Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-language: "en"
+language: "lg"
 tags:
 - text-to-speech
 - TTS
@@ -8,7 +8,7 @@ tags:
 - speechbrain
 license: "apache-2.0"
 datasets:
-- LJSpeech
+- SALT-TTS
 metrics:
 - mos
 ---
@@ -17,7 +17,7 @@ metrics:
 
 ### Text-to-Speech (TTS) with Tacotron2 trained on Professional Studio Recordings
 
-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/).
+This repository provides all the necessary tools for Text-to-Speech (TTS)  with SpeechBrain.
 
 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.
 
@@ -28,8 +28,6 @@ The pre-trained model takes in input a short text and produces a spectrogram in
 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)
 
@@ -39,7 +37,7 @@ 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")
+tacotron2 = Tacotron2.from_hparams(source="/Sunbird/sunbird-lug-tts", savedir="tmpdir_tts")
 hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder")
 
 # Running the TTS