Update README.md

README.md

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-Sunbird Luganda Text-To-Speech translation model trained data crowdsourced on CommonVoice
+# Sunbird AI Text-to-Speech (TTS) model trained on Luganda text 
+
+## Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech
+
+This repository provides all the necessary tools for Text-to-Speech (TTS)  with SpeechBrain using a [Tacotron2](https://arxiv.org/abs/1712.05884) pretrained on [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
+
+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.
+
+
+## Install SpeechBrain
+
+```
+pip install speechbrain
+```
+
+Please notice that we encourage you to read our tutorials and learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+### Perform Text-to-Speech (TTS)
+
+```
+import torchaudio
+from speechbrain.pretrained import Tacotron2
+from speechbrain.pretrained import HIFIGAN
+
+# Intialize TTS (tacotron2) and Vocoder (HiFIGAN)
+tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="tmpdir_tts")
+hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder")
+
+# Running the TTS
+mel_output, mel_length, alignment = tacotron2.encode_text("Mary had a little lamb")
+
+# Running Vocoder (spectrogram-to-waveform)
+waveforms = hifi_gan.decode_batch(mel_output)
+
+# Save the waverform
+torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 22050)
+```
+
+If you want to generate multiple sentences in one-shot, you can do in this way:
+
+```
+from speechbrain.pretrained import Tacotron2
+tacotron2 = Tacotron2.from_hparams(source="speechbrain/TTS_Tacotron2", savedir="tmpdir")
+items = [
+       "A quick brown fox jumped over the lazy dog",
+       "How much wood would a woodchuck chuck?",
+       "Never odd or even"
+     ]
+mel_outputs, mel_lengths, alignments = tacotron2.encode_batch(items)
+
+```
+
+### Inference on GPU
+To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
+
+### Training
+The model was trained with SpeechBrain.
+To train it from scratch follow these steps:
+1. Clone SpeechBrain:
+```bash
+git clone https://github.com/speechbrain/speechbrain/
+```
+2. Install it:
+```bash
+cd speechbrain
+pip install -r requirements.txt
+pip install -e .
+```
+3. Run Training:
+```bash
+cd recipes/LJSpeech/TTS/tacotron2/
+python train.py --device=cuda:0 --max_grad_norm=1.0 --data_folder=/your_folder/LJSpeech-1.1 hparams/train.yaml
+```