Update README.md

README.md

@@ -21,6 +21,9 @@ The pre-trained model takes in input a spectrogram and produces a waveform in ou
 
 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
 
@@ -34,6 +37,7 @@ Please notice that we encourage you to read our tutorials and learn more about
 
 ### Using the Vocoder
 
+- *Basic Usage:*
 ```python
 import torch
 from speechbrain.pretrained import HIFIGAN
@@ -41,6 +45,51 @@ hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", saved
 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.pretrained 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="tmpdir_vocoder")
+
+# 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