Update README.md

README.md

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 ---
-license: cc-by-4.0
+language: "en"
+inference: false
+tags:
+- Vocoder
+- HiFIGAN
+- text-to-speech
+- TTS
+- speech-synthesis
+- speechbrain
+license: "apache-2.0"
+datasets:
+- LJSpeech
 ---
+
+# Vocoder with HiFIGAN trained on LJSpeech
+
+This repository provides all the necessary tools for using a [HiFIGAN](https://arxiv.org/abs/2010.05646) vocoder trained with [LJSpeech](https://keithito.com/LJ-Speech-Dataset/). 
+
+The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram.
+
+The sampling frequency is 22050 Hz.
+
+**NOTES**
+- This vocoder model is trained on a single speaker. Although it has some ability to generalize to different speakers, for better results, we recommend using a multi-speaker vocoder like [this model trained on LibriTTS at 16,000 Hz](https://huggingface.co/speechbrain/tts-hifigan-libritts-16kHz) or [this one trained on LibriTTS at 22,050 Hz](https://huggingface.co/speechbrain/tts-hifigan-libritts-22050Hz).
+- If you specifically require a vocoder with a 16,000 Hz sampling rate, please follow the provided link above for a suitable option.
+
+## Install SpeechBrain
+
+```bash
+pip install speechbrain
+```
+
+
+Please notice that we encourage you to read our tutorials and learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+### Using the Vocoder
+
+- *Basic Usage:*
+```python
+import torch
+from speechbrain.inference.vocoders import HIFIGAN
+hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="pretrained_models/tts-hifigan-ljspeech")
+mel_specs = torch.rand(2, 80,298)
+waveforms = hifi_gan.decode_batch(mel_specs)
+```
+
+- *Convert a Spectrogram into a Waveform:*
+
+```python
+import torchaudio
+from speechbrain.inference.vocoders import HIFIGAN
+from speechbrain.lobes.models.FastSpeech2 import mel_spectogram
+
+# Load a pretrained HIFIGAN Vocoder
+hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="pretrained_models/tts-hifigan-ljspeech")
+
+# Load an audio file (an example file can be found in this repository)
+# Ensure that the audio signal is sampled at 22050 Hz; refer to the provided link for a 16 kHz Vocoder.
+signal, rate = torchaudio.load('speechbrain/tts-hifigan-ljspeech/example.wav')
+
+# Compute the mel spectrogram.
+# IMPORTANT: Use these specific parameters to match the Vocoder's training settings for optimal results.
+spectrogram, _ = mel_spectogram(
+    audio=signal.squeeze(),
+    sample_rate=22050,
+    hop_length=256,
+    win_length=None,
+    n_mels=80,
+    n_fft=1024,
+    f_min=0.0,
+    f_max=8000.0,
+    power=1,
+    normalized=False,
+    min_max_energy_norm=True,
+    norm="slaney",
+    mel_scale="slaney",
+    compression=True
+)
+
+# Convert the spectrogram to waveform
+waveforms = hifi_gan.decode_batch(spectrogram)
+
+# Save the reconstructed audio as a waveform
+torchaudio.save('waveform_reconstructed.wav', waveforms.squeeze(1), 22050)
+
+# If everything is set up correctly, the original and reconstructed audio should be nearly indistinguishable.
+# Keep in mind that this Vocoder is trained for a single speaker; for multi-speaker Vocoder options, refer to the provided links.
+
+```
+
+### Using the Vocoder with the TTS
+```python
+import torchaudio
+from speechbrain.inference.TTS import Tacotron2
+from speechbrain.inference.vocoders import HIFIGAN
+
+# Intialize TTS (tacotron2) and Vocoder (HiFIGAN)
+tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="pretrained_models/tts-tacotron2-ljspeech")
+hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="pretrained_model/tts-hifigan-ljspeech")
+
+# 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)
+```
+
+### 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/vocoder/hifi_gan/
+python train.py hparams/train.yaml --data_folder /path/to/LJspeech
+```
+You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/19sLwV7nAsnUuLkoTu5vafURA9Fo2WZgG?usp=sharing).