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--- |
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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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- Tacotron2 |
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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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metrics: |
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- mos |
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pipeline_tag: text-to-speech |
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library_name: speechbrain |
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--- |
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<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> |
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<br/><br/> |
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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 input a short text 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) - Running Inference |
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To run model inference pull the interface directory as shown in the cell below |
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Note: Run on T4-GPU for faster inference |
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``` |
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!pip install --upgrade --no-cache-dir gdown |
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!gdown 1oy8Y5zwkLel7diA63GNCD-6cfoBV4tq7 |
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!unzip inference.zip |
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``` |
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```python |
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%%capture |
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!pip install speechbrain |
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%cd inference |
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``` |
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```python |
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import torchaudio |
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from TTSModel import TTSModel |
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from IPython.display import Audio |
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from speechbrain.inference.vocoders import HIFIGAN |
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texts = ["This is a sample text for synthesis."] |
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model_source_path = "/content/inference" |
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# Intialize TTS (Transformer) and Vocoder (HiFIGAN) |
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my_tts_model = TTSModel.from_hparams(source=model_source_path) |
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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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print("Saved the audio file!") |
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``` |
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If you want to generate multiple sentences in one-shot, pass the sentences as items in a list. |
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### Inference on GPU |
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. |
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Note: For Training the model please visit this [TTS_Training_Inference](https://colab.research.google.com/drive/1VYu4kXdgpv7f742QGquA1G4ipD2Kg0kT?usp=sharing) notebook |
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### Limitations |
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The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. |
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# **About SpeechBrain** |
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- Website: https://speechbrain.github.io/ |
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- Code: https://github.com/speechbrain/speechbrain/ |
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- HuggingFace: https://huggingface.co/speechbrain/ |
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# **Citing SpeechBrain** |
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Please, cite SpeechBrain if you use it for your research or business. |
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```bibtex |
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@misc{speechbrain, |
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title={{SpeechBrain}: A General-Purpose Speech Toolkit}, |
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author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, |
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year={2021}, |
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eprint={2106.04624}, |
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archivePrefix={arXiv}, |
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primaryClass={eess.AS}, |
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note={arXiv:2106.04624} |
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} |
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``` |
