en" |
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tags: |
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- text-to-speech |
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- TTS |
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- speech-synthesis |
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- speechbrain |
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license: "apache-2.0" |
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datasets: |
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- LJSpeech |
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--- |
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# Text-to-Speech (TTS) with Transformer trained on LJSpeech |
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This repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a [Transformer](https://arxiv.org/pdf/1809.08895.pdf) pretrained on [LJSpeech](https://keithito.com/LJ-Speech-Dataset/). |
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The pre-trained model takes in text input and produces a spectrogram in output. One can get the final waveform by applying a vocoder (e.g., HiFIGAN) on top of the generated spectrogram. |
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### Perform Text-to-Speech (TTS) |
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```python |
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import torchaudio |
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from speechbrain.inference.vocoders import HIFIGAN |
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texts = ["This is the example text"] |
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#initializing my model |
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my_tts_model = TextToSpeech.from_hparams(source="/content/") |
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#initializing vocoder(Hifigan) model |
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hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder") |
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# Running the TTS |
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mel_output = my_tts_model.encode_text(texts) |
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# Running Vocoder (spectrogram-to-waveform) |
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waveforms = hifi_gan.decode_batch(mel_output) |
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# Save the waverform |
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torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 22050) |
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``` |
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